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What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on August 2, 2022 by Bas Swaen and Tegan George. Revised on March 18, 2024.

A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualize your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

The expected cause, “hours of study,” is the independent variable (the predictor, or explanatory variable)
The expected effect, “exam score,” is the dependent variable (the response, or outcome variable).

Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (“hours of study”).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualizing your expected cause-and-effect relationship.

We demonstrate this using basic design components of boxes and arrows. Here, each variable appears in a box. To indicate a causal relationship, each arrow should start from the independent variable (the cause) and point to the dependent variable (the effect).

Sample-conceptual-framework-using-an-independent-variable-and-a-dependent-variable

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the “effect” component of the cause-and-effect relationship.

Let’s add the moderator “IQ.” Here, a student’s IQ level can change the effect that the variable “hours of study” has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

Sample-conceptual-framework-with-a-moderator-variable

Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.

But the graph looks different when we add our “IQ” moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the “IQ” moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.

Moderator vs. mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

Lastly, control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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How to Use a Conceptual Framework for Better Research

A conceptual framework in research is not just a tool but a vital roadmap that guides the entire research process. It integrates various theories, assumptions, and beliefs to provide a structured approach to research. By defining a conceptual framework, researchers can focus their inquiries and clarify their hypotheses, leading to more effective and meaningful research outcomes.

What is a Conceptual Framework?

A conceptual framework is essentially an analytical tool that combines concepts and sets them within an appropriate theoretical structure. It serves as a lens through which researchers view the complexities of the real world. The importance of a conceptual framework lies in its ability to serve as a guide, helping researchers to not only visualize but also systematically approach their study.

Key Components and to be Analyzed During Research

Theories: These are the underlying principles that guide the hypotheses and assumptions of the research.
Assumptions: These are the accepted truths that are not tested within the scope of the research but are essential for framing the study.
Beliefs: These often reflect the subjective viewpoints that may influence the interpretation of data.
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Together, these components help to define the conceptual framework that directs the research towards its ultimate goal. This structured approach not only improves clarity but also enhances the validity and reliability of the research outcomes. By using a conceptual framework, researchers can avoid common pitfalls and focus on essential variables and relationships.

For practical examples and to see how different frameworks can be applied in various research scenarios, you can Explore Conceptual Framework Examples .

Different Types of Conceptual Frameworks Used in Research

Understanding the various types of conceptual frameworks is crucial for researchers aiming to align their studies with the most effective structure. Conceptual frameworks in research vary primarily between theoretical and operational frameworks, each serving distinct purposes and suiting different research methodologies.

Theoretical vs Operational Frameworks

Theoretical frameworks are built upon existing theories and literature, providing a broad and abstract understanding of the research topic. They help in forming the basis of the study by linking the research to already established scholarly works. On the other hand, operational frameworks are more practical, focusing on how the study’s theories will be tested through specific procedures and variables.

Theoretical frameworks are ideal for exploratory studies and can help in understanding complex phenomena.
Operational frameworks suit studies requiring precise measurement and data analysis.

Choosing the Right Framework

Selecting the appropriate conceptual framework is pivotal for the success of a research project. It involves matching the research questions with the framework that best addresses the methodological needs of the study. For instance, a theoretical framework might be chosen for studies that aim to generate new theories, while an operational framework would be better suited for testing specific hypotheses.

Benefits of choosing the right framework include enhanced clarity, better alignment with research goals, and improved validity of research outcomes. Tools like Table Chart Maker can be instrumental in visually comparing the strengths and weaknesses of different frameworks, aiding in this crucial decision-making process.

Real-World Examples of Conceptual Frameworks in Research

Understanding the practical application of conceptual frameworks in research can significantly enhance the clarity and effectiveness of your studies. Here, we explore several real-world case studies that demonstrate the pivotal role of conceptual frameworks in achieving robust research conclusions.

Healthcare Research: In a study examining the impact of lifestyle choices on chronic diseases, researchers used a conceptual framework to link dietary habits, exercise, and genetic predispositions. This framework helped in identifying key variables and their interrelations, leading to more targeted interventions.
Educational Development: Educational theorists often employ conceptual frameworks to explore the dynamics between teaching methods and student learning outcomes. One notable study mapped out the influences of digital tools on learning engagement, providing insights that shaped educational policies.
Environmental Policy: Conceptual frameworks have been crucial in environmental research, particularly in studies on climate change adaptation. By framing the relationships between human activity, ecological changes, and policy responses, researchers have been able to propose more effective sustainability strategies.

Adapting conceptual frameworks based on evolving research data is also critical. As new information becomes available, it’s essential to revisit and adjust the framework to maintain its relevance and accuracy, ensuring that the research remains aligned with real-world conditions.

For those looking to visualize and better comprehend their research frameworks, Graphic Organizers for Conceptual Frameworks can be an invaluable tool. These organizers help in structuring and presenting research findings clearly, enhancing both the process and the presentation of your research.

Step-by-Step Guide to Creating Your Own Conceptual Framework

Creating a conceptual framework is a critical step in structuring your research to ensure clarity and focus. This guide will walk you through the process of building a robust framework, from identifying key concepts to refining your approach as your research evolves.

Building Blocks of a Conceptual Framework

Identify and Define Main Concepts and Variables: Start by clearly identifying the main concepts, variables, and their relationships that will form the basis of your research. This could include defining key terms and establishing the scope of your study.
Develop a Hypothesis or Primary Research Question: Formulate a central hypothesis or question that guides the direction of your research. This will serve as the foundation upon which your conceptual framework is built.
Link Theories and Concepts Logically: Connect your identified concepts and variables with existing theories to create a coherent structure. This logical linking helps in forming a strong theoretical base for your research.

Visualizing and Refining Your Framework

Using visual tools can significantly enhance the clarity and effectiveness of your conceptual framework. Decision Tree Templates for Conceptual Frameworks can be particularly useful in mapping out the relationships between variables and hypotheses.

Map Your Framework: Utilize tools like Creately’s visual canvas to diagram your framework. This visual representation helps in identifying gaps or overlaps in your framework and provides a clear overview of your research structure.

A mind map is a useful graphic organizer for writing - Graphic Organizers for Writing

Analyze and Refine: As your research progresses, continuously evaluate and refine your framework. Adjustments may be necessary as new data comes to light or as initial assumptions are challenged.

By following these steps, you can ensure that your conceptual framework is not only well-defined but also adaptable to the changing dynamics of your research.

Practical Tips for Utilizing Conceptual Frameworks in Research

Effectively utilizing a conceptual framework in research not only streamlines the process but also enhances the clarity and coherence of your findings. Here are some practical tips to maximize the use of conceptual frameworks in your research endeavors.

Setting Clear Research Goals: Begin by defining precise objectives that are aligned with your research questions. This clarity will guide your entire research process, ensuring that every step you take is purposeful and directly contributes to your overall study aims. \
Maintaining Focus and Coherence: Throughout the research, consistently refer back to your conceptual framework to maintain focus. This will help in keeping your research aligned with the initial goals and prevent deviations that could dilute the effectiveness of your findings.
Data Analysis and Interpretation: Use your conceptual framework as a lens through which to view and interpret data. This approach ensures that the data analysis is not only systematic but also meaningful in the context of your research objectives. For more insights, explore Research Data Analysis Methods .
Presenting Research Findings: When it comes time to present your findings, structure your presentation around the conceptual framework . This will help your audience understand the logical flow of your research and how each part contributes to the whole.
Avoiding Common Pitfalls: Be vigilant about common errors such as overcomplicating the framework or misaligning the research methods with the framework’s structure. Keeping it simple and aligned ensures that the framework effectively supports your research.

By adhering to these tips and utilizing tools like 7 Essential Visual Tools for Social Work Assessment , researchers can ensure that their conceptual frameworks are not only robust but also practically applicable in their studies.

How Creately Enhances the Creation and Use of Conceptual Frameworks

Creating a robust conceptual framework is pivotal for effective research, and Creately’s suite of visual tools offers unparalleled support in this endeavor. By leveraging Creately’s features, researchers can visualize, organize, and analyze their research frameworks more efficiently.

Visual Mapping of Research Plans: Creately’s infinite visual canvas allows researchers to map out their entire research plan visually. This helps in understanding the complex relationships between different research variables and theories, enhancing the clarity and effectiveness of the research process.
Brainstorming with Mind Maps: Using Mind Mapping Software , researchers can generate and organize ideas dynamically. Creately’s intelligent formatting helps in brainstorming sessions, making it easier to explore multiple topics or delve deeply into specific concepts.
Centralized Data Management: Creately enables the importation of data from multiple sources, which can be integrated into the visual research framework. This centralization aids in maintaining a cohesive and comprehensive overview of all research elements, ensuring that no critical information is overlooked.
Communication and Collaboration: The platform supports real-time collaboration, allowing teams to work together seamlessly, regardless of their physical location. This feature is crucial for research teams spread across different geographies, facilitating effective communication and iterative feedback throughout the research process.

Moreover, the ability t Explore Conceptual Framework Examples directly within Creately inspires researchers by providing practical templates and examples that can be customized to suit specific research needs. This not only saves time but also enhances the quality of the conceptual framework developed.

In conclusion, Creately’s tools for creating and managing conceptual frameworks are indispensable for researchers aiming to achieve clear, structured, and impactful research outcomes.

Join over thousands of organizations that use Creately to brainstorm, plan, analyze, and execute their projects successfully.

What is a Conceptual Framework?

Updated on August 28, 2023

a researcher putting together their conceptual framework for a manuscript

What are frameworks in research?

Both theoretical and conceptual frameworks have a significant role in research. Frameworks are essential to bridge the gaps in research. They aid in clearly setting the goals, priorities, relationship between variables. Frameworks in research particularly help in chalking clear process details.

Theoretical frameworks largely work at the time when a theoretical roadmap has been laid about a certain topic and the research being undertaken by the researcher, carefully analyzes it, and works on similar lines to attain successful results.

It varies from a conceptual framework in terms of the preliminary work required to construct it. Though a conceptual framework is part of the theoretical framework in a larger sense, yet there are variations between them.

The following sections delve deeper into the characteristics of conceptual frameworks. This article will provide insight into constructing a concise, complete, and research-friendly conceptual framework for your project.

Definition of a conceptual framework

True research begins with setting empirical goals. Goals aid in presenting successful answers to the research questions at hand. It delineates a process wherein different aspects of the research are reflected upon, and coherence is established among them.

A conceptual framework is an underrated methodological approach that should be paid attention to before embarking on a research journey in any field, be it science, finance, history, psychology, etc.

A conceptual framework sets forth the standards to define a research question and find appropriate, meaningful answers for the same. It connects the theories, assumptions, beliefs, and concepts behind your research and presents them in a pictorial, graphical, or narrative format. Your conceptual framework establishes a link between the dependent and independent variables, factors, and other ideologies affecting the structure of your research.

A critical facet a conceptual framework unveils is the relationship the researchers have with their research. It closely highlights the factors that play an instrumental role in decision-making, variable selection, data collection, assessment of results, and formulation of new theories.

Consequently, if you, the researcher, are at the forefront of your research battlefield, your conceptual framework is the most powerful arsenal in your pocket.

What should be included in a conceptual framework?

A conceptual framework includes the key process parameters, defining variables, and cause-and-effect relationships. To add to this, the primary focus while developing a conceptual framework should remain on the quality of questions being raised and addressed through the framework. This will not only ease the process of initiation, but also enable you to draw meaningful conclusions from the same.

A practical and advantageous approach involves selecting models and analyzing literature that is unconventional and not directly related to the topic. This helps the researcher design an illustrative framework that is multidisciplinary and simultaneously looks at a diverse range of phenomena. It also emboldens the roots of exploratory research.

the components of a conceptual framework

Fig. 1: Components of a conceptual framework

How to make a conceptual framework

The successful design of a conceptual framework includes:

Selecting the appropriate research questions
Defining the process variables (dependent, independent, and others)
Determining the cause-and-effect relationships

This analytical tool begins with defining the most suitable set of questions that the research wishes to answer upon its conclusion. Following this, the different variety of variables is categorized. Lastly, the collected data is subjected to rigorous data analysis. Final results are compiled to establish links between the variables.

The variables drawn inside frames impact the overall quality of the research. If the framework involves arrows, it suggests correlational linkages among the variables. Lines, on the other hand, suggest that no significant correlation exists among them. Henceforth, the utilization of lines and arrows should be done taking into cognizance the meaning they both imply.

Example of a conceptual framework

To provide an idea about a conceptual framework, let’s examine the example of drug development research.

Say a new drug moiety A has to be launched in the market. For that, the baseline research begins with selecting the appropriate drug molecule. This is important because it:

Provides the data for molecular docking studies to identify suitable target proteins
Performs in vitro (a process taking place outside a living organism) and in vivo (a process taking place inside a living organism) analyzes

This assists in the screening of the molecules and a final selection leading to the most suitable target molecule. In this case, the choice of the drug molecule is an independent variable whereas, all the others, targets from molecular docking studies, and results from in vitro and in vivo analyses are dependent variables.

The outcomes revealed by the studies might be coherent or incoherent with the literature. In any case, an accurately designed conceptual framework will efficiently establish the cause-and-effect relationship and explain both perspectives satisfactorily.

If A has been chosen to be launched in the market, the conceptual framework will point towards the factors that have led to its selection. If A does not make it to the market, the key elements which did not work in its favor can be pinpointed by an accurate analysis of the conceptual framework.

Fig. 2: Concise example of a conceptual framework

Important takeaways

While conceptual frameworks are a great way of designing the research protocol, they might consist of some unforeseen loopholes. A review of the literature can sometimes provide a false impression of the collection of work done worldwide while in actuality, there might be research that is being undertaken on the same topic but is still under publication or review. Strong conceptual frameworks, therefore, are designed when all these aspects are taken into consideration and the researchers indulge in discussions with others working on similar grounds of research.

Conceptual frameworks may also sometimes lead to collecting and reviewing data that is not so relevant to the current research topic. The researchers must always be on the lookout for studies that are highly relevant to their topic of work and will be of impact if taken into consideration.

Another common practice associated with conceptual frameworks is their classification as merely descriptive qualitative tools and not actually a concrete build-up of ideas and critically analyzed literature and data which it is, in reality. Ideal conceptual frameworks always bring out their own set of new ideas after analysis of literature rather than simply depending on facts being already reported by other research groups.

So, the next time you set out to construct your conceptual framework or improvise on your previous one, be wary that concepts for your research are ideas that need to be worked upon. They are not simply a collection of literature from the previous research.

Final thoughts

Research is witnessing a boom in the methodical approaches being applied to it nowadays. In contrast to conventional research, researchers today are always looking for better techniques and methods to improve the quality of their research.

We strongly believe in the ideals of research that are not merely academic, but all-inclusive. We strongly encourage all our readers and researchers to do work that impacts society. Designing strong conceptual frameworks is an integral part of the process. It gives headway for systematic, empirical, and fruitful research.

Vridhi Sachdeva, MPharm

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

Introduction and overview
What is qualitative research?
What is qualitative data?
Examples of qualitative data
Qualitative vs. quantitative research
Mixed methods
Qualitative research preparation
Theoretical perspective
Theoretical framework
Literature reviews
Research question
Conceptual framework
Introduction

Revisiting theoretical frameworks

Revisiting conceptual frameworks, differences between conceptual and theoretical frameworks, examples of theoretical and conceptual frameworks, developing frameworks for your study.

Data collection
Qualitative research methods
Focus groups
Observational research
Case studies
Ethnographical research
Ethical considerations
Confidentiality and privacy
Power dynamics
Reflexivity

Conceptual vs. theoretical framework

Theoretical and conceptual frameworks are both essential components of research, guiding and structuring the research. Although they are closely related, the conceptual and theoretical framework in any research project serve distinct purposes and have different characteristics. In this section, we provide an overview of the key differences between theoretical and conceptual frameworks.

Theoretical and conceptual frameworks are foundational components of any research study. They each play a crucial role in guiding and structuring the research, from the formation of research questions to the interpretation of results .

While both the theoretical and conceptual framework provides a structure for a study, they serve different functions and can impact the research in distinct ways depending on how they are combined. These differences might seem subtle, but they can significantly impact your research design and outcomes, which is why it is important to think through each one of them.

The theoretical framework describes the broader lens through which the researcher views the topic and guides their overall understanding and approach. It connects the theoretical perspective to the data collection and data analysis strategy and offers a structure for organizing and interpreting the collected data.

On the other hand, the conceptual framework describes in detail and connects specific concepts and variables to illustrate potential relationships between them. It serves as a guide for assessing which aspects of the data are relevant and specifying how the research question is being answered. While the theoretical framework outlines how more abstract-level theories shape the study, the conceptual framework operationalizes the empirical observations that can be connected to theory and broader understanding.

Understanding these differences is crucial when designing and conducting your research study. In this chapter, we will look deeper at the distinctions between these types of frameworks, and how they interplay in qualitative research . We aim to provide you with a solid understanding of both, allowing you to effectively utilize them in your own research.

Theoretical frameworks play a central role in research, serving as the bedrock of any investigation. This section offers a refresher on the essential elements and functions of theoretical frameworks in research.

A theoretical framework refers to existing theory, concepts, and definitions that you use to collect relevant data and offer meaningful empirical findings. Providing an overall orientation or lens, it guides your understanding of the research problem and directs your approach to data collection and analysis .

Your chosen theoretical framework directly influences your research questions and methodological choices . It contains specific theories or sets of assumptions drawn from relevant disciplines—such as sociology, psychology, or economics—that you apply to understand your research topic. These existing models and concepts are tools to help you organize and make sense of your data.

The theoretical framework also plays a key role in crafting your research questions and objectives. By determining the theories that are relevant to your research, the theoretical framework shapes the nature and direction of your study. It's essential to note, however, that the theoretical framework's role in qualitative research is not to predict outcomes. Instead, it offers a broader structure to understand and interpret your data, enabling you to situate your findings within the broader academic discourse in a way that makes your research findings meaningful to you and your research audience.

Conceptual frameworks , though related to theoretical frameworks , serve distinct functions within research. This section reexamines the characteristics and functions of conceptual frameworks to provide a better understanding of their roles in qualitative research .

A conceptual framework, in essence, is a system of concepts, assumptions, and beliefs that supports and informs your research. It outlines the specific variables or concepts you'll examine in your study and proposes relationships between them. It's more detailed and specific than a theoretical framework, acting as a contextualized guide for the collection and interpretation of empirical data.

The main role of a conceptual framework is to illustrate the presumed relationships between the variables or concepts you're investigating. These variables or concepts, which you derive from your theoretical framework, are integral to your research questions , objectives, and hypotheses . The conceptual framework shows how you theorize these concepts are related, providing a visual or narrative model of your research.

A study's own conceptual framework plays a vital role in guiding the data collection process and the subsequent analysis . The conceptual framework specifies which data you need to collect and provides a structure for interpreting and making sense of the collected data. For instance, if your conceptual framework identifies a particular variable as impacting another, your data collection and analysis will be geared towards investigating this relationship.

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Though interconnected, theoretical and conceptual frameworks have distinct roles in research and contribute differently to the research. This section will contrast the two in terms of scope, purpose, their role in the research process, and their relationship to the data analysis strategy and research question .

Scope and purpose of theoretical and conceptual frameworks

Theoretical and conceptual frameworks differ fundamentally in their scope. Theoretical frameworks provide a broad and general view of the research problem, rooted in established theories. They explain phenomena by applying a particular theoretical lens. Conceptual frameworks, on the other hand, offer a more focused view of the specific research problem. They explicitly outline the concrete concepts and variables involved in the study and the relationships between them.

While both frameworks guide the research process, they do so in different ways. Theoretical frameworks guide the overall approach to understanding the research problem by indicating the broader conversation the researcher is contributing to and shaping the research questions.

Conceptual frameworks provide a map for the study, guiding the data collection and interpretation process, including what variables or concepts to explore and how to analyze them.

Study design and data analysis

The two types of frameworks relate differently to the research question and design. The theoretical framework often inspires the research question based on previous theories' predictions or understanding about the phenomena under investigation. A conceptual framework then emerges from the research question, providing a contextualized structure for what exactly the research will explore.

Theoretical and conceptual frameworks also play distinct roles in data analysis. Theoretical frameworks provide the lens for interpreting the data, informing what kinds of themes and patterns might be relevant. Conceptual frameworks, however, present the variables concepts and variables and the relationships among them that will be analyzed. Conceptual frameworks may illustrate concepts and relationships based on previous theory, but they can also include novel concepts or relationships that stem from the particular context being studied.

Finally, the two types of frameworks relate differently to the research question and design. The theoretical framework basically differs from the conceptual framework in that it often inspires the research question based on the theories' predictions about the phenomena under investigation. A conceptual framework, on the other hand, emerges from the research question, providing a structure for investigating it.

Using case studies , we can effectively demonstrate the differences between theoretical and conceptual frameworks. Let’s take a look at some real-world examples that highlight the unique role and function of each framework within a research context.

Consider a study exploring the impact of classroom environments on student learning outcomes. The theoretical framework might be grounded in Piaget's theory of cognitive development, which offers a broad lens for understanding how students learn and process information.

Within this theoretical framework, the researcher formulates the conceptual framework. The conceptual framework identifies specific variables to study such as classroom layout, teacher-student ratio, availability of learning materials, and student performance as the dependent variable. It then outlines the expected relationships between these variables, such as proposing that a lower teacher-student ratio and well-equipped classrooms positively impact student performance.

Another study might aim to understand the factors influencing the job satisfaction of employees in a corporate setting. The theoretical framework could be based on Maslow's hierarchy of needs, interpreting job satisfaction in terms of fulfilling employees' physiological, safety, social, esteem, and self-actualization needs.

From this theoretical perspective, the researcher constructs the conceptual framework, identifying specific variables such as salary (physiological needs), job security (safety needs), teamwork (social needs), recognition (esteem needs), and career development opportunities (self-actualization needs). The conceptual framework proposes relationships among these variables and job satisfaction, such as higher salaries and more recognition being related to higher job satisfaction.

After understanding the unique roles and functions of these types of frameworks, you might ask: How do I develop them for my study? It's essential to remember that it's not a question of choosing one over the other, as both frameworks can and often do coexist within the same research project.

The choice of a theoretical and a conceptual framework often depends on the nature of your research question . If your research question is more exploratory and requires a broad understanding of the problem, a theoretical framework can provide a useful lens for interpretation. However, your conceptual framework may end up looking rather different to previous theory as you collect data and discover new concepts or relationships.

Consider the nature of your research problem as well. If you are studying a well-researched problem and there are established theories about it, using a theoretical framework to interpret your findings in light of these theories might be beneficial. But if your study explores a novel problem or aims to understand specific processes or relationships, developing a conceptual framework that maps these specific elements could prove more effective.

Your research methodology could also inform your choice. If your study is more interpretive and aims to understand people's experiences and perceptions, a theoretical framework can outline broader concepts that are relevant to approaching your study. Your conceptual framework can then shed light on the specific concepts that emerged in your data. By carefully thinking through your theoretical and conceptual frameworks, you can effectively utilize both types of frameworks in your research, ensuring a solid foundation for your study.

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Theoretical vs Conceptual Framework

What they are & how they’re different (with examples)

By: Derek Jansen (MBA) | Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, sooner or later you’re bound to run into the terms theoretical framework and conceptual framework . These are closely related but distinctly different things (despite some people using them interchangeably) and it’s important to understand what each means. In this post, we’ll unpack both theoretical and conceptual frameworks in plain language along with practical examples , so that you can approach your research with confidence.

Overview: Theoretical vs Conceptual

What is a theoretical framework, example of a theoretical framework, what is a conceptual framework, example of a conceptual framework.

Theoretical vs conceptual: which one should I use?

A theoretical framework (also sometimes referred to as a foundation of theory) is essentially a set of concepts, definitions, and propositions that together form a structured, comprehensive view of a specific phenomenon.

In other words, a theoretical framework is a collection of existing theories, models and frameworks that provides a foundation of core knowledge – a “lay of the land”, so to speak, from which you can build a research study. For this reason, it’s usually presented fairly early within the literature review section of a dissertation, thesis or research paper .

Let’s look at an example to make the theoretical framework a little more tangible.

If your research aims involve understanding what factors contributed toward people trusting investment brokers, you’d need to first lay down some theory so that it’s crystal clear what exactly you mean by this. For example, you would need to define what you mean by “trust”, as there are many potential definitions of this concept. The same would be true for any other constructs or variables of interest.

You’d also need to identify what existing theories have to say in relation to your research aim. In this case, you could discuss some of the key literature in relation to organisational trust. A quick search on Google Scholar using some well-considered keywords generally provides a good starting point.

Typically, you’ll present your theoretical framework in written form , although sometimes it will make sense to utilise some visuals to show how different theories relate to each other. Your theoretical framework may revolve around just one major theory , or it could comprise a collection of different interrelated theories and models. In some cases, there will be a lot to cover and in some cases, not. Regardless of size, the theoretical framework is a critical ingredient in any study.

Simply put, the theoretical framework is the core foundation of theory that you’ll build your research upon. As we’ve mentioned many times on the blog, good research is developed by standing on the shoulders of giants . It’s extremely unlikely that your research topic will be completely novel and that there’ll be absolutely no existing theory that relates to it. If that’s the case, the most likely explanation is that you just haven’t reviewed enough literature yet! So, make sure that you take the time to review and digest the seminal sources.

Need a helping hand?

A conceptual framework is typically a visual representation (although it can also be written out) of the expected relationships and connections between various concepts, constructs or variables. In other words, a conceptual framework visualises how the researcher views and organises the various concepts and variables within their study. This is typically based on aspects drawn from the theoretical framework, so there is a relationship between the two.

Quite commonly, conceptual frameworks are used to visualise the potential causal relationships and pathways that the researcher expects to find, based on their understanding of both the theoretical literature and the existing empirical research . Therefore, the conceptual framework is often used to develop research questions and hypotheses .

Let’s look at an example of a conceptual framework to make it a little more tangible. You’ll notice that in this specific conceptual framework, the hypotheses are integrated into the visual, helping to connect the rest of the document to the framework.

As you can see, conceptual frameworks often make use of different shapes , lines and arrows to visualise the connections and relationships between different components and/or variables. Ultimately, the conceptual framework provides an opportunity for you to make explicit your understanding of how everything is connected . So, be sure to make use of all the visual aids you can – clean design, well-considered colours and concise text are your friends.

Theoretical framework vs conceptual framework

As you can see, the theoretical framework and the conceptual framework are closely related concepts, but they differ in terms of focus and purpose. The theoretical framework is used to lay down a foundation of theory on which your study will be built, whereas the conceptual framework visualises what you anticipate the relationships between concepts, constructs and variables may be, based on your understanding of the existing literature and the specific context and focus of your research. In other words, they’re different tools for different jobs , but they’re neighbours in the toolbox.

Naturally, the theoretical framework and the conceptual framework are not mutually exclusive . In fact, it’s quite likely that you’ll include both in your dissertation or thesis, especially if your research aims involve investigating relationships between variables. Of course, every research project is different and universities differ in terms of their expectations for dissertations and theses, so it’s always a good idea to have a look at past projects to get a feel for what the norms and expectations are at your specific institution.

Want to learn more about research terminology, methods and techniques? Be sure to check out the rest of the Grad Coach blog . Alternatively, if you’re looking for hands-on help, have a look at our private coaching service , where we hold your hand through the research process, step by step.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

21 Comments

Thank you for giving a valuable lesson

good thanks!

VERY INSIGHTFUL

thanks for given very interested understand about both theoritical and conceptual framework

I am researching teacher beliefs about inclusive education but not using a theoretical framework just conceptual frame using teacher beliefs, inclusive education and inclusive practices as my concepts

good, fantastic

great! thanks for the clarification. I am planning to use both for my implementation evaluation of EmONC service at primary health care facility level. its theoretical foundation rooted from the principles of implementation science.

This is a good one…now have a better understanding of Theoretical and Conceptual frameworks. Highly grateful

Very educating and fantastic,good to be part of you guys,I appreciate your enlightened concern.

Thanks for shedding light on these two t opics. Much clearer in my head now.

Simple and clear!

The differences between the two topics was well explained, thank you very much!

Thank you great insight

Superb. Thank you so much.

Hello Gradcoach! I’m excited with your fantastic educational videos which mainly focused on all over research process. I’m a student, I kindly ask and need your support. So, if it’s possible please send me the PDF format of all topic provided here, I put my email below, thank you!

I am really grateful I found this website. This is very helpful for an MPA student like myself.

I’m clear with these two terminologies now. Useful information. I appreciate it. Thank you

I’m well inform about these two concepts in research. Thanks

I found this really helpful. It is well explained. Thank you.

very clear and useful. information important at start of research!!

Wow, great information, clear and concise review of the differences between theoretical and conceptual frameworks. Thank you! keep up the good work.

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Writing Tips

How to Make a Conceptual Framework

6-minute read
2nd January 2022

What is a conceptual framework? And why is it important?

A conceptual framework illustrates the relationship between the variables of a research question. It’s an outline of what you’d expect to find in a research project.

Conceptual frameworks should be constructed before data collection and are vital because they map out the actions needed in the study. This should be the first step of an undergraduate or graduate research project.

What Is In a Conceptual Framework?

In a conceptual framework, you’ll find a visual representation of the key concepts and relationships that are central to a research study or project . This can be in form of a diagram, flow chart, or any other visual representation. Overall, a conceptual framework serves as a guide for understanding the problem being studied and the methods being used to investigate it.

Steps to Developing the Perfect Conceptual Framework

Pick a question
Conduct a literature review
Identify your variables
Create your conceptual framework

1. Pick a Question

You should already have some idea of the broad area of your research project. Try to narrow down your research field to a manageable topic in terms of time and resources. From there, you need to formulate your research question. A research question answers the researcher’s query: “What do I want to know about my topic?” Research questions should be focused, concise, arguable and, ideally, should address a topic of importance within your field of research.

An example of a simple research question is: “What is the relationship between sunny days and ice cream sales?”

2. Conduct a Literature Review

A literature review is an analysis of the scholarly publications on a chosen topic. To undertake a literature review, search for articles with the same theme as your research question. Choose updated and relevant articles to analyze and use peer-reviewed and well-respected journals whenever possible.

For the above example, the literature review would investigate publications that discuss how ice cream sales are affected by the weather. The literature review should reveal the variables involved and any current hypotheses about this relationship.

3. Identify Your Variables

There are two key variables in every experiment: independent and dependent variables.

Independent Variables

The independent variable (otherwise known as the predictor or explanatory variable) is the expected cause of the experiment: what the scientist changes or changes on its own. In our example, the independent variable would be “the number of sunny days.”

Dependent Variables

The dependent variable (otherwise known as the response or outcome variable) is the expected effect of the experiment: what is being studied or measured. In our example, the dependent variable would be “the quantity of ice cream sold.”

Next, there are control variables.

Control Variables

A control variable is a variable that may impact the dependent variable but whose effects are not going to be measured in the research project. In our example, a control variable could be “the socioeconomic status of participants.” Control variables should be kept constant to isolate the effects of the other variables in the experiment.

Finally, there are intervening and extraneous variables.

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Intervening Variables

Intervening variables link the independent and dependent variables and clarify their connection. In our example, an intervening variable could be “temperature.”

Extraneous Variables

Extraneous variables are any variables that are not being investigated but could impact the outcomes of the study. Some instances of extraneous variables for our example would be “the average price of ice cream” or “the number of varieties of ice cream available.” If you control an extraneous variable, it becomes a control variable.

4. Create Your Conceptual Framework

Having picked your research question, undertaken a literature review, and identified the relevant variables, it’s now time to construct your conceptual framework. Conceptual frameworks are clear and often visual representations of the relationships between variables.

We’ll start with the basics: the independent and dependent variables.

Our hypothesis is that the quantity of ice cream sold directly depends on the number of sunny days; hence, there is a cause-and-effect relationship between the independent variable (the number of sunny days) and the dependent and independent variable (the quantity of ice cream sold).

Next, introduce a control variable. Remember, this is anything that might directly affect the dependent variable but is not being measured in the experiment:

Finally, introduce the intervening and extraneous variables.

The intervening variable (temperature) clarifies the relationship between the independent variable (the number of sunny days) and the dependent variable (the quantity of ice cream sold). Extraneous variables, such as the average price of ice cream, are variables that are not controlled and can potentially impact the dependent variable.

Are Conceptual Frameworks and Research Paradigms the Same?

In simple terms, the research paradigm is what informs your conceptual framework. In defining our research paradigm we ask the big questions—Is there an objective truth and how can we understand it? If we decide the answer is yes, we may be working with a positivist research paradigm and will choose to build a conceptual framework that displays the relationship between fixed variables. If not, we may be working with a constructivist research paradigm, and thus our conceptual framework will be more of a loose amalgamation of ideas, theories, and themes (a qualitative study). If this is confusing–don’t worry! We have an excellent blog post explaining research paradigms in more detail.

Where is the Conceptual Framework Located in a Thesis?

This will depend on your discipline, research type, and school’s guidelines, but most papers will include a section presenting the conceptual framework in the introduction, literature review, or opening chapter. It’s best to present your conceptual framework after presenting your research question, but before outlining your methodology.

Can a Conceptual Framework be Used in a Qualitative Study?

Yes. Despite being less clear-cut than a quantitative study, all studies should present some form of a conceptual framework. Let’s say you were doing a study on care home practices and happiness, and you came across a “happiness model” constructed by a relevant theorist in your literature review. Your conceptual framework could be an outline or a visual depiction of how you will use this model to collect and interpret qualitative data for your own study (such as interview responses). Check out this useful resource showing other examples of conceptual frameworks for qualitative studies .

Expert Proofreading for Researchers

Whether you’re a seasoned academic or not, you will want your research paper to be error-free and fluently written. That’s where proofreading comes in. Our editors are on hand 24 hours a day to ensure your writing is concise, clear, and precise. Submit a free sample of your writing today to try our services.

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What Is a Conceptual Framework? | Tips & Examples

What Is a Conceptual Framework? | Tips & Examples

Published on 4 May 2022 by Bas Swaen and Tegan George. Revised on 18 March 2024.

Keep reading for a step-by-step guide to help you construct your own conceptual framework.

Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualise your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.

A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.

Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.

Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.

In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .

The expected cause, ‘hours of study’, is the independent variable (the predictor, or explanatory variable)
The expected effect, ‘exam score’, is the dependent variable (the response, or outcome variable).

Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualising your expected cause-and-effect relationship.

It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.

Some common variables to include are moderating, mediating, and control variables.

Moderating variables

Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the ‘effect’ component of the cause-and-effect relationship.

Let’s add the moderator ‘IQ’. Here, a student’s IQ level can change the effect that the variable ‘hours of study’ has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.

But the graph looks different when we add our ‘IQ’ moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.

Below, the value of the ‘IQ’ moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.

Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.

Mediating variables

Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.

Here’s how the conceptual framework might look if a mediator variable were involved:

Moderator vs mediator

It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:

A moderating variable is not affected by the independent variable, even though it affects the dependent variable. For example, no matter how many hours you study (the independent variable), your IQ will not get higher.
A mediating variable is affected by the independent variable. In turn, it also affects the dependent variable. Therefore, it links the two variables and helps explain the relationship between them.

Control variables

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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CBE Life Sci Educ
v.21(3); Fall 2022

Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

Associated Data

To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

	Literature reviews	Theoretical frameworks	Conceptual frameworks
Purpose	To point out the need for the study in BER and connection to the field.	To state the assumptions and orientations of the researcher regarding the topic of study	To describe the researcher’s understanding of the main concepts under investigation
Aims	A literature review examines current and relevant research associated with the study question. It is comprehensive, critical, and purposeful.	A theoretical framework illuminates the phenomenon of study and the corresponding assumptions adopted by the researcher. Frameworks can take on different orientations.	The conceptual framework is created by the researcher(s), includes the presumed relationships among concepts, and addresses needed areas of study discovered in literature reviews.
Connection to the manuscript	A literature review should connect to the study question, guide the study methodology, and be central in the discussion by indicating how the analyzed data advances what is known in the field.	A theoretical framework drives the question, guides the types of methods for data collection and analysis, informs the discussion of the findings, and reveals the subjectivities of the researcher.	The conceptual framework is informed by literature reviews, experiences, or experiments. It may include emergent ideas that are not yet grounded in the literature. It should be coherent with the paper’s theoretical framing.
Additional points	A literature review may reach beyond BER and include other education research fields.	A theoretical framework does not rationalize the need for the study, and a theoretical framework can come from different fields.	A conceptual framework articulates the phenomenon under study through written descriptions and/or visual representations.

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

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Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

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Case Studies: Conceptual and methodological frameworks

One of the RECIPES project’s aims is to understand and explain the differences in the application of the precautionary principle in 9 different case topics, in a way that reflects the particular context of the case and the arguments for invoking the precautionary principle.

In this video , Joe Rini, Research Associate at the IASS Potsdam and coordinator of the nine case studies, explains how the principle is applied in different contexts and thematic areas.

The Conceptual framework for comparative multiple case study analysis , part of the “Multiple case-study analysis” module, focuses on how the case study component fits into the overall RECIPES project, on the results of a literature review, on the case study methodology, and on the key risk properties of complexity, uncertainty, and ambiguity. Together with the case study methodology , this report lays the foundation for carrying out the case study research, as well as the comparative analysis of the 9 case studies and transitioning these findings into the rest of the RECIPES project.

The goal of the “Multiple case-study analysis” is to perform 9 case studies and a comparison of the different cases in order to develop scenarios on the future application of the precautionary principle. This will feed into the design of new tools and guidelines for the precautionary principle in respect of reconciling precaution and innovation.

Each case study topic consists of different technological risks , at different stages of development and deployment, and with completely different legal frameworks. As a result, the project team developed a flexible methodological framework that allows the research teams to learn as much as possible about the case topic using the appropriate and relevant sources. The comparative case analysis employs a number of strategies including pair-wise peer review, iterating the analysis against case study researchers, and remaining open to the best way to compare and contrast the cases.

In this project phase, researchers seek to link the precautionary principle and innovation considerations to the key risk properties of complexity, uncertainty, and ambiguity , which represent challenges for the understanding and the ability to communicate effectively about risk and innovation.

Complexity refers to the difficulty of identifying and quantifying causal links between a multitude of potential candidates and specific adverse effects. It includes the interplay of human agency within the context of regulation, innovation, legal decision-making, changing societal values, and vested interests, which result in higher-level complexity than the technological system alone.

Uncertainty is the key risk property in invoking the Precautionary Principle, as its purpose is to allow decision-makers to act despite scientific uncertainty (lack of knowledge about the outcomes or likelihoods, or both, of an event or process). In contrast, risk describes situations where possible outcomes are known, and the likelihoods of those outcomes can be described. Given the subjective complexity faced by decision-makers in the case study topics, the project highlights the functional uncertainty that emerges from highly complex technologies and societal processes.

Ambiguity refers to the fact that outcomes are potentially uncertain, and that different groups will value these outcomes differently. It is present in how scientists and risk specialists evaluate the same evidence, and in how outcomes as risks are evaluated. It implies the need to engage societal stakeholders in the discussion about the harms and benefits of technological innovations and other risks.

Part of this report also concentrates on innovation, as a crucial element for human and societal improvement, which must be regulated to prevent harm, thus leading to a complex interplay between regulation and innovation.

The Methodological framework for the case study analysis details the methodological framework for carrying out the case study research foreseen by the RECIPES project. The document serves both as a methodological framework – an explanation of what to consider in each section – and as a template for delivering the final analysis at the end of the case study research.

Dino Trescher

With thanks to: Project coordination by Maastricht University

Valuable input provided by the RECIPES research partners at Maastricht University, DBT, Rathenau Institute, and Humboldt University

Interesting for

Educational resources and simple solutions for your research journey

What is a Theoretical Framework? How to Write It (with Examples)

Theoretical framework 1,2 is the structure that supports and describes a theory. A theory is a set of interrelated concepts and definitions that present a systematic view of phenomena by describing the relationship among the variables for explaining these phenomena. A theory is developed after a long research process and explains the existence of a research problem in a study. A theoretical framework guides the research process like a roadmap for the research study and helps researchers clearly interpret their findings by providing a structure for organizing data and developing conclusions.

A theoretical framework in research is an important part of a manuscript and should be presented in the first section. It shows an understanding of the theories and concepts relevant to the research and helps limit the scope of the research.

Table of Contents

What is a theoretical framework ?

A theoretical framework in research can be defined as a set of concepts, theories, ideas, and assumptions that help you understand a specific phenomenon or problem. It can be considered a blueprint that is borrowed by researchers to develop their own research inquiry. A theoretical framework in research helps researchers design and conduct their research and analyze and interpret their findings. It explains the relationship between variables, identifies gaps in existing knowledge, and guides the development of research questions, hypotheses, and methodologies to address that gap.

Now that you know the answer to ‘ What is a theoretical framework? ’, check the following table that lists the different types of theoretical frameworks in research: 3


Conceptual	Defines key concepts and relationships
Deductive	Starts with a general hypothesis and then uses data to test it; used in quantitative research
Inductive	Starts with data and then develops a hypothesis; used in qualitative research
Empirical	Focuses on the collection and analysis of empirical data; used in scientific research
Normative	Defines a set of norms that guide behavior; used in ethics and social sciences
Explanatory	Explains causes of particular behavior; used in psychology and social sciences

Developing a theoretical framework in research can help in the following situations: 4

When conducting research on complex phenomena because a theoretical framework helps organize the research questions, hypotheses, and findings
When the research problem requires a deeper understanding of the underlying concepts
When conducting research that seeks to address a specific gap in knowledge
When conducting research that involves the analysis of existing theories

Summarizing existing literature for theoretical frameworks is easy. Get our Research Ideation pack

Importance of a theoretical framework

The purpose of theoretical framework s is to support you in the following ways during the research process: 2

Provide a structure for the complete research process
Assist researchers in incorporating formal theories into their study as a guide
Provide a broad guideline to maintain the research focus
Guide the selection of research methods, data collection, and data analysis
Help understand the relationships between different concepts and develop hypotheses and research questions
Address gaps in existing literature
Analyze the data collected and draw meaningful conclusions and make the findings more generalizable

Theoretical vs. Conceptual framework

While a theoretical framework covers the theoretical aspect of your study, that is, the various theories that can guide your research, a conceptual framework defines the variables for your study and presents how they relate to each other. The conceptual framework is developed before collecting the data. However, both frameworks help in understanding the research problem and guide the development, collection, and analysis of the research.

The following table lists some differences between conceptual and theoretical frameworks . 5


Based on existing theories that have been tested and validated by others	Based on concepts that are the main variables in the study
Used to create a foundation of the theory on which your study will be developed	Visualizes the relationships between the concepts and variables based on the existing literature
Used to test theories, to predict and control the situations within the context of a research inquiry	Helps the development of a theory that would be useful to practitioners
Provides a general set of ideas within which a study belongs	Refers to specific ideas that researchers utilize in their study
Offers a focal point for approaching unknown research in a specific field of inquiry	Shows logically how the research inquiry should be undertaken
Works deductively	Works inductively
Used in quantitative studies	Used in qualitative studies

How to write a theoretical framework

The following general steps can help those wondering how to write a theoretical framework: 2

Identify and define the key concepts clearly and organize them into a suitable structure.
Use appropriate terminology and define all key terms to ensure consistency.
Identify the relationships between concepts and provide a logical and coherent structure.
Develop hypotheses that can be tested through data collection and analysis.
Keep it concise and focused with clear and specific aims.

Write a theoretical framework 2x faster. Get our Manuscript Writing pack

Examples of a theoretical framework

Here are two examples of a theoretical framework. 6,7

Example 1 .

An insurance company is facing a challenge cross-selling its products. The sales department indicates that most customers have just one policy, although the company offers over 10 unique policies. The company would want its customers to purchase more than one policy since most customers are purchasing policies from other companies.

Objective : To sell more insurance products to existing customers.

Problem : Many customers are purchasing additional policies from other companies.

Research question : How can customer product awareness be improved to increase cross-selling of insurance products?

Sub-questions: What is the relationship between product awareness and sales? Which factors determine product awareness?

Since “product awareness” is the main focus in this study, the theoretical framework should analyze this concept and study previous literature on this subject and propose theories that discuss the relationship between product awareness and its improvement in sales of other products.

Example 2 .

A company is facing a continued decline in its sales and profitability. The main reason for the decline in the profitability is poor services, which have resulted in a high level of dissatisfaction among customers and consequently a decline in customer loyalty. The management is planning to concentrate on clients’ satisfaction and customer loyalty.

Objective: To provide better service to customers and increase customer loyalty and satisfaction.

Problem: Continued decrease in sales and profitability.

Research question: How can customer satisfaction help in increasing sales and profitability?

Sub-questions: What is the relationship between customer loyalty and sales? Which factors influence the level of satisfaction gained by customers?

Since customer satisfaction, loyalty, profitability, and sales are the important topics in this example, the theoretical framework should focus on these concepts.

Benefits of a theoretical framework

There are several benefits of a theoretical framework in research: 2

Provides a structured approach allowing researchers to organize their thoughts in a coherent way.
Helps to identify gaps in knowledge highlighting areas where further research is needed.
Increases research efficiency by providing a clear direction for research and focusing efforts on relevant data.
Improves the quality of research by providing a rigorous and systematic approach to research, which can increase the likelihood of producing valid and reliable results.
Provides a basis for comparison by providing a common language and conceptual framework for researchers to compare their findings with other research in the field, facilitating the exchange of ideas and the development of new knowledge.

Frequently Asked Questions

Q1. How do I develop a theoretical framework ? 7

A1. The following steps can be used for developing a theoretical framework :

Identify the research problem and research questions by clearly defining the problem that the research aims to address and identifying the specific questions that the research aims to answer.
Review the existing literature to identify the key concepts that have been studied previously. These concepts should be clearly defined and organized into a structure.
Develop propositions that describe the relationships between the concepts. These propositions should be based on the existing literature and should be testable.
Develop hypotheses that can be tested through data collection and analysis.
Test the theoretical framework through data collection and analysis to determine whether the framework is valid and reliable.

Q2. How do I know if I have developed a good theoretical framework or not? 8

A2. The following checklist could help you answer this question:

Is my theoretical framework clearly seen as emerging from my literature review?
Is it the result of my analysis of the main theories previously studied in my same research field?
Does it represent or is it relevant to the most current state of theoretical knowledge on my topic?
Does the theoretical framework in research present a logical, coherent, and analytical structure that will support my data analysis?
Do the different parts of the theory help analyze the relationships among the variables in my research?
Does the theoretical framework target how I will answer my research questions or test the hypotheses?
Have I documented every source I have used in developing this theoretical framework ?
Is my theoretical framework a model, a table, a figure, or a description?
Have I explained why this is the appropriate theoretical framework for my data analysis?

Q3. Can I use multiple theoretical frameworks in a single study?

A3. Using multiple theoretical frameworks in a single study is acceptable as long as each theory is clearly defined and related to the study. Each theory should also be discussed individually. This approach may, however, be tedious and effort intensive. Therefore, multiple theoretical frameworks should be used only if absolutely necessary for the study.

Q4. Is it necessary to include a theoretical framework in every research study?

A4. The theoretical framework connects researchers to existing knowledge. So, including a theoretical framework would help researchers get a clear idea about the research process and help structure their study effectively by clearly defining an objective, a research problem, and a research question.

Q5. Can a theoretical framework be developed for qualitative research?

A5. Yes, a theoretical framework can be developed for qualitative research. However, qualitative research methods may or may not involve a theory developed beforehand. In these studies, a theoretical framework can guide the study and help develop a theory during the data analysis phase. This resulting framework uses inductive reasoning. The outcome of this inductive approach can be referred to as an emergent theoretical framework . This method helps researchers develop a theory inductively, which explains a phenomenon without a guiding framework at the outset.

Q6. What is the main difference between a literature review and a theoretical framework ?

A6. A literature review explores already existing studies about a specific topic in order to highlight a gap, which becomes the focus of the current research study. A theoretical framework can be considered the next step in the process, in which the researcher plans a specific conceptual and analytical approach to address the identified gap in the research.

Theoretical frameworks are thus important components of the research process and researchers should therefore devote ample amount of time to develop a solid theoretical framework so that it can effectively guide their research in a suitable direction. We hope this article has provided a good insight into the concept of theoretical frameworks in research and their benefits.

References

Organizing academic research papers: Theoretical framework. Sacred Heart University library. Accessed August 4, 2023. https://library.sacredheart.edu/c.php?g=29803&p=185919#:~:text=The%20theoretical%20framework%20is%20the,research%20problem%20under%20study%20exists .
Salomao A. Understanding what is theoretical framework. Mind the Graph website. Accessed August 5, 2023. https://mindthegraph.com/blog/what-is-theoretical-framework/
Theoretical framework—Types, examples, and writing guide. Research Method website. Accessed August 6, 2023. https://researchmethod.net/theoretical-framework/
Grant C., Osanloo A. Understanding, selecting, and integrating a theoretical framework in dissertation research: Creating the blueprint for your “house.” Administrative Issues Journal : Connecting Education, Practice, and Research; 4(2):12-26. 2014. Accessed August 7, 2023. https://files.eric.ed.gov/fulltext/EJ1058505.pdf
Difference between conceptual framework and theoretical framework. MIM Learnovate website. Accessed August 7, 2023. https://mimlearnovate.com/difference-between-conceptual-framework-and-theoretical-framework/
Example of a theoretical framework—Thesis & dissertation. BacherlorPrint website. Accessed August 6, 2023. https://www.bachelorprint.com/dissertation/example-of-a-theoretical-framework/
Sample theoretical framework in dissertation and thesis—Overview and example. Students assignment help website. Accessed August 6, 2023. https://www.studentsassignmenthelp.co.uk/blogs/sample-dissertation-theoretical-framework/#Example_of_the_theoretical_framework
Kivunja C. Distinguishing between theory, theoretical framework, and conceptual framework: A systematic review of lessons from the field. Accessed August 8, 2023. https://files.eric.ed.gov/fulltext/EJ1198682.pdf

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Published: 26 June 2024

Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists

Ashty S. Karim ORCID: orcid.org/0000-0002-5789-7715 1 , 2 ,
Dylan M. Brown ORCID: orcid.org/0000-0001-8153-7683 1 , 2 ,
Chloé M. Archuleta 1 , 2 ,
Sharisse Grannan 1 , 3 ,
Ludmilla Aristilde ORCID: orcid.org/0000-0002-8566-1486 1 , 4 ,
Yogesh Goyal ORCID: orcid.org/0000-0003-3502-6465 1 , 5 , 6 ,
Josh N. Leonard ORCID: orcid.org/0000-0003-4359-6126 1 , 2 ,
Niall M. Mangan ORCID: orcid.org/0000-0002-3491-8341 1 , 7 ,
Arthur Prindle 1 , 2 , 8 ,
Gabriel J. Rocklin 1 , 9 ,
Keith J. Tyo ORCID: orcid.org/0000-0002-2342-0687 1 , 2 ,
Laurie Zoloth 1 , 10 ,
Michael C. Jewett 1 , 2 nAff13 ,
Susanna Calkins ORCID: orcid.org/0009-0001-3653-0236 1 , 11 nAff14 ,
Neha P. Kamat ORCID: orcid.org/0000-0001-9362-6106 1 , 2 , 12 ,
Danielle Tullman-Ercek ORCID: orcid.org/0000-0001-6734-480X 1 , 2 &
Julius B. Lucks ORCID: orcid.org/0000-0002-0619-6505 1 , 2

Nature Communications volume 15 , Article number: 5425 ( 2024 ) Cite this article

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Synthetic biology

Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society’s most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales—molecular, circuit/network, cell/cell-free systems, biological communities, and societal—giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists.

Cell-free gene expression: an expanded repertoire of applications

Trimming the genomic fat: minimising and re-functionalising genomes using synthetic biology

Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment

Introduction.

Synthetic biology is the fundamental science and engineering research that allows us to reuse, repurpose, and reconfigure biological systems to address society’s most pressing challenges. Synthetic biologists leverage tools and concepts from biology, chemistry, physics, mathematics, engineering, computer science, and the social sciences to harness the enormous diversity of biological function, creating new biological systems that are advancing agriculture 1 , 2 , sustainable biomanufacturing 3 , 4 , 5 , and medicine 6 , 7 , 8 , 9 . Recognition of this potential has led to synthetic biology becoming a major driver of the growing bioeconomy 10 , 11 , 12 . This in turn has created a surge of interest in synthetic biology, attracting an increasing number of researchers and students from around the world who bring diverse backgrounds and perspectives to the field.

While the potential of synthetic biology is clear, developing an approach to train students that meets the diverse needs of this field faces two related challenges. The first challenge is that the field has developed from threads rooted in multiple individual disciplines, resulting in a broad diversity of concepts that must be taught and integrated. At the core are the biological concepts that explain how a function is encoded within a DNA sequence, how control of gene expression activates this function, and how this function can be changed by manipulating the DNA sequence. Building upon this, early synthetic biology incorporated concepts from physics and computer science abstractions that viewed biological components as being ‘wired’ in genetic networks that controlled information flow, much like electronic circuits 13 , 14 , 15 . At the same time, systems biologists were using some of these concepts to study and manipulate cellular networks and signaling pathways 16 , 17 , and chemical engineers were using principles of dynamics and control to engineer metabolic processes for bioproduction 18 , 19 . From these roots, mathematical approaches developed in systems biology were added 20 as well as concepts from chemistry to create new components not yet found in nature 21 . As the field has advanced concepts out of the lab and into the world, approaches from ethics, social sciences, business, and law have become important to incorporate so that researchers innovate responsibly with positive societal impacts 22 , 23 , 24 .

The conceptual breadth of synthetic biology is difficult to cover in any single training program which gives rise to the second challenge for training in synthetic biology—undergraduate and graduate students are often siloed within single disciplines and degree programs, creating barriers to learning outside of these traditional boundaries. Thus, students receive most of their exposure to synthetic biology through elective courses or research in labs rather than through a structured curriculum as might be associated with other mature disciplines. This can lead to synthetic biology training that emphasizes a narrow set of concepts over others or focuses on content rather than “science practices” 25 that are known to support deep learning 26 , 27 . For example, there might be an intense focus on training students how to manipulate CRISPR genome editing systems on the molecular scale, but very little integration of how deficiencies of the molecular-level genome targeting affect the function of the larger cellular system, tissue, or organism in which the CRISPR system is utilized.

The field must overcome these training challenges, as integration of these multi-disciplinary concepts is critical for developing successful synthetic biology technologies. For example, cellular synthesis of products from sustainable feedstocks requires understanding the underlying reaction chemical kinetics (chemistry), enzyme biophysics and substrate transport (physics), genetic regulation of enzymes and cellular physiology (biology), reactor vessel scale-up (engineering), and socio-techno-economic analyzes (business). Similar combinations of expertise are also required to create synthetic biology technologies that address other important societal goals in sustainability, environmental health, and human health.

Fortunately, important first steps to developing new training approaches are beginning to happen with the emergence of new undergraduate opportunities and PhD programs in synthetic biology. For high school students and undergraduates, experiential learning opportunities have emerged to facilitate hands-on learning, such as BioBits Kits 28 , 29 , 30 , 31 , the ODIN marketplace for genetic engineering supplies 32 , BioBuilder 33 , and others 34 , 35 . In addition, opportunities such as the international Genetically Engineered Machines (iGEM) competition, the Build-a-Genome Course 36 , and the Cold Spring Harbor Summer Course in Synthetic Biology have paved the way to explore synthetic biology and this integration of disciplines. Though, there is an opportunity to refine and expand these efforts with an overarching framework that more systematically incorporates concepts from the many fields contributing to synthetic biology. At the PhD level, two notable programs in the US (Rice University) and the UK (Imperial College) have begun to explore systematic approaches to training in synthetic biology. Rice’s PhD program covers physical biology, systems biology, and synthetic biology, requiring one dedicated course in synthetic biology. Imperial College’s program starts with a Master of Research degree followed by a PhD with courses in systems biology and synthetic biology. Both programs are structured to provide training to students to integrate concepts across disciplines but require significant prerequisites in STEM. But how do students who may not have access to one of these programs receive this type of synthetic biology training? The Engineering Biology Research Consortium (EBRC) has worked to address this by creating an “Introduction to Engineering Biology” curriculum module to give students a basic understanding of the tools, technologies, and opportunities in synthetic biology 37 . While each of these programs are important first steps, a critical opportunity remains for creating a new approach to synthetic biology training that can: (1) teach synthetic biologists of the future how to traverse and integrate multiple disciplines into their understanding of the field, no matter what their specific background; (2) be accessible to students from a range of backgrounds in order to democratize opportunity and access to synthetic biology concepts; and (3) be adaptable to incorporate advances in a rapidly changing field.

To address this opportunity, we created a conceptual framework for synthetic biology training that can be used in any course or program, developed over the past several years as part of the National Science Foundation-sponsored “Synthesizing Biology Across Scales“ graduate training program at Northwestern University. The framework is based on the observation that every synthetic biology technology is made up of components that function across multiple scales—molecular, circuit/network, cell/cell-free systems, biological communities, and societal—and that the success of these technologies is deeply dependent on their interfaces (Fig. 1 ). This scales framework can be found in other engineering disciplines as well, such as in electrical and computer engineering where technologies naturally break down along scales, from transistors, to circuits, to chips, to devices, and integrate across scales to enable powerful applications.

A schematic representation of the deconstruction framework: biotechnologies can be deconstructed along scales to identify biological phenomena that are important to the technology at each scale, understand the principles by which these phenomena work at that scale, and identify the important interfaces between scales where engineering challenges often arise. Deconstructing technologies along scales allows multidisciplinary concepts to be mapped and applied at individual scales (annotated) and allows new technologies to be reconstructed by combining elements and applying concepts at each scale.

Here, we describe a course-based implementation of the scales framework that teaches undergraduates, masters, and PhD students how to deconstruct synthetic biology across scales, analyze how components interact at interfaces between scales to yield emergent phenomena, conceptualize how to combine components across scales to create new synthetic biology solutions to global challenges, and incorporate the consideration of ethics when developing synthetic biology technologies. Our vision is that training students to deconstruct synthetic biology technologies across scales will help them (1) recognize where their domain expertise fits within a particular synthetic biology technology, (2) identify their own knowledge gaps that can be filled through additional topical learning or research collaborations, and (3) gain a holistic picture of the landscape of pieces that must work together to create a successful technology. Each of these “science practices,” which allow students to actively engage in scientific inquiry, promotes disciplinary learning and development as a scientist 25 . Emphasizing the societal scale, we hope to drive responsible innovation by training students to think of concepts in ethics, access, equity, and societal-level impact early and often throughout the development of synthetic biology technologies. We envision that the scales framework and the corresponding deconstruction approach is a launching point for the field of synthetic biology to provide a foundational way of training the next generation of synthetic biologists.

The scales framework for synthetic biology

The scales framework is a conceptual way to understand how to build synthetic biology solutions to address societal challenges how biological phenomena work across multiple scales (Fig. 1 ). The deconstruction approach to teaching this framework posits that for a given synthetic biology technology, the components and functions that work together to form that technology can be thought of as working along distinct scales: molecular, circuit/network, cellular, biological communities, and societal. Each of these scales represent a distinct set of components and functions and the physical, chemical, biological, and social science concepts that naturally drive function or impact at that scale. In addition, interactions between components at the interfaces between these scales often give rise to emergent behavior and engineering challenges that are important for real-world applications. Below we briefly describe the components, functions, and concepts that arise at each scale.

The molecular scale includes the individual molecular components of biological systems (e.g., nucleic acids, proteins, lipids, metabolites) and the physical, chemical, and mathematical principles required for understanding and engineering the function of these components. Driving concepts at the molecular scale include the biophysics of protein and RNA folding (including concepts such as free energy folding landscapes and folding kinetics), molecular interactions, enzymology, and others 38 , 39 . The functions that occur on this scale are molecular structure, complex assembly 40 , catalysis (enzymes) 41 , motion (molecular motors) 42 , charge transport 43 , and others that are carried out by individual molecules.

The components of the network/circuit scale consist of collections of molecules that interact to give rise to higher-order functions, often depending on which subset of interactions are present. Network/circuit scale functions are those that biological systems utilize to propagate information, coordinate physiological states, and implement control over those states 44 , 45 . Common biological functions at this scale include coordination and regulation of gene expression (transcription/translation), propagation of information in biological systems in signaling networks, and control of molecular transformations in metabolic reaction networks 44 , 45 , 46 , 47 , 48 .

Phenomena at the cell/cell-free systems scale encapsulate the components of the molecular and network/circuit scale, creating a biochemical environment that supports systems-level functions. Biological functions at this scale include coupled transcription, translation, and post-translational modification 49 , mechanobiology 50 , cell division 51 , exo- and endocytosis 52 , cell sensing 53 , somatic hypermutation (i.e., antibody production) 54 , homeostasis 55 , and transport 55 , 56 . Sometimes these functions can be spatially organized within a range of cellular components such as lipid vesicles, bacterial microcompartments, and macromolecular condensates that organize molecules in membrane-less organelles 57 , 58 . At this scale, concepts that govern the behavior include molecular transport, reaction diffusion, energy and redox balance, and others. Cell-free systems are included here because they can perform many of the same functions as cells with similar levels of biological complexity 7 , 59 , 60 .

The components of the biological communities scale include multi-cellular interactions and communities of organisms that work together to give rise to higher-order functions and emergent behaviors 61 . There is a rich diversity of systems at this scale, ranging from microbiomes and biofilms to tissues, organs, and even whole bodies 62 , 63 , 64 , 65 , 66 . Biological functions that occur on this scale include emergent microbial community dynamics 65 , cell-cell signaling 67 , 68 , biofilm formation 69 , tissue-scale phenomena such as tissue growth 70 , regeneration and function, cell-material interactions, inter- and intraspecies metabolic interaction 71 , and others 72 , 73 , 74 . Population dynamics, microbial ecology, metagenomics, and micro- and macroevolution play a significant role at this scale.

Finally, the societal scale encompasses concepts that will determine how synthetic biology technologies impact, influence, and change the world around us. Functions at this scale include technology distribution; equity and affordability in technology access; social, biological, and economic sustainability; public perception; legal and regulatory aspects of technology (intellectual property and policy); and more 24 , 75 , 76 , 77 . The concepts associated with this scale include the philosophical ethics of synthetic biology research, stakeholder interaction and analysis, frameworks for user studies and field trials, lifecycle analysis, and quantitative estimates of the needs and viability of synthetic biology technologies 78 , 79 . Traditionally this scale has been separated from science and engineering at the other scales, yet it contains components and functions driven by scientific principles similar to the other scales. Recognizing the need to train ethically minded practitioners, we emphasize the integration of the societal scale as one of the five key scales so that we consider it as an important part throughout training and technology development.

The interfaces between these scales give rise to emergent behavior important for applications, though this can also present challenges for engineering. By understanding these interfaces, we can learn general “rules” to emergence of complexity and, in turn, engineer-improved technologies. We can understand these interfaces through common methods for bridging across scales. For instance, mathematical techniques such as mean-field averaging, which assumes that many identical components interact in similar ways 80 , and asymptotic analysis, which characterizes the strongest interactions between heterogeneous components 81 , enable us to analyze the transition between scales. The fundamental properties, process, and results of mapping interactions to macro-level behavior inform our understanding of the emergence of complexity across scales 82 , 83 , 84 , 85 , 86 .

For some technologies that we deconstruct, the scales are clear. Practitioners can identify a global challenge (e.g., chemical production, environmental health, human health) and deconstruct synthetic biology technologies that address them (e.g., semi-synthetic artemisinin, bacterial nitrogen fixation, CAR-T therapeutics) (Box 1 ). However, for some technologies, scales with strong interfaces may naturally blur together; for instance, it is hard to define exactly when a molecular scale complex that regulates protein phosphorylation begins to process and propagate information through a phosphorylation cascade at the network scale 87 . Learning to deconstruct synthetic biology solutions allows practitioners to understand when the boundaries between scales become ‘fuzzy’, so that they can take advantage of the gradation of phenomena that occur across different spatial and temporal scales and engineer them accordingly. By using case studies on real-world synthetic biology technologies, we can teach core concepts of the field to students from diverse backgrounds in an interactive and engaging way.

Box 1. Deconstruction case studies

The deconstruction approach provides a framework to analyze synthetic biology technologies through case studies . Synthetic biological systems that address challenges in (A) the environment (nitrogen fixation), (B) sustainable bioproduction (semi-synthetic artemisinin production), and (C) human health (CAR-T therapies) are deconstructed along scales.

Box 1 Text . Many synthetic biology technologies can be broken down into components that must work together across the molecular, circuit/network, cellular, and biological communities scales. For each technology, societal scale concepts concerning ethics, equity, access, intellectual property, and business considerations are critical to its success. Here are several examples of flagship synthetic biology technologies deconstructed across these scales.

Environmental Health—nitrogen-fixing bacteria for sustainable fertilizers. Nitrogen-fixing bacteria that can produce fertilizer compounds offer a potential solution for sustainable farming, currently challenged by an over-reliance on energy-intensive chemical fertilizers that cause environmental contamination when overapplied 91 . Engineering a bacteria to produce enough fixed nitrogen for farming needs requires understanding and engineering across scales. At the molecular scale, the core nitrogen-fixing reaction is carried out by the nitrogenase enzyme complex 92 , 93 . Nitrogenase requires coordinated interaction with electron-transporting proteins that work together at the network/circuit scale 92 , 108 . Also important at the network/circuit scale are the layers of genetic circuitry that coordinate the synthesis of the many nitrogenase components and its cofactor synthesis enzymes—this regulation must be understood as it presents potential barriers to controlling nitrogenase expression 108 . Both of these scales are embedded in a cellular chassis that must support their function 94 , 95 . Finally, the eventual application of a nitrogen-fixing bacteria in the soil requires considerations at the biological communities scale to understand how this bacteria would interact with the native soil microbiome and the target plants 109 , 110 . At the societal scale, questions arise as to the safety and biocontainment strategies needed when releasing engineered organisms, technology access, which intellectual property strategies that can benefit the most people including farmers, and stakeholder analysis to understand if the technology will be adopted.

Biochemical Production—semi-synthetic artemisinin production. Artemisinin is a frontline anti-malarial drug produced in the plant Artemisia annua , and its availability can be challenged by seasonal production variation 111 . Microbial bioproduction of more artemisinin requires understanding and engineering across scales. Often bioproduction strategies genetically integrate metabolic pathways into a heterologous host that is then further engineered to make the molecule of interest 18 . At the molecular scale, artemisinin production requires tailored cytochrome P450s and dehydrogenases 96 . At the network scale, these enzymes, along with others, must work together in metabolic pathways with carbon flux carefully controlled to minimize toxic intermediates and side reactions 112 , 113 . This control requires selection of an appropriate cellular scale host organism that can support the necessary central carbon metabolism and tolerate the acid toxicity of the product 114 , 115 . As production is scaled, the communities scale becomes important, as scale up requires populations of cells to interact with one another in a complex bioreactor environment where availability and transport of nutrients (e.g., oxygen levels, pH) can become important 116 , 117 , 118 . At the societal scale, questions of cost and profitability, sustainability of production, infrastructure requirements, accessibility to the biochemicals, public perception, and acceptance of the technologies naturally arise.

Human Health—CAR-T cell therapy. Chimeric antigen receptor (CAR) T-cell therapy is a promising approach to provide treatments for an expanding range of cancers 97 , 98 , 119 . CAR-T therapies are designed to reprogram the natural abilities of the human immune system to recognize cancer cells and trigger their destruction, and as such they require engineering and consideration across multiple scales. At the molecular scale, a key challenge is designing the CAR protein to recognize features that are unique to the surface of cancer cells while not recognizing healthy cells 120 . Once a cancer cell is recognized, the CAR must activate processes at the network scale within the T cell, triggering cell-mediated killing and gene expression programs 121 . At the cellular scale, the importance of cell identity becomes critical, since CARs can be implemented in a range of immune cell types, with each choice impacting CAR performance 121 . At the biological communities scale, concepts related to side effects (including off-target and on-target activity) become important, creating a natural interface to the molecular scale at which CAR variants can be engineered to have improved specificity 122 . In this scale, concepts such as transport also become important, such as distinct challenges associated with using CAR-T cell therapies to treat solid tumors because of limited penetration, as compared to blood cancers in which T cells can more readily access cancerous cells. At the societal scale, challenges and concepts related to safety, ethics, clinical trials and cost and access of the treatment become important when analyzing the success of the technology 123

A case studies-based course in the deconstruction approach

Our course teaches senior undergraduate students and first-year graduate students from a range of degree programs how to analyze problems and solutions related to synthetic biology through the deconstruction approach. The learning objectives of this course are for students to be able to: (1) deconstruct biological phenomena along the scales that they occur; (2) analyze how engineering choices made at one scale affect biological function at another scale; (3) assemble potential synthetic biology solutions to global challenges across scales; and (4) identify the scientific value and impacts of synthetic biology research on broader societal goals, as well as ethical considerations that arise. The course has no prerequisites and was designed to achieve these learning objectives through a case studies pedagogical approach, which is proven to enhance learning and student engagement 88 , allowing integration of multi-disciplinary concepts across scales.

For the course, we identified three of the most pressing global challenge areas currently being addressed by synthetic biology to develop case studies—environmental health, biochemical production, and human health (Box 1 ) 2 . Each challenge area is taught over the course of a three-week module and includes a historical basis for the global challenge (e.g., defining the problem), current synthetic biology research and commercial endeavors in this area, a deconstruction of at least one poignant example, homework assignments (e.g., investigating and designing solutions), student presentations (e.g., explanation), and a guest lecture by an expert in that area. We introduce each challenge area loosely based on the Heilmeier Catechism 89 , defining the problem, how it is addressed today, how synthetic biology might play a role in addressing it, and a discussion on the societal risks, success, and future of synthetic biology in the challenge area. Each module builds on the previous module, adding a deeper layer of understanding of the deconstruction approach (Fig. 2 ). For example, in the first module we define the scales in the context of a guided case study, in the second module we ask students to weight the importance of each scale to a chosen technology, and in the third module students tackle the challenges at the interfaces between scales. While our course used environmental health for module 1, biochemical production for module 2, and human health for module 3 (Fig. 2 ), the progression of modules can be taught using any topic sequence, allowing the course to be adapted to the needs or interest of different teaching environments and to new topics that emerge as the field progresses. In addition, the division of the course into modules is naturally amenable to team teaching approaches.

The course is split across three modules with each subsequent module exploring deeper concepts of the deconstruction approach. Different case studies can be used to implement each module, depending on instructor and student interests. Here we show the progression from environmental health to biochemical production to human health topics in the Northwestern course.

We begin the course by introducing environmental health challenges in the context of United Nations Sustainable Development Goal 3 90 —good health and well-being—and survey the many ways synthetic biology could contribute to solutions in soil, water and air quality, carbon sequestration, waste valorization, remediation, sustainable resource recovery, sustainable biomaterials, recycling, and sustainable fertilizers. We then focus on our first major deconstruction case study on bacterial nitrogen fixation for sustainable fertilizers (Box 1 ). The nitrogen fixation example also serves as the first introduction to the five scales, as it is deconstructed in the narrative of imagining a synthetic biologist wanting to address the environmental challenge of chemical fertilizers. After a historical introduction to Crooke’s challenge of the need for fertilizers, the geopolitics of fertilizer distribution, and the development of the Haber-Bosch process 91 , we then imagine how a synthetic biologist may partner with nature to create a more sustainable way to produce fertilizer. This naturally starts at the cellular scale by identifying nitrogen fixing bacteria, and quickly dives into the molecular and network/circuit scales on the quest to understand how to engineer the microbe to fix more nitrogen through understanding the nitrogenase enzyme complex and its regulation 92 , 93 . Reviewing the literature gets us back to the cellular scale to understand which microbes are optimal 94 , 95 . The biological communities and societal scales naturally emerge when we consider applying engineered microbes to the field. Two guest lectures in this area, one focusing on academic synthetic biology research in this area and another representing synthetic biology startup companies, give students multiple perspectives to understand how this area is actively being pursued.

The focus on fertilizer and agriculture naturally transitions the course to the biochemical production challenge area, where we begin by understanding how commodities such as food, energy, water, materials, and chemicals are intricately linked, and how holistic understanding of a challenge area can give rise to useful solutions. We deconstruct early advances of molecular biology and early synthetic biology technologies such as golden rice, Roundup Ready® crops, and first, second, and third generation biofuels. Our major deconstruction case study in this section is the semi-synthetic artemisinin project 96 (Box 1 ), where we use class time to deconstruct the technology along each scale and identify the scales in which key hurdles were overcome during the project. Importantly, we discuss the number of resources that were dedicated to the project, the amount of fundamental knowledge that was gained, the technologies developed during the project that are being used in other areas of synthetic biology, and the current commercial use of the technology as way to evaluate the success of the project. An industry speaker is included in this section to give students perspective on sustainable bioproduction products that are actively being marketed and sold.

The course finishes with the human health challenge area, where we begin by introducing the unique layers of complexity that occur at the biological communities and societal scales. We frame the need for synthetic biology solutions in human health by discussing the historical development of pharmaceuticals and the promise of synthetic biology for developing new therapeutic approaches 6 . We then dive into cell-based therapies and recent synthetic biology tools that allow for molecular, network, and cellular scale engineering of mammalian cells, and control of variability across a population of cells. Our deconstruction case studies in this section are CAR-T-cell therapies 97 , 98 (Box 1 ) and gene drives 99 . Following a student-led deconstruction of these activities, we use discussion-based learning techniques to emphasize the ethics of human subject research through case studies on the use of HeLa cells and personal genomics. Our guest lecturer in this area is a societal scale expert (e.g., bioethicist, artist) that emphasizes the application of societal scale concepts in the course. In addition, we include a guest lecture from one of our faculty to introduce research actively being pursued in our institution.

An important component of our pedagogy is activities for students to actively deconstruct technologies across scales, including individual assignments, small-group evaluation of technologies, and cooperative learning activities based on inclusive teaching practices 100 , 101 , 102 (Fig. 3 ). This begins in the environmental health section where students are assigned to pick a technology and deconstruct it without the scales framework introduced ( assignment 1 ). Once the nitrogen fixation technology is deconstructed in lectures, they are then asked to revisit the deconstruction of this same technology with the scales framework ( assignment 2 ), and present to class. In the biochemical production section, the course begins to flip from instructor-centric to student-centric deconstructions through additional group work. We randomly paired students together and asked them to pick a technology to deconstruct and go beyond just identifying the scales by weighting the importance of each scale within their chosen technology (Fig. 3A ). We found that students interpret the importance of scales differently. For example, two students focusing on food alternatives found different scales are important for different technologies, while in some metabolic engineering examples, students found the network/circuit scale to be of importance regardless of the selected technology. This type of cross-case comparison helped promote the abstraction of deconstruction concepts.

A Students deconstructed technologies in groups of two and assessed the importance of each scale for their given technology. Each group was asked to rank how important each scale was for their selected synthetic biology technology from 0 (no importance) to 10 (high importance). Radar plots are displayed for different student groups’ responses where each geometric shape or area represents one response. Differences in student responses on ‘the importance of scales’ are depicted in three ways: deconstructing the same technology, deconstructing different technologies that aim to tackle a similar problem, and deconstructing similar technologies within a research area. B Students deconstructed technologies across scales using an inclusive teaching technique called a jigsaw group activity. Each circle represents one student in the course, each letter is a specific scale, and each number corresponds to a specific grouping of students that are assigned a different technology. Home groups allow students to frame their deconstruction across different scales, while scale expert groups allow students to gain expertise in a scale by comparing across different technologies. Reassembling back into home groups allows students to share their expertise and learn from each other. Discussing the societal scale across technologies as a class allows comparisons between different technologies.

In the human health section, CAR-T and gene drives are deconstructed through a unique jigsaw method, a cooperative and inclusive learning approach that requires students to address a complex problem from various theoretical and/or methodological approaches (Fig. 3B ) 100 , 101 , 102 . Students are first split into several “home (jigsaw) groups” consisting of one “scale expert” at the molecular, network, cell/cell-free, and biological communities scales to discuss a game plan to deconstruct their assigned technology. While students do not necessarily have expertise in their assigned scale, using the term ‘expert’ is meant to inspire confidence in students to learn scale concepts and then empower them to teach their peers. Students then divide small “scale expert groups” and use peer instruction to develop deep knowledge in a specific scale (a ‘piece of the puzzle’). The students then return to their home groups, synthesize their expert information into a compelling deconstruction of their technology and together discuss the societal scale. At the end of this activity, we come back to a large group discussion of technology challenges across scale interfaces and the societal implications of the technology.

Throughout the course, each student is assigned to conduct a newsreel presentation by presenting one synthetic biology research article and one news item of their choice to the class using the scales framework, creating a consistent source for ethics discussions and other societal scale topics. Finally, students perform and present a deep dive deconstruction of a technology of their choice as their final project. In this way the course incorporates a wide range of technology case studies that are both instructor and student chosen. The ability for students to drive most of the topic selection (e.g., engaging in the practice of science) in this course builds off the known positive impact of choice on student engagement 103 and allows course content to adapt as the field of synthetic biology evolves.

By framing the course around biotechnologies and the scales of synthetic biology, we can teach synthetic biology in a way that is agnostic to student backgrounds and expertise. In this way, we can introduce multi-disciplinary concepts from biology, chemistry, physics, mathematics, computer science, engineering, and the social sciences in the context that they are needed within a given scale. This helps students identify where their background and expertise can be incorporated within a synthetic biology technology. The scales framing also allows students to identify their own knowledge gaps so that they can fill them with further study and collaboration.

Evaluating success of the deconstruction approach

Teaching a course rooted in quantitative fundamentals of synthetic biology technologies, but largely taught through learning how to define problems, develop models, construct explanations, and build arguments (e.g., scientific practices) has proven to be a rewarding experience for students. In total, 103 students from chemistry, biology, biomedical engineering, civil and environmental engineering, and chemical engineering programs took the course across three separate years that the course was offered at Northwestern University. Students across implementations of the course resonated with the deconstruction approach as can be seen from an analysis of end-of-course written reflections as part of their final projects (Table 1 ). Responses, subjected to thematic analysis 104 , revealed that students not only enjoyed the course but also developed holistic ways of thinking, critical thinking skills, an ability to recognize challenges at the interface between scales, and an understanding of how they would use the deconstruction approach outside the course (e.g., reading literature, career aspirations). Years after taking the course, one student reflected in an interview that, “[the scales framework] has been super helpful for the conception of my own research because I’m often on the lower scales, more of the mechanisms and specific interactions of molecules and proteins. Anytime we’re making single changes to add more of this one component to our mixture, it really changes everything else … and it goes beyond these lower-level interactions. It’s not that I’m consciously trying to think in that way, but I think it’s been baked into me. These scales all do interact and are relevant. Even when it feels like I’m making small changes, I feel I need to stop and consider the potential for repercussions and effects that would climb up the ladder.” Students have applied and seen value in the skills developed in the course years after taking the course.

Integrating the societal scale into a STEM course

An important goal of the deconstruction approach is to train students to think about the societal scale impacts of their work as it is being conceptualized, rather than after it has been done. Traditional science and engineering training often leaves out societal scale components or relegates them to special courses in the humanities (e.g., bioethics) or business (e.g., intellectual property) that do not fully integrate these topics within science and engineering. We integrated the societal scale into our course in three specific ways: (1) training students to identify challenges at the societal scale, and biological functions needed to address these challenges, through course assignments; (2) creating space for students to explore the connectedness of how science and engineering choices made at one scale could drive outcomes at the societal scale through in-class discussion grounded in bioethics best practices 105 ; and (3) inviting a guest lecturer with expertise in bioethics and the societal scale to guide an informed and meaningful discussion around this scale using examples from their own work. Our intent was to introduce students to the many topics this scale encompasses (e.g., bioethics, technology access and equity, intellectual property, business models, investment strategies, and policy), teach them to identify connections between the societal scale and the four other scales and teach them how to discuss and grapple with societal scale challenges for any technology.

Our specific societal scale and bioethics discussion activities were based on bioethics best practices 105 . We conducted think-pair-share class discussions with prompts along several themes: (1) themes related to societal perceptions of biotechnology; (2) themes related to unintended consequences of developing biotechnologies; and (3) themes related to additional safeguards and regulatory processes that could be developed in response to unintended consequences. For example, during the human health part of the course when we discussed gene drives as a method to combat malaria. Our discussions touched on intellectual property, genetically modified organisms, and regulations; molecular and cellular approaches to biocontainment to mitigate risk; and public perception of technology and what is natural. We wove these types of concepts into each case study, student deconstruction assignments and discussions, and a standalone discussion of the ethics of human subject research. The most recent iteration of the course also had an artist lead discussion of how science and art can interface to impact the world. As a result, students often expressed excitement and eagerness to think about the societal scale and how they might advance or disrupt the world in which we live. In our discussions we did not try to seek an answer to questions at this scale but rather focused on presenting and discussing different viewpoints, emphasizing the importance of considering societal scale challenges. Many students came away with their viewpoints expanded, with 34% commenting on the importance of societal scale thinking (Table 1 ).

Adapting the approach to other learning environments

In developing the course, we created a syllabus, a schedule, and content that is designed to be adapted to other learning environments. Our goal is for the scales framework and the deconstruction approach to be adaptable to support a range of learning objectives within different institutions and programs and to be adapted to changes with the field. Towards this goal, we have created and included here a modular version of our course structure, a syllabus, and the three evaluated deconstruction assignments with corresponding rubrics for any instructor who would like to use them or adapt them for a course in synthetic biology (see Supporting Information). The content can be used in several ways. If instructors are comfortable with the progression of topics from environmental health to biochemical production to human health, then the course plan could be used verbatim to implement a full course that could serve as an introduction to synthetic biology, or as a second course in synthetic biology. If instructors would rather begin with a different topic area, then they could use our course plan and structure as an example and choose a different framing example in a different topic area (Box 1 ) to do a full deconstruction of a technology at the beginning of the course, followed by similar activities to explore other topic areas. This method could also be used to implement a standalone module on the deconstruction approach within a different synthetic biology course. In this model, case studies can be used to get students excited by the field before deep diving into synthetic biology tools and principles that are typically discussed in introductory synthetic biology courses. It was important to select case studies that we as instructors had expertise in to give the most enriching experience for our students and to help facilitate their learning. Including more formal cross-case study comparisons would help enhance student understanding of the deconstruction approach and mobilize knowledge. Portions of the course could even be used as modules to add an ethics component to an existing synthetic biology course. In addition, the three framing deconstruction assignments can be added into existing courses to teach and evaluate student learning of the deconstruction approach. While our implementation of the course was tailored to a mixed class of advanced undergraduates, masters, and beginning PhD students, we envision the approach being easily tailored to other groups.

Over three years of implementing this course, several best practices for implementation appeared. Initially the course was developed for synchronous, remote learning and was adapted to in-person sessions which means that the course is fully compatible with remote, in-person, or hybrid teaching. At the heart of the course are student presentations and discussions. This made the course challenging to implement when class sizes reached more than 30 students. The number and type of presentations can be changed to address this. We also struggled to identify the proper number of assignments and in-class activities given that most assignments were free-form writing. Giving comprehensive rubrics and instructions helped manage expectations and improved student enjoyment of the course. While we had no prerequisites for the course, many students who took previous biology and/or synthetic biology courses had an advantage. Implementations of the course where this is the only available course in synthetic biology may benefit from an “introduction to synthetic biology” module to familiarize students with tools and techniques in the field. Despite differences in prior knowledge, we had students come to this course from chemistry, biology, engineering, and biotechnology and left inspired to work in synthetic biology.

Looking to the future

As the field of synthetic biology matures, there is a compelling opportunity to explore common training approaches across institutions that can be used to accelerate progress in the field even further. As a highly multi-disciplinary field, it can be challenging to find a convergent training approach that incorporates cross-field concepts while giving students and practitioners a common language to integrate these concepts towards a common engineering goal. We believe that by emphasizing the scales of engineered biological systems and their application use cases, the scales framework and the deconstruction approach helps to achieve this goal and can incorporate discipline-specific concepts simultaneously. In this way, the scales framework facilitates the teaching of “science practices” (e.g., modeling, explanation, argumentation) 25 and core ideas of 21st-century science which will facilitate developing disciplinary expertise and versatility 26 , 27 .

Here, the scales approach has allowed us to train students from a range of disciplinary backgrounds in common, multi-disciplinary concepts. Teaching students first how to deconstruct technologies along scales and then identify concepts that apply at each scale, allows them to integrate diverse concepts together in the context of how they are used for engineering. While biological emergent behavior lends itself to the scales-based framework, synthetic biology has traditionally been skewed towards the molecular and circuits/network scales. In contrast, bioengineering and biomedical engineering are traditionally skewed towards the cell and biological communities scales. Yet often the goals of synthetic biologists and bio/biomedical engineers are the same: to tackle a global challenge with biological solutions. The scales framework allows for appreciation of all the scales, which we hope encourages researchers to seek out knowledge of traditionally overlooked scales and work across scales to develop impactful biotechnologies.

While we have started to lay the framework for a deconstruction approach to teaching synthetic biology, it is far from complete. As the field evolves, it is our hope that the deconstruction approach evolves with it. We can already see evidence of this through the definition of the scales. For example, in our recent implementation of the course during a deep dive into CRISPR gene drives, students challenged our definition of the biological communities scale and actively discussed whether a new scale should be added to encapsulate concepts relevant to organismal populations such as population genetics. In addition, drawing connections to how different other fields use the scales framework—like computer engineering where technologies are built from transistors, to circuits, to chips, to devices—can further refine its application to synthetic biology and drive additional innovation. For example, the existence of computer-aided design tools that can be used within and across scales to design computer systems is a powerful encapsulation of the scales framework and is a particularly exciting prospect for synthetic biology 106 , 107 . Using this central framework, iterations of this course could be developed that bring in additional discipline-specific concepts, pointing out when in each synthetic biology technology those concepts can be applied. In this way, a student trained in that discipline can learn when and how to collaborate with researchers in other disciplines, addressing the need to learn to integrate and traverse disciplines. We anticipate that continued adoption, discussion, and development of the deconstruction approach will allow the concepts to be refined to match the needs of the field.

We envision the deconstruction approach to be more than just a pedagogical approach to teaching synthetic biology. Rather, we hope that it is viewed as a way of thinking for synthetic biologists of the future. By teaching students to think across scales, we hope that their holistic view of what it takes to make a successful synthetic biology technology will allow them to identify knowledge gaps that can be filled by new learning, new collaborations, or even drive new research to fill those gaps. By placing the societal scale on equal footing with the other scales, we hope to create an ethically minded workforce that will drive responsible innovation. And by emphasizing how many disciplines are needed across scales to achieve success, we hope to welcome diverse perspectives to the field of synthetic biology so we can all work towards solving society’s grand challenges together.

Supporting Information

The following supplemental materials are provided on the Northwestern Arch database ( https://doi.org/10.21985/n2-x989-tb47 ) to aid the adoption and adaption of the scales framework and deconstruction approach to other learning environments:

Northwestern_CSB_Deconstructing_SynBio_Content_Map.pdf – a table outlining how the course content can be delivered across a ten-week course. Modules on environmental health, biochemical production and human health are outlined. A schedule for the provided assignments is given, along with how to integrate guest lectures.

Northwestern_CSB_Deconstructing_SynBio_Syllabus.pdf – example syllabus for the deconstructing synthetic biology course.

Northwestern_CSB_Deconstructing_SynBio_Assignment_1-First_Deconstruction.pdf – the first deconstruction assignment given to students before they have been taught about the scales framework.

Northwestern_CSB_Deconstructing_SynBio_Assignment_2-Second_Deconstruction.pdf – the second deconstruction assignment given to students immediately after they have been taught about the scales framework.

Northwestern_CSB_Deconstructing_SynBio_Assignment_3-Final_Project.pdf – the course final project entailing a deep dive deconstruction using all the principles learned in the course.

Data availability

The full set of deidentified responses used for thematic analysis in Table 1 can be made available upon reasonable request pending ethical consideration of intended use.

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Acknowledgements

We thank all 103 students that participated in this course and our evaluations since 2021 as without them this would not be possible. We would also like to provide many thanks to several colleagues that have provided various forms of feedback on the conceptual framework and this manuscript: Mark Blenner (University of Delaware) for trying out an early version of the course content; Mary Dunlop (BU), Richard Murray (Caltech), Joff Silberg (Rice), Kristala Prather (MIT), Ron Weiss (MIT), and Natalie Kuldell (MIT) who serve on the Synthetic Biology Across Scales (SynBAS) NSF National Research Traineeship (NRT) advisory board who provided critical feedback on the development of the deconstruction approach; and Ron Vale (HHMI) and Tim Mitchison (Harvard) for advice on publishing this work. We would also like to thank Karsten Temme (Pivot Bio), Michael Köpke (LanzaTech), Sam Weiss Evans (Harvard), Weston Kightlinger (Resilience), Jennifer Brophy (Stanford), Khalid Alam (Stemloop), Marilene Pavan (LanzaTech), and Dario Robleto for invaluable contributions made to developing the deconstruction approach through their guest lectures. The development of the deconstruction approach was supported by the National Science Foundation through the SynBAS NRT program (2021900) and by the Bachrach Family Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Author information

Michael C. Jewett

Present address: Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA

Susanna Calkins

Present address: Nexus for Faculty Success, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA

Authors and Affiliations

Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA

Ashty S. Karim, Dylan M. Brown, Chloé M. Archuleta, Sharisse Grannan, Ludmilla Aristilde, Yogesh Goyal, Josh N. Leonard, Niall M. Mangan, Arthur Prindle, Gabriel J. Rocklin, Keith J. Tyo, Laurie Zoloth, Michael C. Jewett, Susanna Calkins, Neha P. Kamat, Danielle Tullman-Ercek & Julius B. Lucks

Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA

Ashty S. Karim, Dylan M. Brown, Chloé M. Archuleta, Josh N. Leonard, Arthur Prindle, Keith J. Tyo, Michael C. Jewett, Neha P. Kamat, Danielle Tullman-Ercek & Julius B. Lucks

Independent Evaluator, Lake Geneva, WI, 53147, USA

Sharisse Grannan

Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, 60208, USA

Ludmilla Aristilde

Department of Cell and Developmental Biology, Northwestern University, Chicago, IL, 60611, USA

Yogesh Goyal

Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA

Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, 60201, USA

Niall M. Mangan

Department of Biochemistry and Molecular Genetics, Northwestern University, Chicago, IL, 60611, USA

Arthur Prindle

Department of Pharmacology, Northwestern University, Chicago, IL, 60611, USA

Gabriel J. Rocklin

The Divinity School, University of Chicago, Chicago, IL, 60637, USA

Laurie Zoloth

Searle Center for Advancing Learning and Teaching, Northwestern University, Evanston, IL, 60208, USA

Biomedical Engineering Northwestern University, Evanston, IL, 60208, USA

Neha P. Kamat

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Contributions

A.S.K.—Devised and developed the deconstruction concept, developed and taught the course, wrote the manuscript, made figures, and edited the manuscript. D.M.B.—Developed the course, wrote the manuscript, made figures, and edited the manuscript. C.M.A.—Developed the course and edited the manuscript. S.G.—Developed the course evaluation approach, performed course evaluation, collected, and analyzed evaluation data, and edited the manuscript. L.A.—Developed the deconstruction concept and edited the manuscript. Y.G.—Developed the deconstruction concept and edited the manuscript. J. N. L.—Devised and developed the deconstruction concept and edited the manuscript. N.M.M.—Developed the deconstruction concept and edited the manuscript. A.P.—Developed the deconstruction concept and edited the manuscript. G.J.R.—Developed the deconstruction concept and edited the manuscript. K.J.T.—Devised and developed the deconstruction concept and edited the manuscript. L.Z.—Developed the deconstruction concept, developed the approach to integrating ethics into the course, and edited the manuscript. M.C.J.—Devised and developed the deconstruction concept and edited the manuscript. S.C.—Developed the deconstruction concept, developed the course evaluation approach, performed course evaluation, collected and analyzed evaluation data, and edited the manuscript. N.P.K. – Devised and developed the deconstruction concept and edited the manuscript. D.T.E.—Devised and developed the deconstruction concept and edited the manuscript. J.B.L.—Devised and developed the deconstruction concept, developed and taught the course, wrote the manuscript, and edited the manuscript.

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Karim, A.S., Brown, D.M., Archuleta, C.M. et al. Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists. Nat Commun 15 , 5425 (2024). https://doi.org/10.1038/s41467-024-49626-x

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DOI : https://doi.org/10.1038/s41467-024-49626-x

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Powering the future: an integrated framework for clean renewable energy transition.

1. Introduction

2. background, 2.1. clean renewable energy community transition dynamics, 2.2. role of dimensions, indicators, and metrics in energy transition, 3. methodology, 3.1. literature review, 3.2. conceptual framework, 3.2.1. efficient built environment, 3.2.2. reliable energy system, 3.2.3. accessible energy system, 4. review of renewable energy transition metrics, 4.1. environmental dimension metrics, 4.2. technical dimension metrics, 4.3. social dimension metrics, 4.4. economic dimension metrics, 4.5. political and institutional dimension metrics, 5. discussion, 5.1. challenges associated with metrics identification, 5.2. evaluating metrics for clean renewable energy communities transition.

High and Easy are assigned a value of 3, reflecting optimal conditions or the highest degree of relevance or ease of application.
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Hard and Data Availability Varies are scored as 1, denoting challenging conditions or inconsistent data availability.

5.3. Classification of Metrics Based on Clean Renewable Energy Communities Transition Objectives

6. future research and limitations, 7. conclusions, supplementary materials, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Term	Definition
Dimension	A factor that affects or is affected by the transition from fossil fuels to clean renewable energy sources. The dimensions are environmental, social, technical, economic, and political and institutional.
Indicator	Quantitative or qualitative measurement or value that describes the current or forecasted trend of sustainability dimensions and objectives.
Metric	A way to measure the progress and impact of the transition from fossil fuels to low-carbon renewable sources, including combinations of one or more methods, and a value that reflects changes in energy supply, demand, efficiency, reliability, emissions, and economics over time.

Sustainable Dimensions	Description
Environmental	Deals with ecological health, biodiversity, and climate resilience.
Technical	Focuses on infrastructure, technology, and resource efficiency.
Social	Addresses community well-being, equity, and quality of life.
Economic	Considers economic viability, job creation, and affordability.
Political and Institutional	Involves governance, policies, and stakeholder engagement.

Dimensions	Indicators	Metrics	Definition	References
Environmental	GHG Emission	Total Emissions	The total emission quantifies the direct and indirect emissions of energy.	[ ]
	GHG Emission	Carbon Intensity	The amount of greenhouse gases emitted per unit of energy produced.	[ , , , ]
	Waste Generated	Waste Footprint Component	The quantity of waste generated during energy production and consumption activities.	[ ]
	Water Consumption	Water Footprint Component	The amount of water used in energy production processes is often expressed as a water footprint.	[ ]
	Natural Resources	Natural Resource Depletion or Abiotic Depletion	Used to assess the impact of resource depletion in life cycle assessment.	[ ]
	Land Use	Land Use Energy Intensity	The energy required to transform land for energy production is often measured per unit area.	[ ]
		Absolute Area of Land converted	The total land area required to supply energy needs and offset carbon emissions.	[ ]
		Annual Land Transformation	The extent of land converted for energy production purposes on an annual basis.	[ ]
		Lifetime Land Transformation	The duration over which transformed land returns to its original state after energy use.	[ ]
		Land-Use Efficiency	The capacity of energy in land area occupied.	[ ]
		Energy Footprint	It is the land needed to supply energy and land needed to offset CO by plantation.	[ ]
		Land Occupation Metric	The area of transformed land and the time needed for full recovery to its original state.	[ ]
	Ecological Footprint	Carbon Sequestration	The global biological system affects the world’s carbon cycle through biological processes.	[ ]

Dimensions	Indicators	Metrics	Definition	References
Technical	Renewable Energy Share	Renewable Energy Fraction	The percentage of energy derived from renewable sources compared to total energy consumption.	[ ]
	System Generation	Residual Load Range	The expected number of hours per year when system demand exceeds generating capacity.	[ ]
		Surplus Energy	The expected number of days per year when available generation exceeds daily peak demand.	[ ]
		Power System Flexibility	The system’s power ability to cope with uncertainty and not affect reliability and economy.	[ ]
		Insufficient Ramping Resource Expectation (IRRE)	A metric used to measure the system flexibility for long-term planning.	[ ]
	System Efficiency	Energy Efficiency	The average efficiency of energy conversion and utilization processes within the system.	[ ]
		Total Final Consumption (TFC)	The consumption of energy carriers such as solid, liquid, or gaseous fuels and electricity to fulfill this service demand.	[ ]
		Total Primary Energy (TPE)	The primary energy required to produce these energy carriers.	[ ]
		Loss of Power Supply (LPSP) Probability	The metric is used to assess system reliability by measuring the risk of inadequate power supply to load requirement.	[ ]
		Energy Intensity	The total final renewable energy consumption per unit of economic output.	[ ]
	System Security	Full Load Hours of Generation	The time needed for a power plant to operate at full capacity to produce a certain amount of energy.	[ ]
	System Performance	Net Energy Ratio (NER)	Measures the ratio of total energy output to total energy input of the system.	[ ]
	Adequacy	Loss of Load Hours (LOLH)	The expected number of hours per year when system demand exceeds generating capacity.	[ ]
		Loss of Load Expectancy	The average frequency of power supply interruptions.	[ ]
		Loss of Load Probability	The probability of system peak or hourly demand exceeding generating capacity.	[ ]
		Loss of Load Events	The number of events where system load is not served due to capacity deficiency in a year.	[ ]
	Reliability	Expected Unserved Energy (EUE)	The expected total energy not supplied to any load buses, regardless of cause or location.	[ ]
		Expected Energy Not Supplied	The expected total energy not supplied to any load buses, regardless of cause or location.	[ ]
		Energy Index of Unreliability (EIU)	The expected total energy not supplied divided by the total energy demand.	[ ]
		Energy Index of Reliability (EIR)	The ratio of the total energy supplied to the total energy demand.	[ ]
		System Minutes	The total duration of system-wide interruptions in energy supply over a specific period.	[ ]
		Average Interruption Time (AIT)	The average duration of system-wide interruptions in energy supply over a specified period.	[ ]

Dimensions	Indicators	Metrics	Definition	References
Social	Equitable	Changes in Energy Expenditures	Percentage of household income spent on energy bills, indicating the affordability of energy.	[ ]
	Secure	Energy Burden	The percentage of household income spent on energy bills.	[ ]
	Accessible	Energy Access	The availability and affordability of energy services to meet basic needs, such as lighting, cooking, heating, cooling, etc.	[ ]
	Acceptable	Community Acceptance	The level of public support for and acceptance of renewable energy projects in local communities.	[ , ]
	Health Impacts and Pollutant Exposure	Occupational Pollutant Concentration	The concentration of pollutants in workplaces associated with energy production activities.	[ ]
	Health Impacts and Pollutant Exposure	Proximity to Resource Extraction	Distance from residential areas to resource extraction sites, indicating environmental impact.	[ ]

Dimensions	Indicators	Metrics	Definition	References
Economic	Energy Affordability	Levelized Cost of Energy (LCOE)	The average cost of energy production over the lifetime of a project, excluding subsidies.	[ , ]
	Energy Affordability	Cost of Valued Energy (COVE)	Improved valuation metric that accounts for time-dependent electricity prices.	[ ]
	Resource Cost	Real Gross Domestic Product (RGDP)	The total value of goods and services produced within a country, adjusted for inflation.	[ ]
	Employment	Jobs Created per Installed Capacity	The number of jobs created by renewable energy projects measured based on the energy capacity, including direct, indirect, and induced jobs.	[ ]
	Financial Viability Over Time	Energy Payback Time (EPBT)	Time required to generate the same amount of energy that has been invested into the system over the entire lifecycle as primary energy.	[ ]
		Energy Return on Energy Investment (EROI)	The ratio of energy delivered by an energy source to the energy required to extract it.	[ ]
		Total Net Present Cost	It assesses the component costs over a lifetime.	[ ]
	Cost Effectiveness	Cost per Unit of Energy Saved	The cost of implementing a renewable energy project divided by the amount of energy saved.	[ ]

Dimensions	Indicators	Metrics	Definition	References
Political and Institutional	Participation	Public Participation in Energy Planning	The involvement and influence of stakeholders, such as consumers, communities, civil society, etc., in energy planning and management.	[ , ]
	Policy Support	Renewable Energy Policies	The presence and effectiveness of policies that support renewable energy development, such as feed-in tariffs, tax incentives, etc.	[ , , ]
	Policy Support	Regulatory Certainty	The stability and predictability of the regulatory environment for renewable energy projects.	[ , ]
	Institutional Capacity	Institutional Capacity for Renewable Energy	The ability of institutions to plan, implement, and manage renewable energy projects.	[ , ]

Attributes	Definition
Relevance	It must be associated with one or more of the dimensions of the framework. It must reflect at least one of the indicators.
Ease of application	It has a clear tool, methodology, or approach to measure energy transition performance.
Input data availability and quality	The required input is clear. Input data are accessible through a clear approach. The data are accurate, complete, and reliable.
Reliable	The output results can be interpreted. Ability of the output data to reflect desired objectives. The metric provides accurate and truthful output.
Comparable	Can be tracked over time. Allows changes or differences in the phenomenon being measured to be detected.

Objectives	Aspects	Description
Efficiency	Operational Efficiency	Refers to optimizing processes, minimizing waste, and achieving maximum output while considering social, economic, and environmental aspects.
	Resource Efficiency	Focuses on using resources (land, energy, materials, financial resources, etc.) effectively to transition communities to clean renewable energy.
	Productivity	Indicates how efficiently resources, including land and energy potential, are transformed into valuable outputs.
Reliability	Dependability	Reflects the reliability and predictability of energy services.
Reliability	Continuity	Addresses uninterrupted energy supply and consistent performance.
Accessibility	Equitable Access	Highlights fair and inclusive availability of energy services for all, regardless of socioeconomic factors, through energy distribution and policy development that facilitates and supports energy transition.
Accessibility	Affordability	Considers the financial accessibility of energy services.

Metric/Objectives	Carbon Intensity	Waste Footprint Component	Land Use Energy Intensity	Land Use Efficiency	Renewable Energy fraction	Residual Load Range	Energy Efficiency	Total Primary Energy	Loss of Power Supply Probability	Full Load Hours of Generation	Net Energy Ratio	Expected Unserved Energy	Energy Access	Occupational Pollutant Concentration	Cost of Valued Energy	Energy Return on Energy Investment	Cost per Unit of Energy Saved	Renewable Energy Policies
Efficiency	X	X	X	X	X		X								X	X	X
Reliability					X	X	X	X	X	X	X	X
Accessibility					X						X	X	X	X	X		X	X

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Wehbi, H. Powering the Future: An Integrated Framework for Clean Renewable Energy Transition. Sustainability 2024 , 16 , 5594. https://doi.org/10.3390/su16135594

Wehbi H. Powering the Future: An Integrated Framework for Clean Renewable Energy Transition. Sustainability . 2024; 16(13):5594. https://doi.org/10.3390/su16135594

Wehbi, Hanan. 2024. "Powering the Future: An Integrated Framework for Clean Renewable Energy Transition" Sustainability 16, no. 13: 5594. https://doi.org/10.3390/su16135594

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Mapping km-scale global extreme rainfall onto mesoscale convective systems lifecycle, frequency and dynamics

Fildier, Benjamin
Carenso, Maxime
Fiolleau, Thomas

Mesoscale convective systems are the building block of tropical precipitation, as more than 40% of global precipitation and more than 80% of extreme rainrates are produced by these organized systems. However, when investigating the sensitivity of global rain extremes, the behavior and morphology of organized storm systems are typically ignored and corresponding dynamics are instead interpreted using the textbook framework of a convecting parcel. Indeed, despite rich observational and case studies describing the internal dynamics and structures of MCSs, no conceptual framework exist to this day to bridge the gap between global hydrologic sensitivity and MCS behavior.This work introduces new approaches to link extreme precipitation rates in the tropics to the occurrence, internal dynamics and lifecycle of individual MCSs. Individual storms are idenditifed based on by the Lagrangian tracking algorithm TOOCAN which tracks storm anvils over their lifecycle, and which has been applied to satellite observations and to global storm resolving models in the DYAMOND experiment. We first use this rich dataset to develop a numerical interface that maps the occurrence of extreme precipitation rate onto the MCS cloud shield. We then introduce a novel conceptual framework to decompose the sensitivity of precipitation extremes to the change in storm occurrence and change in internal dynamics within this cloud shield. Results are threefold. We demonstrate a robust phasing in the timing of global extreme rainrates within the storm lifecycle, robustly occurring at 25-30% of the storm's lifetime for the models and regions analyzed. The analytical decomposition confirms that in a given climate state, variability in the heaviest rainrates across regions mostly occur through changes in MCS frequency, rather than changes in their efficiency at producing rain. We finally argue that the sensitivity of extremes to climate state may occur through both a change in occurrence and a change in internal MCS dynamics.

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Regional, rural and urban development
The OECD helps all subnational regions to become more equitable, inclusive and resilient by prioritising well-being. Our work addresses global trends like climate change, digitalisation, migration, jobs and demographic shifts by applying a local lens. Using a place-based approach and OECD regional data, we help improve lives for people, places and firms at the local level.
Mapping km-scale global extreme rainfall onto mesoscale convective
Indeed, despite rich observational and case studies describing the internal dynamics and structures of MCSs, no conceptual framework exist to this day to bridge the gap between global hydrologic sensitivity and MCS behavior.This work introduces new approaches to link extreme precipitation rates in the tropics to the occurrence, internal ...

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Moderator vs. mediator

Control variables

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How to Make a Conceptual Framework

What Is In a Conceptual Framework?

Steps to Developing the Perfect Conceptual Framework

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2. Conduct a Literature Review

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Independent Variables

Dependent Variables

Control Variables

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Intervening Variables

Extraneous Variables

4. Create Your Conceptual Framework

Are Conceptual Frameworks and Research Paradigms the Same?

Where is the Conceptual Framework Located in a Thesis?

Can a Conceptual Framework be Used in a Qualitative Study?

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What Is a Conceptual Framework? | Tips & Examples

Table of contents

Moderating variables

Mediating variables

Moderator vs mediator

Control variables

Cite this Scribbr article