Case Studies for eLearning: How and when they work best
Case studies in education are an age old teaching strategy. They provide meaningful, content-related experiences through which learners can discover and imagine abstract principles in real world settings. In this article, we talk about effective approaches to using case studies in eLearning environments.
Case studies make excellent reading and comprehension activities, while simultaneously serving as information providing tools. These are discovery activities for learners that focus your learners’ analytical and problem-solving skills on the scenario presented in the case study. They are also a great way to demonstrate a real incident or an event that conveys a crucial lesson for best practices. Through this, learners connect intimately and directly with the industry they are training for. In a nutshell, case studies are their first line of contact with the future work environment .
Moreover, case studies make great content for an eLearning interface: think about an eLearning screen with tabs like “About”, “Synopsis”, “Events”, “References”, “Assignment”. Each page includes text and multimedia for learners to tinker and play with. The idea is to display the case in a fun and explorative fashion. This is as opposed to a simple pdf file with lines and lines of text that becomes harder to read with each page!
The goal of a case study is to relay relevant learning materials and then ask questions based on the reading. In eLearning, case studies are richer. The material has more variety, as it is linked from the current resources available on the Internet.
The eLearning medium also provides an interactive environment for case studies with plenty of room for collaboration and online discussion. A complex case study can be simplified using eLearning tools and also become more engaging than the traditional case study delivery method.
The content of your case study could even be retrieved, with the appropriate citations, from a local newspaper. Has there been an article or a news story on some interesting business or an incident? Is it related to your learning objective? Use it as a case study.
Case studies are great for teaching complex knowledge that cannot be taught by using simple formulas. They are especially good for teaching judgement skills and decision making skills required when dealing with real-world dilemmas. They can also be presented in multiple formats. For example, in an instructor-led case study, the case is explained by the instructor and so are the assignments related to it.
Virtual field trips are also great examples of eLearning case studies . Mini-case studies, or vignettes, presented in the beginning of the chapters also serve as great classroom-to-outside-world connection tools. Some case studies can be utilized to develop “Reaction Papers”, in which learners create a summary and reaction to the events they read in the case study.
Simple Strategies to Provide Case Studies
So what are the best practices for case studies? In an eLearning environment, case studies are a rich mixture of multimedia. In the following cases, using a scenario-based approach will work well:
1. When you can create a simulation of an actual system or have extensive video content on it. Find documentaries related to the content, in the form of YouTube videos. Additionally, create an interactive screen with buttons and dialogues between actors to simulate a scene in the case study.
2. Explain charts, diagrams and other technical and business graphics using case studies.
3. Give some life to your numerical data on spreadsheets by narrating a story in the form of a case study.
4. When explaining blueprints, drawings and specifications of products and systems.
5. When demonstrating conventional business documents such as reports, contracts, instruction manuals, email messages, memos and letters.
When using case studies in your eLearning , provide prompts to learners to prepare them that they are about to read a real case. Provide specific guidance and facts to understand the case better. Explain how the case relates to the learning objective or the topic in the eLearning course . What are the important features of the case? What should the learners focus on? Provide sufficient clues on where to start the examination of the case. Also, provide questions in the end of the case to help with brainstorming and critical thinking.
Using case studies is an excellent teaching strategy. Case studies are good for teaching complex knowledge. They promote observing and reflecting on new knowledge and experiences.
Try to use case studies that are based on the local work context of your learners. Learners should be able to relate to the experiences narrated in the case. For your global learners, try to include case studies based on international businesses.
Case studies are also recommended for teaching judgement skills necessary to deal with complex and contradictory situations common in the real world setting.
If you are looking for a way to connect your eLearning course to the outside world, simply integrate a case study.
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5 Elearning Case Studies
July 26, 2022
Darcy Dario
Take your training to the next level by reading up on some elearning case studies. The results of these studies speak for themselves, and they can also work for you and your teams. Below is a list of some elearning case studies that have seen increases in knowledge retention, participation rates, and completion rates.
1. Tennis Australia & SC Training (formerly EdApp)
Tennis Australia used elearning to train over 50 part-time and casual staff for the 2022 Australian Open. Preparing for one of the largest sports tournaments in Australia is no easy task. Ball kids aged 12 to 15 have other responsibilities like school or part-time jobs. Gathering over 380 ball kids for training and preparing training courses is a challenge, but it was possible thanks to SC Training (formerly EdApp)’s cross-platform training approach.
Tennis Australia handed in its existing training material to SC Training (formerly EdApp), and SC Training (formerly EdApp)’s team of course designers created different types of engaging courses in days. Training was distributed through invite links and QR codes. All Tennis Australia had to do was either print off a generated dynamic QR code to hang up in a locker room or back office, or send an invite link by SMS text or email. This allowed team members to start their training anytime and anywhere, whenever they had five minutes to spare.
When training can be accessed in bite-sized formats on smartphones, not only is it convenient but it also puts the team members in a better mindset to learn and retain their training. SC Training (formerly EdApp) also used game-based lesson slides to make them feel like mini-games rather than training slides. Tennis Australia teams were able to just swipe, drag and drop, and match their way through lessons, leading to higher completion rates.
- Case Study: Tennis Australia
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*Up to 10 users.
2. UNITAR & SC Training (formerly EdApp)
The United Nations Institute for Training and Research (UNITAR) incorporated SC Training (formerly EdApp)’s microlearning strategy to educate learners across the globe. Working with SC Training (formerly EdApp)’s mission to democratize learning, UNITAR expanded its reach with elearning by giving microlessons to developing and vulnerable communities in places like Afghanistan, Iraq, and across the Middle East and Africa.
UNITAR delivered courses on some incredible topics like gender inclusivity , peace, women’s entrepreneurship, Sustainable Development Goals, and more. All of these courses can be found in SC Training (formerly EdApp)’s course library for free.
3. Pandora & SC Training (formerly EdApp)
Pandora saw completion rates of over 80% with SC Training (formerly EdApp), and 90% of their employees preferred this tool over their existing elearning management system at the time. Their employees found SC Training (formerly EdApp)’s mobile learning or m learning approach to online training to be suitable for their daily routines compared to other learning environments. They realized that they were always on their phones, so it almost became a daily routine to check out SC Training (formerly EdApp) on their phones after scrolling through social media sites like Twitter or Facebook.
The ability to earn badges and banners also served as a great motivator for everyone to complete their compliance training. With the application of gamification features like leaderboards (by request only), Pandora managed to encourage their learners to continually learn as they work with them. Pandora’s sales associates also said they felt more confident in their retail and sales skills because any practical tips or techniques can be found in their pockets on SC Training (formerly EdApp)’s mobile app.
4. Ryman Healthcare & SC Training (formerly EdApp)
Thanks to SC Training (formerly EdApp), Ryman Healthcare was able to move from paper training to smartphone training in less than a month at an enterprise level. Elearning and mobile training helped improve knowledge retention and compliance for Ryman teams. In fact, Ryman Healthcare increased their teams’ knowledge retention by 90% and their participation rate by 100%. Approximately 93% of their workforce also found that they learned something new in their mobile training compared to their old corporate training format.
With SC Training (formerly EdApp), Ryman Healthcare was able to centralize its training and provide an unparalleled and innovative experience to over 5,000 of its team members. Ryman Healthcare created highly relevant and branded courses with beautiful instructional design about core practices such as medication, sanitation, safety regulations, and food service in a matter of days. Their admins were also able to respond to changes with real-time edits through SC Training (formerly EdApp)’s easy-to-use authoring tool, and training was scaled and updated as quickly as possible.
To ensure the high quality of care in their mission, Ryman Healthcare deployed quizzes to reinforce key information. These quizzes were created with SC Training (formerly EdApp)’s built-in quiz maker, Rapid Refresh. All quiz results were also tracked, analyzed, and organized in SC Training (formerly EdApp)’s analytics dashboards.
- Case Study: Ryman Healthcare
5. Marley Spoon & SC Training (formerly EdApp)
The last elearning case study is how Marley Spoon used SC Training (formerly EdApp)’s microlearning tools to onboard hundreds of new employees during their unprecedented growth brought on by COVID-19. Marley Spoon found that employee training through instructor-led training in classrooms was no longer a viable option for them, so they needed a solution that can handle a large number of team members who are divided into various sub-departments.
Using SC Training (formerly EdApp)’s features like Sites & Groups has empowered Marley Spoon to easily create and share content with these unique groups. SC Training (formerly EdApp) also allowed the delivery of vital information through elearning courses like food safety and procedures, all while representing Marley Spoon’s strong brand identity.
Food businesses have to work and grow in very fast-paced environments, so having a product like SC Training (formerly EdApp) that can be used for distance-learning to keep up with those changes was extremely valuable to Marley Spoon.
Darcy is a learning expert at SC Training (formerly EdApp), a mobile-based training platform that helps businesses bring their training solutions to the next level with democratized learning. She has a background in content writing and specializes in eLearning and global communications. When she’s not writing SEO-optimized content, she’s trying to finish her video game backlog.
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Table of Contents
Elearning case study: going to the next level.
Wayne was looking to take his eLearning design to the next level – instead of a series of PowerPoint-like slides that learners click through followed by a quiz at the end, he wanted something more engaging and effective. Part 1 of this eLearning case study includes the background and choices that were made. Part 2 of this eLearning case study features advice from eLearning and instructional design professionals.
Part 1: eLearning Case Study
The challenge :.
Wayne had been working as the learning manager for a small firm specializing in online advertising and social media marketing for about four years. Prior to that, he led a team of online advertising and social media specialists for six years. He has deep knowledge of the industry and made it a point to continue to stay on top of industry trends.
Since its inception, the firm had emphasized a culture of learning that included in-person and online training. Coinciding with Wayne’s transition into the learning manager role, the firm made a major investment in online learning courses in order to better meet the on-demand training needs of staff. After implementing a new learning management system (LMS), which was initially populated with a series of off-the-shelf courses on sales, customer service and various recordings of webinars that had been delivered to clients, Wayne quickly added a series of documents and job aids that could be used by staff when they were in the home office as well as when they were in meetings with clients.
Seeking to take full advantage of the technology available, Wayne began using Camtasia to create 5-10 minute eLearning segments focused on various product features and frequently asked questions. In general, these eLearning components consisted of PowerPoint-like presentations that learners would click through in order to orient themselves to various product features. Wayne included voiceover to make the presentations more engaging and included a 10-question quiz at the end of each segment to ensure learners could correctly answer basic questions about the content. After churning out 25 of these segments, Wayne still felt something was missing from these online courses.
The Solution :
In this year’s training budget, Wayne included $50,000 in order to consult with an eLearning company on how to better create efficient and effective eLearning programs for staff. After several meetings with the eLearning company, Wayne decided to use the money budgeted to invest in new eLearning modules for additional topics.
Together with the eLearning company, Wayne agreed that the money would best be allocated to create and develop 10 new modules over the next four months. These new 5-10 minute modules would include professional graphic design and more interactive components where content would be integrated into true/false, multiple choice and matching activities. Going forward, Wayne also agreed that future modules could benefit by including short video clips, in addition to solely featuring text on the screen.
The Results :
Wayne was impressed with the project management abilities of the eLearning company. They were easy to work with, asked some questions that vastly improved the content and delivery, and completed all 10 modules within the originally estimated 4-month time period.
While post-module evaluation surveys included some grumbles from staff who just wanted to be able to read through the information and be done with the module, overall the feedback was very positive and enthusiastic. One social media specialist commented that the new modules were “light years better than the other PowerPoint-style modules.” When asked what could be improved, one online advertising specialist suggested “to make these modules accessible by smartphone as well.”
Part 2: What eLearning Experts Say:
Definitely mix it up.
“Including multimedia as part of eLearning works to ensure students remain engaged in the process. Whether it is video or interactive games and presentations, adding even a small number of these activities helps to vary the educational rhythm for the student. Integrating a story as a unifying thread is also an important part of ensuring students retain information.”
– Michel Hansmire , Principal, Sparkworks Media
But what about more complex training needs?
“Wayne should address some of the more nuanced subjects such as sales techniques , dealing with difficult people, and complex budget management. Wayne can take advantage of his budget allocation to work with a professional eLearning company in order to create scenario-based eLearning, grounded in the real world. By putting a case study into a realistic context, Wayne can build courses that assess a learner’s ability to solve real-world problems —and isn’t that what it’s really all about? Check out Cathy Moore’s SlideShare presentation if you’d like to learn more about how to build courses that include real-world context.”
– Kirby Crider , Sr. Instructional Designer, Windwalker Corporation
Go Gamified and Make it Fun (because life is too short for boring eLearning!)
“Wayne’s next step should be to think more audaciously about how to get learners to absolutely LOVE their learning. He should be thinking about how he can get the learners to look forward to every new course he publishes in the same way they would the next big blockbuster film. That way he can get a better ROI for his company, at the same time build himself L&D rock star status! He needs to think more about how he will improve user engagement first, not what subjects he will teach or what tool he will use.
Research shows that learners involve themselves more with gamified learning and LMS features than other types of training. In fact, they spend 50% longer on an LMS with gamification features, and in the world of eLearning, gamification increases participation, such that staff experiencing gamified training are 86% more active than non-gamified training.
The fact is that employees training on a gamified LMS, deploying game-based eLearning acquire more factual knowledge, attain a higher skill level and retain information for longer.”
– Juliette Denny, Managing Director, Growth Engineering
Do you have an eLearning case study that you want to get expert opinions on? Contact us or let us know in the comments.
Brian Washburn
Brian has over 25 years of experience in Learning & Development including the last 7 as CEO of Endurance Learning.
Brian is always available to chat about learning & development and to talk about whether Endurance Learning can be your training team’s “extra set of hands”.
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An analysis of students' perspectives on e-learning participation – the case of COVID-19 pandemic
International Journal of Information and Learning Technology
ISSN : 2056-4880
Article publication date: 17 May 2021
Issue publication date: 24 June 2021
During the COVID-19 pandemic, educational institutions were forced to shut down, causing massive disruption of the education system. This paper aims to determine the critical factors for the intention to participate in e-learning during COVID-19.
Design/methodology/approach
Data were collected by surveying 131 university students and structural equation modelling technique using PLS-SEM was employed to analysis the data.
The results showed that the COVID-19 related factors such as perceived challenges and COVID-19 awareness not only directly impact students' intention but also such effects are mediated through perceived usefulness and perceived ease of use of e-learning systems. However, the results showed that the educational institution's preparedness does not directly impact the intention of students to participate in e-learning during COVID-19. The results also showed that the gender and length of the use of e-learning systems impact students' e-learning systems use.
Originality/value
These results demonstrated that, regardless of how well the educational institutions are prepared to promote the use of e-learning systems, other COVID-19-related challenges play a crucial role in forming the intention of students to participate in e-learning during the COVID-19 pandemic. Theoretical and practical implications are provided.
- Distance learning
- Higher education
- Online education
Nikou, S. and Maslov, I. (2021), "An analysis of students' perspectives on e-learning participation – the case of COVID-19 pandemic", International Journal of Information and Learning Technology , Vol. 38 No. 3, pp. 299-315. https://doi.org/10.1108/IJILT-12-2020-0220
Emerald Publishing Limited
Copyright © 2021, Shahrokh Nikou and Ilia Maslov
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The COVID-19 pandemic is the defining global health crisis of our time, and it is adding a fair amount of complexity in how different activities are being conducted ( Adnan and Anwar, 2020 ). Such effects are crucial on higher education, forcing all teaching and learning activities to face a sudden transition to wholly online learning contexts ( Toquero, 2020 ). While the educational environments are still struggling with the digitalisation and digital transformation challenges and finding optimal ways to adapt, the Coronavirus pandemic has fundamentally affected their core: staff and students ( Adedoyin and Soykan, 2020 ; Aristovnik et al. , 2020 ; Strauß and Rummel, 2020 ). For them, the period is inevitably very stressful as all learning and teaching activities – e.g. all classes, meetings, seminars, supervisions and exams were forced to move online within short notice ( Bao, 2020 ; Hodges et al. , 2020 ). Though such transformation is not entirely new for such institutions, they are all now forced to move away from traditional teaching and learning structures to a virtual environment as old education models are no longer adaptable to the challenges of rapidly changing educational environments ( Van Nuland et al. , 2020 ).
In the educational environments, information and communications technology (ICT) has been extensively used to deliver information for education and learning, and e-learning has been an emerging paradigm of modern education ( Sun et al. , 2008 ). E-learning relies on the use of multiple information systems, services and technologies. Information system encompasses information service and information technology (IT), where service is understood as the use of IT. Furthermore, the user experience (UX) and usability of information technology and services also affect e-learning process, not only the technical aspects, but also the social aspects ( Nakamura et al. , 2017 ). Given the relatively recent events in terms of COVID-19 and quarantine situation worldwide, e-learning has become increasingly important as one of the optimal solutions for education ( Radha et al. , 2020 ). We argue that in order to understand better factors influencing individual decision to participate in e-learning in a worldwide quarantine situation, comprehensive research with a holistic approach is needed. Hence, we aim to address this issue by assessing students' experience in their participation in e-learning. Based on this aim, the research question guides this study is What antecedent factors impact students ' intention to participate in e-learning during the COVID-19 quarantine? To answer the stated research question, we develop an integrated theoretical model that encompasses several antecedent factors (perceived challenges during COVID-19, school and teachers' perceived preparedness) and constructs from Technology Acceptance Model (TAM: Davis, 1989 ), perceived usefulness and perceived ease of use ( Yu, 2020 ). We conduct empirical research and collect data through an online survey questionnaire, focusing on university students as the target group. The data will be analysed through structural equation modelling (SEM) using SmartPLS v. 3.
The rest of this paper is structured as follows: Section 2 presents the literature review with the operationalisation of the required terminology and theoretical framework for the study. Section 3 provides the theoretical framework and hypotheses. Section 4 describes the methodology, research design, and data collection. Section 5 provides the results followed by Section 6 , providing discussions. Section 7 concludes the research and outlines the limitations and recommendations for future research.
2. Literature review
2.1 e-learning and participation in e-learning concepts.
To support e-learning, learning management systems (LMS) is increasingly being used, which are e-learning software that can be used to empower teachers to enrich students' learning ( Bansode and Kumbhar, 2012 , p. 415). LMS is a powerful software system enhancing learning and provides automated delivery of the course content and tracking of the learning progress of the students ( Dalsgaard, 2006 ). Sun et al. (2008 , p. 1183) defined e-learning as the use of telecommunication to deliver information for education and training. Garrison and Anderson (2003) defined e-learning participation as teaching and learning facilitated and supported by Internet technologies. In this research, e-learning is defined as the overall technological system to deliver teaching, whereas participation in e-learning is the act of use of telecommunication to deliver teaching and learning within such a system. Khan (2004) defined e-learning as an iterative process that goes from the planning stage through design, production and evaluation to delivery and maintenance stages. However, there are both advantages and disadvantages to e-learning. On a positive side, e-learning allows for a learner-centred, self-paced, cost-effective way of learning and on a negative side, there is a lack of social interactions, higher degrees of frustration and confusion, with higher preparation time for instructors ( Zhang et al. , 2012 ).
Sun et al. (2008) stated that personal perceptions about e-learning could influence attitudes and impact whether a user would intend to use to e-learning in the future. Uppal et al. (2018) and Kim and Frick (2011) mentioned that the supportiveness of the service, information quality and system quality are different aspects of e-learning quality, which could also impact the decision of the users. Moreover, Benigno and Trentin (2000) stated that e-learning is potentially affected by factors such as student characteristics, student-student interaction, learning materials, learning environment, and information technology (IT). Also, Selim (2007) mentioned that there are eight critical success factors of participation in e-learning (e.g. instructor’s attitude towards and control of the technology and student motivation and technical competency). Furthermore, Sun et al. (2008) suggested that perceived e-learning satisfaction is depended on the six dimensions: learner, instructor, course, technology, design and environmental. Sun et al. (2008) concluded that learner computer anxiety, instructor attitude toward e-learning, e-learning course flexibility, e-learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments were the critical factors affecting learner's perceived satisfaction.
Garavan et al. (2010) conceptualised participation in e-learning and quantitatively validated the research model. In their model, the participation in e-learning is formed by the general-person characteristics (e.g. age and social class), motivation to learn and instructional design characteristics of e-learning (content quality and learner support, feedback and recognition). Additionally, the perceived barriers and enablers to e-learning are potentially affected by the proper instructional design of e-learning. Fleming et al. (2017) identified that predictors of future use and overall satisfaction from using e-learning are low perceived complexity of the e-learning system, the knowledge of e-learning, and available technical support for e-learning. Zhang et al. (2012) presented a research model that evaluates the impact of multiple factors on the intention to continue participation in the e-learning systems. Zhang et al. (2012) concluded that the intention to participate depends directly and indirectly on the psychological safety communication climate, on the perceived responsiveness of e-learning system and self-efficacy, as well as satisfaction from the previous use of the system. Furthermore, satisfaction and membership of the community were found to affect the intention to continue participation in e-learning.
2.2 Blended learning: boundaries between physical and virtual
Hrastinski (2008) stated that e-learning participation does not only occur online but also takes place offline. This is mainly due to the fact that e-learning requires time and energy to learn, communication, thinking and assessing what learners have obtained from e-learning communities in more traditional learning settings. Literature on e-learning is primarily on the so-called blended learning of physical and digital learning and Anthony et al. (2020) stated that blended learning (BL) has been increasing in popularity and demand. However, recent literature on the issue seems to be dominated with the factors of educator presence in online settings, interactions between students, teachers and content, and designed connections between online and offline activities as well as between campus-related and practice-related activities.
Wilson (2009 , p. 20) stated that “learning space continuum has two types of conditions at its extremities, wholly independent self-directed unstructured learning at one end and structured teacher-led didactic learning environments at the other”. Furthermore, Wilson (2009) identified different places for learning spectrums, ranging from unstructured that corresponds to home, bar, cafe or gym to lecture theatre and seminar places for holding workshops. The notion of learning space continuum may become necessary when we take into consideration e-learning. As Ellis and Goodyear (2016 , p. 150) identified, the “boundaries” between the physical and the virtual are become less transparent and more permeable, in addition to the greater need of students of being capable of using digital technologies to discover and construct knowledge that is meaningful to them.
Hence, we argue that e-learning participation cannot be defined narrowly as a specific activity in a specific context, but rather a range of activities, some of which may be even blended with the physical (more traditional) learning and interaction with teachers or other students in a more structured or unstructured manner. This could have a significant impact on the way not only e-learning, but the overall learning process is structured, including how the different technologies are used, how the instructional learning programs are structured, what are the social interrelationships between the students, instructors, organisations, and how the success of learning is measured.
2.3 COVID-19, quarantine and e-learning
Kaplan et al. (2020) stated that a third of the global population worldwide was on a quarantine lockdown in order to limit the spread of the COVID-19. This action led to the social distancing and thus fewer social connections, which also included closures of commercial enterprises and higher educations, resulting in limited physical presence and social interactions between the people. The impact of COVID-19 is also seen in the educational environments, with a potential to experience unparalleled transformations, just as many other human spheres of behaviour, which are facilitated by the advents in the development of IT, such as 5G ( Kaplan et al. , 2020 , p. 4). Paraschi (2020 , p. 19) stated that e-learning might even be an alternative activity that is to help communities previously relying on other activities, such as competitive educational and training e-learning programs blended with on-site summer schools in a Greek island as a replacement for tourism, which suffered greatly during the COVID-19 pandemic.
However, there are multiple challenges related to e-learning that come as a result of COVID-19. For instance, Almaiah et al. (2020) identified the critical challenges and factors of e-learning system usage during COVID-19 pandemic. In the research, the authors covered the topics of e-learning system quality, trust, culture, self-efficacy, and issues of financial support, change management and technical maintenance, all of which were mentioned as potentially influential factors of e-learning adoption. Moreover, we argue that COVID-19 pandemic is a challenge impacting the approach to e-learning, thus requiring adaptation and innovation in higher education to cope with the posed challenge. Alea et al. (2020) have evaluated the perceptions among the teachers about the impact of COVID-19 and the community quarantine on the distance learning and found multiple challenges related to it, as well as individual issues with preparedness for delivering distance learning. Also, Abbasi et al. (2020) stated that students did not prefer e-teaching over face-to-face teaching during the lockdown situation, and that administration and faculty members must take necessary measures to improve e-learning during the lockdown. Favale et al. (2020) stated that in the context of 80–90% of people in Italy staying at home during the quarantine, remote working and online collaboration exploded in an Italian university. Thus, the research on participation in e-learning in the context of COVID-19 is very relevant and timely.
2.4 Information service, information systems and information technology
In literature, information service is defined as “a component of an information system representing a well-defined business unit that offers capabilities to realise business activities and owns resources (data, rules, roles) to realise these capabilities” ( Ralyté et al. , 2015 , p. 39). Furthermore, Wijnhoven and Kraaijenbrink (2008 , p. 93) suggested that information services are “services that facilitate the exchange of information goods with or without transforming these goods”. The authors (2008, p. 114) stated that “information services have a lot in common with other types of information systems”, hence implying that the information services are distinct from the information systems. Importantly, it is necessary to outline that information system (IS) is defined as any combination of information technology (IT) and people's activities using that technology ( Gupta, 2000 ).
Accordingly, IT consists of telecommunications, computing, and content, whereby different types of IT are represented at the intersections (e.g. Internet being partly computing, and partly telecommunications). Hence, one may wonder about the exact definitions of an information service, an information system, an information technology and what is the interrelation between them. It is essential to underline that the terms are potentially having blurry boundaries and are hard to define. For the purposes of this particular study, information service is defined as the use of information technology by people. However, the information system of e-learning at large is not considered to be limited only to LMS such as Moodle as there are many other physical and virtual information services that could facilitate e-learning. This study will try to focus on the information services of e-learning that facilitate participation over IT.
3. Theoretical framework and hypothesis development
Ke and Hoadley (2009) suggested that there is no “one size fits all frameworks” when evaluating online learning communities. From the literature on e-learning, there are a number of identified antecedent factors that could potentially influence participation in e-learning. Besides, factors related to the current situation of pandemic (COVID-19) may also impact the participation in e-learning. The research model for this study is developed based on the literature review outlined above. Firstly, several antecedent factors that may affect participation in e-learning are identified. Secondly, these factors are used to build a theoretical framework which will be evaluated and examined empirically.
3.1 COVID-19 related factors
At the time of writing the paper, the research on the COVID-19 is new, as it is a relatively recent event. Hence, the exploratory purpose of the paper is to identify potential factors that may impact e-learning participation in quarantine time. Therefore, we aim to review the most recently published studies on this topic. For example, Alea et al. (2020) have recently performed a research on the opinions of teachers concerning the preparedness and challenges that the university might face when adopting e-learning in the times of the quarantine. They empirically evaluated the (1) awareness of the COVID-related situation, (2) the teacher's readiness and school's preparedness to conduct distance learning, and (3) perceived challenges in distance learning education ( Musingafi et al. , 2015 ). In this study, nevertheless, as we plan to survey students instead of teachers, we adapt the same survey questions and modify them slightly to fit the context of our study. As such, we use (1) awareness of COVID-19, (2) perceived challenges to participate in e-learning during the quarantine, (3) perceived educational institutions preparedness [perceived teachers' preparedness and perceived school's preparedness] to conduct distance learning, as the COVID-19 related factors to examine the students' intention to e-learning participation.
Awareness of COVID-19 has a positive effect on the intention to e-learning participation.
Awareness of COVID-19 has a positive effect on perceived usefulness.
Awareness of COVID-19 has a positive effect on perceived ease of use.
Perceived challenges during COVID-19 has a negative effect on the intention to e-learning participation.
Perceived challenges during COVID-19 has a negative effect on perceived usefulness.
Perceived challenges during COVID-19 has a negative effect on perceived ease of use.
Perceived educational institutions preparedness during COVID-19 has a positive effect on the intention to e-learning participation.
Perceived educational institutions preparedness during COVID-19 has a positive effect on perceived usefulness.
Perceived educational institutions preparedness during COVID-19 has a positive effect on perceived ease of use.
3.2 Perceived usefulness of e-learning
Perceived usefulness has a significant effect on the intention to e-learning participation.
3.3 Perceived ease of use of e-learning
Perceived ease of use has a significant effect on the intention to e-learning participation.
Perceived ease of use has a significant effect on perceived usefulness.
3.4 Intention to participate in e-learning
In the current study, our dependent variable is e-learning participation, which is measured by the student's intention to participate. There may be multiple different factors that could affect the intention of students to participate in e-learning during the quarantine situation. Prior studies in e-learning research use intention to participate in e-learning ( Masrom, 2007 ; Tselios et al. , 2011 ; Zhang et al. , 2012 ; Park, 2009 ) as the outcome variable.
Moreover, we intend to examine several potential individual characteristics as control variables when assessing the model. We argue that the younger students are more accepting the use of IT for learning. Evidence is paradoxical in this aspect, as Fleming et al. (2017) stated that age does not impact the intention of using e-learning. Ong and Lai (2006) stated that gender might indirectly affect the acceptance of e-learning, as men and women had different perceptions of PU and PEOU of e-learning systems. The theoretical framework model is provided in Figure 1 .
4. Methodology
The data collection was done between 15 August to 15 October 2020 through an online survey when closure of all educational institution, specifically higher education was announced by the Finnish government started from March 2020. Prior to the primary data collection, survey items (instruments) to measure five factors predicting the use of e-learning during COVID-19 among higher education students were adopted from previously validated studies and based on the adaptation process, the items for the current study were slightly modified suit the contexts of the study, COVID-19 and e-learning.
The items for measuring COVID-19 awareness (three items), perceived teachers and school preparedness (six items) and perceived COVID-19 challenges (four items) all were derived from Alea et al. (2020 , pp. 134–136). Survey items for measure perceived usefulness (four items) and perceived ease of use (four items) were derived from Masrom (2007) and Davis (1989) . Finally, items for measuring intention to participate in e-learning during the COVID-19 were derived from Lee et al. (2009) and Davis (1989) . The model measurement and assessment of the constructs were done through the use of SmartPLS 3.2 that was guided by the procedures of Partial Least Squares Structural Equation Modelling (PLS-SEM).
4.1 Data collection
During the school closures, the survey instrument was distributed through an online survey application. The data were obtained only from those respondents who indicated they are currently university students. As mentioned, the data collection was formed in the course of two months, and over 350 invitations were sent. After the closure of the survey, 153 responses were received. Upon further examination of the completeness of the data and removing unengaged responses or those who indicated that they are not currently students, in total, 131 responses were included in the dataset for further analysis.
5.1 Descriptive statistics
Of the respondents, 73 (55.7%) were female, while 56 (42.7%) respondents were males, and two did not want to reveal their gender. The average age of respondents was 25 years old with (standard dev. = 6.1). Moreover, the highest degree of the respondents was as follow: high school diploma ( N = 63), bachelor's degree ( N = 40), master's degree ( N = 19), and PhD or other ( N = 9). We also asked respondents to indicate how long in total have they been using e-learning systems. The following information was retrieved; less than a year ( N = 61), between one to three years ( N = 37), more than three years ( N = 32) and only one respondent indicated has never used such learning systems. We also asked the respondent to indicate to what extent the instructor's teaching style would impact their decision to participate in e-learning. We asked, “the instructor encourages and motivates me to use e-learning”, or “the instructor's style of presentation holds my interest”. The results showed that 36 students thought the teaching style of the instructor would motivate and encourage them to use e-learning systems and interestingly 23 students mentioned it does not affect their intention or the effect is not considerable. Regarding the second question, we found 28 students who believed that the instructor's presentation style would have a substantial impact on their intention to use e-learning systems to participate in e-learning. The same number of ( N = 28) students believed that the instructor's presentation style does not at all play a role in their decision to use such systems for e-learning participation, or the effect is somewhat limited.
5.2 Measurement results
In the following, we report on the data analysis at the measurement model, which refers to the assessment of the measures' reliability and their validity. In doing so, we computed: (1) item (indicator) loadings and internal consistency reliability, (2) convergent validity, and (3) discriminant validity ( Hair et al. , 2019 ).
5.2.1 Item loadings and internal consistency reliability
PLS-SEM results were utilised for the item loadings in this study. Table 1 shows the detail of item loadings. As shown in Table 1 , all item loadings (except one item PCHA_2 with the slightly lower value) satisfied the recommended loading values of >0.70 ( Hair et al. , 2019 ). However, from the algorithm process in PLS-SEM, one item (indicator) from the COVID-19 awareness (CAWA_3) was dropped. Therefore, 24 items remained for the next step of the PLS-SEM analysis. Internal consistency reliability refers to the evaluation findings for the statistical consistency across survey items (indicators). According to Hair et al. (2019) , internal consistency reliability should be reported through Cronbach's alpha ( α ) and Composite Reliability (CR). Therefore, we computed these two tests and the values achieved were all above to the recommended threshold of 0.70 ( Hair et al. , 2019 ) providing good internal consistencies.
5.2.2 Convergent validity and discriminant validity
Convergent validity is a statistical measure that assesses the construct validity, and it suggests that assessments having similar or same constructs should be positively related. Regarding the convergent validity, the value s of average variance extracted (AVE) must be reported. As shown in Table 1 , all the AVE values were above the recommended threshold of 0.50.
Discriminant validity test examines the extent to which a construct is different from other constructs ( Hair et al. , 2019 ). In order to report the values, the Fornell Larcker criterion will be used, and the AVE scores of a construct should be lower than the shared variance for all model constructs. As shown in Table 2 , all the AVE scores satisfied this condition, and therefore, the discriminant validity was established based on the evaluation of the Fornell Larcker criterion ( Fornell and Larcker, 1981 ).
However, as we used the PLS-SEM approach to perform the data analysis, we also assessed the discriminant validity through the Heterotrait-Monotrait Ratio of Correlations (HTMT). Discriminant validity problems also appear when HTMT values are higher than 0.90. The construct can be similar if HTMT shows a value of >0.90, which in this case, it indicates the lack of discriminant validity. Table 3 shows the HTMT values, and as it is indicated, all values were lower than 0.90.
We also examined the collinearity by reporting Variance Inflation Factor (VIF) values. The collinearity will be an issue if the VIF value is above 3.00 ( Hair et al. , 2019 ). Perceived usefulness (VIF = 1.663) and perceived ease of use (VIF = 1.559) are the predictor of intention to participate in e-learning during the COVID-19. Moreover, COVID-19 awareness is the predictor of perceived usefulness (VIF = 1.064) and perceived ease of use (VIF = 1.064). Perceived educational institutions preparedness predict perceived usefulness (VIF = 1.087) and perceived ease of use (VIF = 1.087). Perceived COVID-19 challenges predict perceived usefulness (VIF = 1.088) and perceived ease of use (VIF = 1.088). Therefore, the collinearity test results show that collinearity does not emerge as an issue in this study ( Hair et al. , 2019 ).
5.3 Structural results
The structural model assessment was performed following Hair et al. (2019) recommendation. In order to assess the path coefficient between endogenous and exogenous constructs, the sample was bootstrapped through 5.000 sub-sampling. The results of the SRMR indicator estimating the goodness of fit of the structural model was 0.065. The structural results showed that most of the hypotheses were supported ( Table 4 and Figure 2 ). The outcome variable, i.e. intention to participate in e-learning was explained by variance of 69%. Moreover, the perceived usefulness and perceived ease of use were explained by variance of 21% and 15%, respectively. The SEM results showed that the path between COVID-19 awareness to intention to participate in e-learning was significant ( β = 0.192; t = 3.220; p = 0.001); therefore, H1 was supported by the model. The SEM results also showed that the path between COVID-19 awareness to perceived usefulness ( β = 0.243; t = 2.748; p = 0.005) was significant; thus H1a was supported by the model. However, the COVID-19 awareness to perceived ease of use was not significant; thus H1b was rejected by the model.
The SEM results showed that the path between perceived challenges, as expected, negatively impact intention to participate in e-learning ( β = −0.186; t = 2.789; p = 0.005); therefore, H2 was supported by the model. The SEM results also showed that the path between perceived challenges during the COVID-19, as expected, negatively impact both perceived usefulness ( β = −0.36; t = 4.599; p = 0.001) and ( β = −0.246; t = 3.167; p = 0.002), thus H2a and H2b were both supported by the model. In addition, the SEM results showed that the path between perceived educational institutions preparedness to intention to participate in e-learning was not significant; therefore, H3 was rejected by the model. This finding is similar to Zia (2020) who also found that the curriculum and technology have a negative impact on the online classes during the COVID-19 pandemic. Furthermore, the SEM results showed that the path between perceived educational institutions preparedness to PU was also not significant; thus H3a was rejected by the model. However, perceived educational institutions preparedness to PEOU was significant ( β = 0.235; t = 2.365; p = 0.02), thus H3b was supported by the model. Finally, the strongest relationship emerged between the path from perceived usefulness to participate in e-learning ( β = 0.623; t = 9.225; p = 0.001); therefore, H4 was supported by the model. However, the results showed that the path between perceived ease of use to participate in e-learning was significant was not significant; thus, H5 was rejected by the model. As per path between PEOU to PU, the SEM results showed a significant effect of PEOU to PU ( β = 0.484; t = 6.220; p = 0.001); thus H5a was supported by the model.
We also examined the mediating effect of perceived usefulness and ease of use between the COVID-19 related factors and intention to participate in e-learning. To do so, we first accounted for the results of total indirect effects and then examined the specific indirect effects values, as PLS-SEM procedures required. The mediation test results showed the total indirect effects for the paths between COVID-19 awareness ( β = 0.161; t = 2.618; p = 0.01), and perceived challenges ( β = −0.251; t = 4.630; p = 0.001) to intention to participate in e-learning were significant, indicating that there might be mediation effects in these path relationships. Therefore, we checked the specific indirect effects values and found that theses paths are mediated only through perceived usefulness. The result showed that the paths between COVID-19 awareness ( β = 0.152; t = 2.553; p = 0.01) and perceived challenges ( β = −0.224; t = 4.187; p = 0.001) to intention to participate in e-learning were partially mediated through perceived usefulness. Finally, the effect of perceived educational institutions preparedness to intention to participate in e-learning was only realised through the mediating effect of PEOU-PU ( β = 0.07; t = 2.218; p = 0.03).
5.4 Multigroup analysis (MGA)
The research model was further investigated to see if the demographic characteristics of the respondents impact the path relationships in the model. To do so, we used the gender, and the average time the participant used the e-learning system in their e-learning activities. These two variables were used as control variables, and then we ran multigroup analysis (MGA) with PLS-SEM. The MGA results showed that respondents are different in some paths (see Table 5 ). For example, the path between perceived teachers and school's preparedness to perceived usefulness was only significant for males ( β = 0.261; t = 1.995; p = 0.05). The MGA results also showed that the path relationships between perceived challenges to (1) intention to participate in e-learning, (2) PU and (3) PEOU, were significant only for females. Therefore, the perceived challenges of COVID-19 could be considered as an important and influential factor influencing directly the decision-making of the students in e-learning participation. Finally, the path between the COVID-19 awareness to PEOU was only significant for females ( β = 0.332; t = 3.406; p = 0.001).
We also divided respondents into two groups based on their use of e-learning systems; group 1 included those who indicated they have experienced and used such systems for less than a year ( N = 61), group two for those who indicated they have experienced and used such systems for more than one year ( N = 69). The MGA results showed that the path between perceived educational institutions preparedness and PEOU was only significant for Group 1, those who mentioned that they had used the e-learning system for less than one year. However, more differences were observed in paths between COVID-19 awareness and perceived challenges to intention to participate in e-learning, as well as the path between perceived challenges to PEOU, such that the effects of these two path relationships were only significant for respondents in Group 2 (see Table 5 ).
6. Discussion
The SEM analysis revealed that the students' intention to participate in e-learning is significantly affected by the COVID-19 awareness and perceived challenges of the pandemic. It may be because of the subjective nature of the studied phenomena, which relies on the factors that relate to the individual (i.e. awareness and perceived challenges of the pandemic). These finding are similar to Raza et al. (2020) who also stated that there is need for improving the e-learning experience among students and escalating their intention to use such learning systems. Moreover, the perceived educational institution's preparedness (i.e. teachers and schools) seems to affect the intention to participate in e-learning only through the mediating effect of PEOU-PU. It may suggest that students do not see educational institutions' preparedness by itself as a motivating factor to use the e-learning system. It may also suggest that educational institutions have not been appropriately prepared to fully utilise the functionalities of e-learning systems (e.g. usefulness) facilitating the students' learning.
Moreover, the structure results showed that the awareness of COVID-19 situation might affect the usefulness of e-learning systems, but not the extent to which the use of such systems is easy. Given the pandemic requirements for safety via the social distancing and distance learning, students might consider e-learning systems as a better and safer alternative towards conventional in campus education. In other words, students have no other alternative left other than adapting to the dynamic situation and accepting to use e-learning systems to cope with the changes in their learning modes. Interestingly and as expected, the perceived challenges of COVID-19 situation seem to be a very influential factor determining the perceived value of e-learning systems and the intention to use them, however, it should be noted that the effect is negative. It may suggest that emotional and stress management of students is highly crucial for e-learning in the quarantine times.
Ong and Lai (2006) found that gender might impact the participation in e-learning through the perceived usefulness and perceived ease of use of e-learning systems. In the current paper, it was found the gender of the students impact their decision in e-learning participation. We would suggest that the perceived challenges of COVID-19 situation are having a more pronounced negative effect on female students than on their male counterpart. Plausibly, this might be due to the females' perceptions of their computer self-efficacy, which is crucial for e-learning ( Zhang et al. , 2012 ). In a similar vein, we would argue that the personality variations across genders may affect the results of why COVID-19 awareness has a significant impact on PEOU and the effect is only for females and why perceived preparedness has a significant impact on PU and that the effect is realised only for males. However, the latter may also be explained by the fact that males are more things-oriented, whereas females are people-oriented ( Su et al. , 2009 ). Hence, suggesting that males could potentially see more connections between e-learning systems' functionality (usefulness) and how these were improved by the preparedness of educational institutions.
The fact that the path between perceived educational institutions preparedness and PEOU was significant for those who used e-learning systems for a year or less may indicate that the educational institution's preparedness is only able to help an inexperienced user of e-learning systems by providing sufficient support and relevant information in the times of the pandemic. More experienced users of e-learning systems may have learned how to use them; hence the preparedness did not affect their perception of ease-of-use of e-learning systems. Contrarily, for experienced users who have used e-learning systems longer than a year, it may be that they are able to put the perceived challenges in perspective to the times when e-learning was not the main and the only mode of learning. The experience of use of e-learning systems is logically expected to be highly correlated with the age and the education level; hence, it could be hard to pinpoint whether differences come from the experience or other demographic variables.
7. Conclusions
The education of university students has been interrupted due to COVID-19 pandemic. The current situation has imposed unique challenges of smoothly maintaining the process of teaching and learning, as such e-learning has become an immediate solution to cope with the disruption in higher education. The results of this research revealed several theoretical implications. The first being the extension of the Technology Acceptance Model (TAM: Davis, 1989 ) for making it relevant to the current COVID-19 situation, and its application in the context of higher education to assess students' intention to use e-learning systems. The core theoretical focus of this study was to develop a conceptual model to identify factors impacting the students' intention to e-learning participation during the COVID-19 pandemic. This paper theoretically contributes to the literature by showing that the awareness of and the perceived challenges of the COVID-19 pandemic situation were the most significant factors influencing e-learning participation during the COVID-19 pandemic. As students' awareness of COVID-19 pandemic is increased, they would be more willing to achieve their education goals through the use of e-learning systems, especially when they are socially isolated, campus education is restricted and have to perform their studies mostly online. Moreover, the findings showed that no matter how well prepared the educational institutions (teachers and schools) are, the usefulness of e-learning systems still plays the leading role in enhancing the students' intention to participate in e-learning. Surprisingly, we did not find any direct impact of ease of use of e-learning systems to the intention of e-learning participation. Perhaps, blended learning (offline and online education) could be still the most proffered modes of learning for the students. In other words, a blended approach, where traditional teaching is combined with online teaching, should have ushered the students to participate in e-learning.
Alea et al. (2020) have found that there are multiple challenges in terms of educational preparedness during the COVID-19. However, in this study, it was found that educational institutions preparedness has little to no effect on the intention to participate in e-learning. Thus, the educational institutions are advised to consider the findings of this study to review their approaches to address their politics regarding e-learning in the times of the quarantine. We also found that the effects of the perceived pandemic challenges and educational institutions preparedness are different for experienced and inexperienced users of e-learning systems as well as among female and male students. As such, gender should be considered as a crucial factor in e-learning initiative taken by the educational institutions. Perceived challenges seem to have the most negative impact on women in the pandemic situation and their participation in e-learning. Sun et al. (2008) suggested that personal perceptions about e-learning affect the intention to participate in e-learning. In our study, it seems that the intention to participate in e-learning is affected by the perceptions about the contextual situation, such as about the current pandemic situation, perceived challenges it creates, and how does the educational institution prepare itself to tackle the situation.
7.1 Limitations
One of the drawbacks of the current research is the sample size used that can be expanded to achieve more generalisable findings. The conceptual model was developed for the purpose of this research, and therefore, the structural results and findings should be interpreted carefully. The size of the dataset and the sampling strategy might be other sources of potential errors. Since the data were collected through an online survey and during the COVID-19 pandemic situation, it is very hard to evaluate and assess whether the respondents answered questions as accurate as possible. Finally, this study took place in Finland, and might not apply to other countries due to different COVID-19 situation, regulations and imposed restriction during the current situation.
7.2 Future research
This research has uncovered interesting manifold insights about the different COVID-19 related factors on e-learning at educational institutions. As such, future research may utilise the conceptual model developed in this research and aim to explore further findings in other contexts. For instance, by investigating what encourages students to participate in e-learning more and why education institutions preparedness (both teachers and schools) does not account for higher intention to participate in e-learning. Students' perceptions could also be explored qualitatively. For example, why and how exactly awareness about COVID-19 encourages more intention to use e-learning systems. Future research is also advised on exploring further how educational institutions should become better prepared for future events, if they may occur, such as one we are witnessing in the current pandemic situation.
Theoretical model
Structural model
Reflective indicator loadings and internal consistency reliability
| Construct | Items | Loading | Mean | Std | | CR | AVE |
|---|
| Perceived usefulness of e-learning | PU1 | 0.94 | 3.85 | 2.05 | 0.94 | 0.95 | 0.85 |
| PU2 | 0.91 | 3.85 | 2.03 |
| PU3 | 0.93 | 3.59 | 2.09 |
| PU4 | 0.90 | 4.59 | 1.93 |
| Perceived ease of use of e-learning | PEOU1 | 0.90 | 5.28 | 1.54 | 0.91 | 0.94 | 0.79 |
| PEOU2 | 0.89 | 5.45 | 1.56 |
| PEOU3 | 0.91 | 5.16 | 1.62 |
| PEOU4 | 0.86 | 5.24 | 1.52 |
| COVID-19 awareness | COVA1 | 0.87 | 6.81 | 0.74 | 0.80 | 0.91 | 0.83 |
| COVA2 | 0.94 | 6.70 | 0.95 |
| Perceived educational institutions preparedness | PEIP1 | 0.75 | 3.73 | 1.88 | 0.91 | 0.93 | 0.69 |
| PEIP2 | 0.76 | 4.29 | 1.84 |
| PEIP3 | 0.83 | 4.85 | 1.80 |
| PEIP4 | 0.85 | 4.82 | 1.82 |
| PEIP5 | 0.90 | 4.86 | 1.86 |
| PEIP6 | 0.87 | 4.61 | 1.89 |
| Perceived challenges | PC1 | 0.82 | 5.66 | 1.83 | 0.85 | 0.89 | 0.68 |
| PC2 | 0.68 | 5.05 | 1.84 |
| PC3 | 0.91 | 5.53 | 1.91 |
| PC4 | 0.87 | 5.74 | 1.82 |
| Intention to participate in e-learning | INT1 | 0.85 | 2.87 | 2.12 | 0.91 | 0.94 | 0.80 |
| INT2 | 0.85 | 4.50 | 1.83 |
| INT3 | 0.93 | 3.79 | 2.04 |
| INT4 | 0.93 | 3.62 | 2.06 |
: = Cronbach's alpha; CR = Composite reliability; AVE = Average explained variance| | COAV | INT | PC | PEOU | PU | PEIP |
|---|
| COVID-19_awareness | | | | | | |
| Intention to participate in e-learning | 0.303 | | | | | |
| Perceived challenges | 0.154 | −0.408 | | | | |
| Perceived ease of use | 0.079 | 0.538 | −0.283 | | | |
| Perceived usefulness | 0.205 | 0.794 | −0.346 | 0.567 | | |
| Perceived educational institutions preparedness | 0.153 | 0.265 | −0.212 | 0.299 | 0.226 | |
Discriminant validity (HTMT)
| | COAV | INT | PC | PEOU | PU | PEIP |
|---|
| | | | | | | |
| Intention to participate in e-learning | 0.346 | | | | | |
| Perceived challenge | 0.222 | 0.431 | | | | |
| Perceived ease of use | 0.090 | 0.587 | 0.303 | | | |
| Perceived usefulness | 0.225 | 0.857 | 0.362 | 0.610 | | |
| Perceived educational institutions preparedness | 0.173 | 0.280 | 0.217 | 0.326 | 0.234 | |
Structural results
| Hypothesis | | -statistics | Sig |
|---|
| : COVID-19_awareness → Intention to participate in e-learning | 0.192 | 3.220 | |
| : COVID-19_awareness → Perceived usefulness | 0.243 | 2.748 | |
| : COVID-19 awareness → Perceived ease of use | 0.081 | 0.890 | NS |
| : Perceived challenges → Intention to participate in e-learning | −0.186 | 2.789 | |
| : Perceived challenges → Perceived usefulness | −0.360 | 4.599 | |
| : Perceived challenges → Perceived ease of use | −0.246 | 3.167 | |
| : Perceived educational institutions preparedness → Intention to participate in e-learning | 0.022 | 0.389 | NS |
| : Perceived educational institutions preparedness → Perceived usefulness | 0.112 | 1.267 | NS |
| : Perceived educational institutions preparedness → Perceived ease of use | 0.235 | 2.365 | |
| : Perceived ease of use → Intention to participate in e-learning | 0.110 | 1.780 | NS |
| : Perceived usefulness → Intention to participate in e-learning | 0.623 | 9.225 | |
| : Perceived ease of use → Perceived usefulness | 0.484 | 6.220 | |
Multigroup analysis results
| Path relationships | | -statistics | | Sig |
|---|
| |
| Perceived educational institutions preparedness → PU | 0.261 | 1.995 | 0.05 | Male |
| Perceived challenge → Intention to participate in e-learning | −0.310 | 3.828 | 0.001 | Female |
| Perceived challenge → PU | −0.572 | 6.487 | 0.001 | Female |
| Perceived challenge → PEOU | −0.335 | 3.981 | 0.001 | Female |
| COVID-19 awareness → PEOU | 0.332 | 3.406 | 0.001 | Female |
| |
| Perceived educational institutions preparedness → PEOU | 0.331 | 2.161 | 0.031 | Group 1 |
| COVID-19 awareness → Intention to participate in e-learning | 0.248 | 2.906 | 0.004 | Group 1 |
| Perceived Challenge → Intention to participate in e-learning | −0.289 | 3.114 | 0.002 | Group 2 |
| Perceived Challenge → PU | −0.279 | 2.518 | 0.01 | Group 2 |
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Home » Management Case Studies » Case Study of IBM: Employee Training through E-Learning
Case Study of IBM: Employee Training through E-Learning
“E-learning is a technology area that often has both first-tier benefits, such as reduced travel costs, and second-tier benefits, such as increased employee performance that directly impacts profitability.” – Rebecca Wettemann, research director for Nucleus Research
In 2002, the International Business Machines Corporation (IBM) was ranked fourth by the Training magazine on it’s “The 2002 Training Top 100”. The magazine ranked companies based on their commitment towards workforce development and training imparted to employees even during periods of financial uncertainty.
Since its inception, IBM had been focusing on human resources development : The company concentrated on the education and training of its employees as an integral part of their development. During the mid 1990s, IBM reportedly spent about $1 billion for training its employees. However, in the late 1990s, IBM undertook a cost cutting drive , and started looking for ways to train its employees effectively at lower costs. After considerable research, in 1999, IBM decided to use e-Learning to train its employees. Initially, e-Learning was used to train IBM’s newly recruited managers.
IBM saved millions of dollars by training employees through e-learning. E-Learning also created a better learning environment for the company’s employees, compared to the traditional training methods . The company reportedly saved about $166 million within one year of implementing the e-learning program for training its employees all over the world. The figure rose to $350 million in 2001. During this year, IBM reported a return on investment (ROI)’s of 2284 percent from its Basic Blue e-Learning program. This was mainly due to the significant reduction in the company’s training costs and positive results reaped from e-learning. Andrew Sadler, director of IBM Mindspan Solutions, explained the benefits of e-learning to IBM, “All measures of effectiveness went up. It’s saving money and delivering more effective training,’ while at the same time providing five times more content than before.” By 2002, IBM had emerged as the company with the largest number of employee’s who have enrolled into e-Learning courses.
However, a section of analysts and some managers at IBM felt that e-Learning would never be able to’ replace the traditional modes of training completely. Rick Horton, general manager of learning services at IBM, said, “The classroom is still the best in a high-technology environment, which requires hands-on laboratories and teaming, or a situation where it .is important for the group to be together to take advantage of the equipment.”
Though there were varied opinions about the effectiveness of e-Learning as a training tool for employees, IBM saw it as a major business opportunity and started offering e- learning products to other organizations as well. Analysts estimated that the market for e-Learning programs would grow from $2.1 billion in 2001 to $33.6 billion in 2005 representing a 100 percent compounded annual growth rate (CAGR).
Background Note
Since the inception of IBM, its top management laid great emphasis on respecting every employee. It felt that every employee’s contribution was important for the organization. Thomas J. Watson Sr. (Watson Sr.), the father of modern IBM had once said, “By the simple belief that if we respected our people and helped them respect themselves, the company would certainly profit.” The HR policies at IBM were employee-friendly. Employees were compensated well – as they were paid above the industry average. in terms of wages. The company followed a ‘no layoffs’ policy. Even during financially troubled periods, employees were relocated from the plants, labs and headquarters, and were retrained for careers in sales, customer engineering, field administration and programming.
IBM had emphasized on training its employees from the very beginning. In 1933 (after 15 years of its inception), the construction of the ‘IBM Schoolhouse’ to offer education and training for employees, was completed. The building had Watson Sr.’s ‘Five Steps of Knowledge’ carved on the front entrance. The five steps included ‘Read, Listen, Discuss, Observe and Think.’ Managers were trained at the school at regular intervals.
To widen their knowledge base and broaden their perspectives, managers were also sent for educational programs to Harvard, the London School of Economics, MIT and Stanford. Those who excelled in these programs were sent to the Advanced Managers School, a program offered in about forty colleges including some in Harvard, Columbia, Virginia, Georgia and Indiana. IBM’s highest-ranking executives were sent to executive seminars, organized at the Brookings Institutions this program typically covered a broad range of subjects including, international and domestic, political and economic affairs. IBM executives were exposed to topical events with a special emphasis on their implications for the company.
In 1997, Louis Gerstner (Gerstner), the then CEO of IBM , conducted a research to identify the unique characteristics of best executives and managers. The research revealed that the ability to train employees was an essential skill, which differentiated best executives and managers. Therefore, Gerstner aimed at improving the managers’ training skills. Gerstner adopted a coaching methodology of Sir John Whitmore, which was taught to the managers through training workshops.
However, after some time, Gerstner realized that the training workshops were not enough. Moreover, these workshops were not ‘ just-in-time .’ Managers had to wait for months before their turn of attending the work shops came. Therefore, in most of the cases, during the initial weeks at the job, the employees did not possess the knowledge of critical aspects like team building.
IBM trained about 5000 new managers in a year. There was a five-day training program for all the new managers, where they were familiarized with the basic culture, strategy and management of IBM. However, as the jobs became more complex, the five-day program turned out to be insufficient for the managers to train them effectively. The company felt that the training process had to be continuous and not a one-time event.
Gerstner thus started looking for new ways of training managers. The company specifically wanted its management training initiatives to address the following issues:
- Management of people across geographic borders
- Management of remote and mobile employees
- Digital collaboration issues
- Reductions in management development resources
- Limited management time for training and development
- Management’s low comfort level in accessing and searching online HR resources
The company required a continuous training program, without the costs and time associated with bringing together 5000 managers from all over the world. After conducting a research, IBM felt that online training would be an ideal solution to this problem. The company planned to utilize the services of IBM Mindspan Solutions to design and support the company’s manager training program. This was IBM’s first e- learning project on international training.
Online Training at IBM
In 1999, IBM launched the pilot Basic Blue management training program, which was fully deployed in 2000. Basic Blue was an in-house management training program for new managers. It imparted 75 percent of the training online and the remaining 25 percent through the traditional classroom mode. The e-Learning part included articles, simulations, job aids and short courses.
The founding principle of Basic Blue was that ‘learning is an extended process, not a one-time event.” Basic Blue was based on a ‘4- Tier’ blended learning model’. The first three tiers were delivered online and the fourth tier included one -week long traditional classroom training. The program offered basic skills and knowledge to managers so that they can become effective leaders and people-oriented managers.
The managers were provided access to a lot of information including a database of questions, answers and sample scenarios called Manager QuickViews. This information addressed the issues like evaluation, retention, and conflict resolution and so on, which managers came across. A manager who faced a problem could either access the relevant topic directly, or find the relevant information using a search engine. He/she had direct access to materials on the computer’s desktop for online reading. The material also highlighted other important web sites to be browsed for further information. IBM believed that its managers should be aware of practices and policies followed in different countries. Hence, the groups were foremen virtually by videoconferencing with team members from all over the world,”
In the second tier, the managers were provided with simulated situations. Senior managers trained the managers online. The simulations enabled the managers to learn about employee skill-building, compensation and benefits, multicultural issues, work/life balance- issues and business conduct in an interactive manner. Some of the content for [his tier was offered by Harvard Business School and the simulations were created by Cognitive Arts of Chicago. The online Coaching Simulator offered eight scenarios with 5000 scenes of action, decision points and branching results. IBM Management Development’s web site, Going Global offered as many as 300 interactive scenarios on culture clashes.
In the third tier, the members of the group started interacting with each other online. This tier used IBM’s collaboration tools such as chats, and team rooms including IBM e-Learning products like the Team-Room, Customer-Room and Lotus Learning Space. Using these tools, employees could interact online with the instructors as well as with peers in their groups. This tier also used virtual team exercises and included advanced technologies like application sharing, live virtual classrooms and interactive presentation: on the web. In this tier, the members of the group had to solve problems as a team by forming virtual groups, using these products. Hence, this tier focused more on developing the collaborative skills of the learners.
Though training through e-Learning was very successful, IBM believed that classroom training was also essential to develop people skills. Therefore, the fourth tier comprised a classroom training program, own as ‘Learning Lab.’ By the time the managers reached this tire, they all reached a similar level of knowledge by mastering the content in the first three tiers. Managers had to pass an online test on the content provided in the above three tiers, before entering the fourth tier. In the fourth tier, the managers had to master the information acquired in the above three tiers and develop a deeper understanding and a broader skills set. There were no lectures in these sessions, and the managers had to learn by doing and by coordinating directly with others in the classroom.
The tremendous success of the Basic Blue initiative encouraged IBM to extend training through e-Learning to its-sales personnel and experienced managers as well. The e-Learning program for the sales personnel was known as ‘Sales Compass,’ and the one for the experienced managers, as ‘Managing@ IBM.’ Prior to the implementation of the Sales Compass e-Learning program, the sales personnel underwent live training at the company’s headquarters and training campuses. They also attended field training program, national sales conferences and other traditional methods of training. However, in most of the cases these methods proved too expensive, ineffective and time-consuming. Apart from this, coordination problems also cropped up, as the sales team was spread across the world. Moreover, in a highly competitive market, IBM could not afford to keep its sales team away from work for weeks together.
Though Sales Compass was originally started in 1997 on a trial basis to help the sales team in selling business intelligence solutions to the retail and manufacturing industries, it-was not implemented on a large scale. But with the success of Basic Blue, Sales Compass was developed further. The content of the new Sales Compass was divided into five categories including Solutions (13 courses), industries (23 courses), personal skills (2 courses), selling skills (11 courses), and tools and job aid (4 aids).
The sales personnel of IBM across the globe could use the information from their desktops using a web browser. Sales Compass provided critical information to the sales personnel helping them to understand various industries (including automotive, banking, government, insurance etc) in a much better manner. The information offered included industry snapshot, industry trends, market segmentation, key processes, positioning and selling industry solutions and identifying resources.
It also enabled the sales people to sell certain IBM products designed for Customer Relationship Management (CRM) , Enterprise Resource Planning (ERP) , Business Intelligence (BI) , and so on. Sales Compass also trained the sales personnel on skills like negotiating and selling services. Like the Basic Blue program, Sales Compass also had simulations for selling products to a specific industry like banking, about how to close a deal, and so on. It also allowed its users to ask questions and had links to information on other IBM sites and related websites.
Sales Compass was offered to 20,000 sales representatives, client relationship representatives, territory representatives, sales specialists, and service professionals at IBM. Brenda Toan (Toan), global skills and learning leader for IBM offices across the world, said, “Sales Compass is a just-in-time, just-enough sales support information site. Most of our users are mobile. So they are, most of the times, unable to get into a branch office and obtain information on a specific industry or solution. IBM Sales Compass provides industry-specific knowledge, advice on how to sell specific solutions, and selling tools that support our signature selling methodology, which is convenient for these users.”
IBM also launched an e-Learning program called ‘Managing @ IBM’ for its experienced managers, in late 2001. The program provided content related to leadership and people management skills, and enabled the managers to meet their specific needs. Unlike the Basic Blue program, this program enabled managers to choose information based on their requirements. The program included the face-to- face Learning Lab, e-learning, and Edvisor, a sophisticated Intelligent Web Agent. Edvisor offered three tracks offering various types of information.
By implementing the above programs, IBM was able to reduce its training budget as well as improve employee productivity significantly. In 2000, Basic Blue saved $16 million while Sales Compass saved $21 million. In 2001, IBM saved $200 million and its cost of training per-employee reduced significantly – from $400 to $135. E-learning also resulted in a deeper understanding of the learning content by the managers. It also enabled the managers to complete their classroom training modules in lesser time, as compared to the traditional training methods used earlier. The simulation modules and collaboration techniques created a richer learning environment. The e-learning projects also enabled the company to leverage corporate internal knowledge as most of the content they carried came from the internal content experts.
IBM’s cost savings through E-Learning
| | |
| Basic Blue | 16.0 |
| Going global | 0.6 |
| Coaching simulators | 0.8 |
| Manager Quick-Views | 6.6 |
| Customer-Room | 0.5 |
| Sales Compass | 21.0 |
The e-Learning projects of IBM had been successful right from the initial stages of their implementation. These programs were appreciated by HR experts of IDM, and other companies. The Basic Blue program bagged three awards of ‘Excellence in Practice’ from the American Society for Training & Development (ASTD) in March 2000. It was also included among the ten best ‘world-class implementations of corporate learning’ initiatives by the “E-Learning across the Enterprise: The Benchmarking Study of Best Practices” (Brandon Hall) in September 2000.
IBM continued its efforts to improve the visual information in all its e-Learning programs to make them more effective. The company also encouraged its other employees to attend these e-learning programs. Apart from this, IBM planned to update these programs on a continuous basis, using feedback from its new and experienced managers, its sales force and other employees.
IBM used e-Learning not only to train its employees, but also in other HR activities. In November 2001, IBM employees received the benefits enrollment material online. The employees could learn about the merits of various benefits and the criteria for availing these benefits, such as cost, coverage, customer service or performance using an Intranet tool called ‘Path Finder.’ This tool also enabled the employees to know about the various health plans offered by IBM. Besides, Pathfinder took information from the employees and returned a preferred plan with ranks and graphs. This application enabled employees to see and manage their benefits, deductions in their salaries, career changes and more. This obviously, increased employee satisfaction. The company also automated its hiring process. The new tool on the company’s intranet was capable of carrying out most of the employee hiring processes. Initially, IBM used to take ten days to find a temporary engineer or consultant. Now, the company was able to find such an employee in three days.
IBM also started exploring the evolving area of ‘mobile learning’ Analysts felt that for mobile sales force of IBM, m-Learning was the next ideal step (after e-Learning). IBM leveraged many new communication channels for offering its courses to employees. IBM also started offering the courses to its customers and to the general public. In early 2002, American Airlines (AA) used IBM’s e-Learning package, which enabled its flight attendants to log on to AA’s website and complete the ‘safety and security training’ from any place, at any time. The content included instruction clips, graphics, flash animation, and so on. This made the airlines annual safety training certification program guides more effective. Shanta Hudson-Fields, AA’s manager for line training and special projects, commented, “The full service package that IBM offers has allowed us to develop an effective online course for our large group of busy attendants. In addition to providing a flexible training certification experience for our attendants, American has also brought efficiency and cost savings to our training processes using IBM’s e-Learning solution.” The company had trained 24,000 flight attendants by November 2002.
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Dear Sir/Madam Hi according to your valuable report, may I have validated statistics of IBM educational per capita buget and ranking? Best regards
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Optimizing placebo and minimizing nocebo effects through communication: e-learning and virtual reality training development
- Janine Westendorp 1 , 2 ,
- Liesbeth M. van Vliet 1 , 2 ,
- Stefanie H. Meeuwis 1 , 2 ,
- Tim C. olde Hartman 3 ,
- Ariëtte R. J. Sanders 4 ,
- Eric Jutten 5 ,
- Monique Dirven 6 ,
- Kaya J. Peerdeman 1 , 2 &
- Andrea W. M. Evers 1 , 2
BMC Medical Education volume 24 , Article number: 707 ( 2024 ) Cite this article
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The effects of many treatments in healthcare are determined by factors other than the treatment itself. Patients’ expectations and the relationship with their healthcare provider can significantly affect treatment outcomes and thereby play a major role in eliciting placebo and nocebo effects. We aim to develop and evaluate an innovative communication training, consisting of an e-learning and virtual reality (VR) training, for healthcare providers across all disciplines, to optimize placebo and minimize nocebo effects through healthcare provider-patient communication. The current paper describes the development, mid-term evaluation, optimization, and final evaluation of the communication training, conducted in The Netherlands.
The development of both the e-learning and the VR training consisted of four phases: 1) content and technical development, 2) mid-term evaluation by healthcare providers and placebo/communication researchers, 3) optimization of the training, and 4) final evaluation by healthcare providers. To ensure the success, applicability, authenticity, and user-friendliness of the communication training, there was ongoing structural collaboration with healthcare providers as future end users, experts in the field of placebo/communication research, and educational experts in all phases.
Placebo/communication researchers and healthcare providers evaluated the e-learning positively (overall 7.9 on 0–10 scale) and the content was perceived as useful, accessible, and interesting. The VR training was assessed with an overall 6.9 (0–10 scale) and was evaluated as user-friendly and a safe method for practicing communication skills. Although there were some concerns regarding the authenticity of the VR training (i.e. to what extent the virtual patient reacts like a real patient), placebo and communication researchers, as well as healthcare providers, recognized the significant potential of the VR training for the future.
Conclusions
We have developed an innovative and user-friendly communication training, consisting of an e-learning and VR training (2D and 3D), that can be used to teach healthcare providers how to optimize placebo effects and minimize nocebo effects through healthcare provider-patient communication. Future studies can work on improved authenticity, translate the training into other languages and cultures, expand with additional VR cases, and measure the expected effects on providers communication skills and subsequently patient outcomes.
Peer Review reports
The effects of many regular clinical treatments in healthcare are partially determined by factors other than the treatment itself [ 1 , 2 ]. Patients’ expectations and the relationship with their healthcare provider can significantly affect treatment outcomes and thereby play a major role in placebo and nocebo effects [ 3 ]. We define placebo and nocebo effects as the changes in patient outcomes that can be explained by the expectations someone has about the treatment[ 4 ]. The underlying biopsychosocial processes involved in placebo and nocebo effects have been extensively studied. These processes include learning mechanisms (e.g. patients’ previous experiences or clinicians’ suggestions) and the healthcare provider-patient relationship (e.g. emphatic behavior) that can influence patient expectations and trust [ 3 , 5 , 6 , 7 , 8 ]. As the healthcare provider-patient interaction plays such an important role in eliciting placebo and nocebo effects [ 9 , 10 , 11 , 12 ], training healthcare providers’ communication with their patients is pivotal for optimizing healthcare.
Experts in placebo research consented that there are several strategies to optimize placebo effects and minimize nocebo effects through communication in clinical practice [ 4 , 13 ]. For example, healthcare providers could enhance treatment effects if they outline the expected benefits from treatment [ 14 ], prevent side effects by fine-tuning the information they give to patients [ 15 , 16 , 17 ], and increase trust and satisfaction through an empathetic attitude [ 18 , 19 , 20 , 21 ]. However, experts also agree that these communication strategies are currently underutilized, and that healthcare providers should preferably be trained to address placebo and nocebo effects via their communication [ 13 ].
Our goal was to develop and evaluate an innovative communication training for healthcare providers to optimize placebo and minimize nocebo effects through healthcare provider-patient communication. We aimed for the training to be suitable for healthcare providers across disciplines at every level, whether they are actively practicing or still in training, thus ensuring its broad applicability. The communication training will exist of two advanced eHealth components: an e-learning and virtual reality (VR) training. Using these eHealth techniques has the potential for great outreach as it can be easily offered online. Other advantages over hiring teachers or actors are: costs-efficiency, standardized teaching and practicing, safe learning environment, and opportunities for extensive repetitive practice [ 22 , 23 , 24 , 25 ]. Additionally, the use of virtual patients yields comparable learning effects compared to role-playing actors [ 26 , 27 ]. The aim of the communication training was threefold: 1) to familiarize healthcare providers with state-of-the art knowledge on placebo and nocebo effects, 2) to raise awareness about the role of placebo and nocebo effects in everyday clinical practice, and 3) to teach communication techniques that can optimize placebo effects and minimize nocebo effects in clinical practice. The current paper describes the development, mid-term evaluation, optimization, and final evaluation of the communication training.
The content of the communication training was based on the most recent scientific insights and expert consensus on placebo and nocebo effects, which has been investigated systematically during the first [ 4 ] and second [ 13 ] official Society for Interdisciplinary Placebo Studies (SIPS) conferences in 2017 and 2019. The training consists of two parts. First, the background theory, empirical evidence and communication skills are taught in an e-learning. Second, hands-on practice is offered in a VR training. Both the e-learning and the VR tool were developed in Dutch.
The e-learning was developed first and its content was the starting point for the VR training. The development of both the e-learning and the VR training took place between May 2021 and October 2022 and was divided into four phases: 1) content and technical development, 2) mid-term evaluation by healthcare providers and placebo/communication researchers, 3) optimization of the training, and 4) final evaluation by healthcare providers. To ensure the success, applicability, authenticity, and user-friendliness of the training, in all phases there was ongoing structural collaboration with a group of experts. This group consisted of all authors and the experts mentioned in the acknowledgements, in total including two general practitioners, two anesthesia practitioners (one physician and one physician assistant), one VR expert (and his team members) who developed the VR application, one educational expert (and her team members) who developed the e-learning, and fifteen national and international researchers (most with backgrounds in biomedical and health sciences, some of whom are also working in clinical practice). The authors together set up the content and design of the training. Throughout the phases, updates were consistently shared with the other experts for feedback and approval. The studies were conducted in The Netherlands and approved by the Ethical Committee of Psychology Research of Leiden University (2022–03-01-A.W.M. Evers-V2-3783 and 2022–06-10-A.W.M. Evers-V2-4051).
E-Learning development and evaluation
Content determination.
For the development of the e-learning we collaborated with a non-profit medical education provider, the Dutch Institute for Rational Use of Medicine (IVM). To determine the specific design and content topics of the e-learning, a brainstorm session was organized with an expert group of national and international clinicians and placebo/communication researchers (i.e. all authors and experts mentioned in acknowledgements). Subsequently, a content framework was created in collaboration with an education developer from IVM, which was sent to the expert group for approval. All involved experts agreed on the topics to be included (Fig. 1 ).
Overview of the e-learning’s main structure and contents
E-learning structure
The e-learning structure is based on leading didactic theories [ 28 , 29 , 30 , 31 ]. To activate and motivate, the e-learning starts with a welcome video, followed by an audio message from a general practitioner (AS) who already makes extensive use of the communication techniques. Second, healthcare providers are challenged to think about their own knowledge and skills, and what they want to improve. Third, an introduction about placebo and nocebo effects in clinical practice is given. This introduction is followed by five substantive modules (Fig. 1 ). Each module contains a video, which focuses on background knowledge, and textual information, which focuses on practical skills. Subsequently, an assignment is given (‘step-by-step case’) in which the healthcare provider can practice the learned techniques on an own (imaginary) patient. During this assignment, several questions are asked on how to act in a certain situation, followed by specific automated feedback. In a final take home assignment, the healthcare provider is encouraged to plan a moment to apply the learned knowledge in clinical practice. The e-learning ends with an optional test (15 multiple choice questions; pass after ≥ 10 correct answers) after which accreditation points could be obtained (Dutch accreditation available for: ABC 1, Kwaliteitsregister V&V and Verpleegkundig Specialisten Register). Thirty five test questions were developed to provide variety when a test had to be retaken.
E-learning optimization and evaluation
The e-learning was evaluated twice: mid-term evaluation and final evaluation. The mid-term evaluation took place directly after finishing the development of the first version of the e-learning and the collected feedback was used for optimization of the e-learning. In the final evaluation, the e-learning was re-evaluated by a new group of participants to measure if the adjustments led to improvement and to determine if the training was ready to be used in practice.
Participants
In both evaluations, we asked healthcare providers (future users) to evaluate the e-learning. During the mid-term evaluation we additionally included placebo/communication researchers to assess the e-learning for accuracy and quality of the content. In both evaluations, participants were recruited from the professional network of the research group members, for example researchers and healthcare professionals from Leiden University Medical Center (LUMC) and Radboud University Medical Center (RadboudUMC). In the final evaluation, participants were also recruited via (social) media (e.g. on LinkedIn and in the newsletter of IVM). Healthcare providers could follow the e-learning for free and they indicated whether they agreed to use their data for research before they started. In the mid-term evaluation, placebo/communication researchers ( N = 4) and healthcare providers (nurse N = 3; unknown N = 2) assessed the quality of the e-learning (whether the content is correct) and tested the user experience and realism of the e-learning. In the final evaluation, the e-learning was evaluated by healthcare providers (physician N = 5; nurse N = 4, other [unspecified] N = 9).
Procedure & materials
In both evaluations, participants went through the e-learning by themselves, at a self-chosen moment, from their own computers. No researcher was present during this process. To evaluate the e-learning two questionnaires were designed: 1) General questionnaire and 2) Specific questionnaire. The General questionnaire, offered through the e-learning environment, included 14 questions: Five questions about the participants’ background (e.g. ‘What is your job function?’), five multiple choice questions (e.g. ‘Do you think that the e-learning is user-friendly? yes/ reasonable/not really/no’), three open ended questions (e.g. ‘How can we improve the e-learning?’), and one rating (‘What grade do you give this e-learning? scale 1–10’). Table 1 (first column) shows the multiple choice questions. The Specific questionnaire, sent by e-mail, included 14 rating questions (scale 1–10) to evaluate each separate part of the e-learning (see the first column of Table 2 ; e.g. ‘How would you rate the quality of the information in Module 1? 1 = very poor quality 10 = very good quality’), and one open question (‘Do you have any additional feedback?’). During the mid-term evaluation, participants completed both questionnaires. During the final evaluation, participants completed only the General questionnaire.
VR training development and evaluation
In the VR training, healthcare providers interact with simulated patients in two different scenarios while using VR headsets. The VR training focused on training those techniques that have been agreed upon by the expert group in determining the content of the e-learning, as described above. To optimize placebo effects, the provider is taught to explain why the chosen treatment is offered, to emphasize what its short- and long-term benefits are, and to display a warm and empathic attitude (e.g. by maintaining eye contact with the virtual patient). To minimize nocebo effects, the provider learns techniques such as how to identify patients at risk by recognizing negative expectancy patterns, and how to carefully introduce potential side effects of a treatment. For development of the VR training, we collaborated with The Simulation Crew (TSC). TSC is a Dutch company that specializes in developing interactive VR communication training courses using Artificial Intelligence (AI) based speech technology and simulation techniques for training and feedback. In order to ensure that the VR training fits well with conversations in clinical practice, there was structural collaboration with two clinicians (ToH and AS). During the creation of the patient cases, roleplay sessions with three nurses were conducted. Throughout the development process, intensive consultations took place between the researchers, VR developers, and involved clinicians. The researchers took into account the empirical evidence, the VR developers the developmental feasibility, and the clinicians the comparison with clinical practice. Two patient cases were designed (Fig. 2 ). The names within the described cases have been contrived for development of the training and do not pertain to actual individuals under any circumstances. In selecting the features of the patients, we endeavored to be as diverse as possible, by incorporating variations in gender and age.
Brief description of the patient cases in the VR training
VR training structure
The two patient cases were integrated into an app, which can be utilized in 2D on mobile devices and in 3D with the Oculus Quest 2 VR headsets. Only the 3D version was tested in this study since the 2D version was developed later. Healthcare providers can talk aloud in the VR environment and the patient talks back. Artificial Intelligence (AI) tools, such as speech recognition and natural language processing/understanding , ensured that providers can freely interact with the patients in the VR environment and that they can explore the impact of different communication strategies on the patient. During the mid-term evaluation, the patient had a computer voice. To ensure natural responses from the virtual patients, between the mid-term and final evaluation TSC recorded all possible reactions with motion capture (gestures), facial capture (facial expression), and human voice. Moreover, the AI tracked and detected gaze direction which was used for feedback on keeping eye contact with the patient. After completing the consultation with the virtual patient, healthcare providers received personalized feedback on how they communicated with the patient, and what they could do to improve their skills.
VR training optimization and evaluation
The VR training (3D version) was evaluated twice: during a mid-term evaluation and a final evaluation. During the mid-term evaluation, both patient cases were assessed separately because case 2 was developed after the first evaluation of case 1. During the final evaluation, both cases were re-evaluated to measure if the adjustments led to improvement and to determine if the training was ready to be used in practice.
In both evaluations, we asked healthcare providers (future users) to evaluate the VR training. During the mid-term evaluation we additionally included placebo/communication researchers to assess the training for accuracy and quality of the content. In both evaluations, participants were recruited from the professional network of the research group members, for example researchers and healthcare professionals from Leiden University Medical Center (LUMC) and Radboud University Medical Center (RadboudUMC). During the mid-term evaluation, placebo/communication researchers ( N = 7) and healthcare providers (physician N = 7, nurse N = 2) assessed the VR training on quality, user experience, and authenticity (i.e. to what extent the virtual conversation corresponds with a real conversation). During the final evaluation, the VR training was evaluated by healthcare providers (nurse N = 10; physician N = 8; psychologist N = 2; unknown N = 2; researcher N = 1). Five participants were part of both evaluations.
Both evaluations were in person and several test days were organized in collaboration with TSC. In addition, some individual test appointments were scheduled. The procedure and materials were the same for both evaluations. Participants put on the VR headsets and went through one or both VR cases, having a conversation with the virtual patient multiple times. Participants’ interim feedback was noted by the researcher/TSC and the first impression was discussed and noted after the test. At the end of the appointment, all participants were asked to complete an evaluative questionnaire. The questionnaire contained five questions about the participants’ background (e.g. ‘What is your job function?’), multiple choice questions (e.g. ‘do you think the structure of the case is logical? Yes/Reasonable/Not really/No’), ratings (e.g. ‘how user-friendly do you find the VR training? scale 1–10’), and room for comments. See the first column of Table 3 for the multiple choice questions and ratings.
Participant characteristics
The background characteristics of all participants are summarized in Table 4 .
Mid-term evaluation
During the mid-term evaluation, all components of the e-learning were rated positively (range M = 7.5 – M = 8.4) except the take-home assignment ( M = 5.9, SD = 1.64) (Table 2 ). The alternation between the different types of information (e.g. text, video, assignment) was experienced as positive, as well as the structure, user-friendliness, and level of the e-learning (Table 1 ). The e-learning as a whole was assessed with a 7.9 ( N = 7 , SD = 0.90). Figure 3 shows some qualitative comments of participants per study.
Qualitative quotes evaluation studies
Optimization
Based on the quantitative and qualitative analysis of the mid-term evaluation, the following adjustments were made to optimize the e-learning:
- The take home assignment was offered as an optional, instead of a required part of the training.
- We added a clear overview screen at the beginning of the e-learning with the aim, the structure, the welcome video and an overview of the chapters.
- More example phrases, that healthcare providers can use in daily practice, were added (e.g. how to explore expectations).
- Detailed feedback on grammar and the general layout of the e-learning was processed when possible.
Final evaluation
The e-learning improved in terms of user-friendliness (‘yes’ from 43 to 72%) and applicability in practice (‘yes’ from 29 to 72%), see Table 1 . The overall assessment was equal in both evaluation moments ( N = 7 , M = 7.9, SD = 0.90 vs. N = 18, M = 7.9, SD = 0.76). Quotes of participants confirmed that the added practical examples were helpful: e.g. “Design, amount of information and usefulness of the information was good. Even though I am not a doctor, I will certainly use the knowledge and tips I have gained in my nursing role” . Enhancing the quality of the videos or including healthcare provider-patient interaction videos are potential suggestions for improvement (see quotes in Fig. 3 ).
During the mid-term evaluation, case 1 was rated less positively than case 2 ( M = 5.9; SD = 2.13 vs.
M = 7.4; SD = 0.48). More than half of the participants scored case 1 as difficult , however all participants perceived case 2 as either doable or easy . In both cases, participants indicated that the interaction with the simulated patient was difficult because the tool does not always understand everything they said (due to speech recognition limitations). This resulted in a stiff and sometimes unnatural conversation flow. The user-friendliness, on the other hand, was immediately assessed as sufficient in both cases ( M = 7.1; SD = 2.09 and M = 7.4; SD = 1.55, respectively), see Table 3 and Fig. 3 .
The first step towards VR training improvement was that all possible reactions/movements of the virtual patient were recorded by an actor in a motion-sensitive suit. This improvement gave the simulated patient a more human appearance. The following adjustments were also made to optimize the VR training:
- The recognition and vocabulary of the simulated patient was expanded, allowing the system to better understand what the participant is saying and improve the responses.
- After the participant welcomed the patient, the patient starts talking directly instead of waiting for a question from the trainee, which makes the start of the conversation smoother.
- More instructions were added to guide the participant through the conversation.
- The visuals were optimized (e.g. enhanced legibility of the computer screen in the virtual environment).
The final evaluation showed that case 1 improved in terms of structure, level and overall rating (see Table 3 ). Case 2 was assessed almost equal as in the mid-term evaluation. In both cases about half of the participants perceived the acquired knowledge as directly applicable in clinical practice (44% and 50%, respectively), almost the other half perceived it as reasonably applicable (39% and 44%, respectively). The comments also indicated that the VR training was perceived as valuable: e.g. “I think very valuable to use in education” . For additional quotes, see Fig. 3 . The VR training as a whole was assessed with a 6.9 ( N = 22, SD = 1.19). Instances where the avatar does not understand the participant or gives inappropriate responses remain a focus point for improvement in the future.
We developed and evaluated an innovative communication training, consisting of an e-learning and VR training, for healthcare providers to optimize placebo and minimize nocebo effects through healthcare provider-patient communication. Results of the evaluation studies show that both healthcare providers and communication/placebo researchers were mostly positive about the communication training. The e-learning was experienced as user-friendly and the content was perceived as accessible, interesting, and easily applicable in clinical practice. Enhancing the quality of the videos or including healthcare provider-patient interaction videos are potential suggestions for improvement. The VR training was experienced as user-friendly as well, and as offering a safe learning environment. Instances where the VR avatar does not understand the participant or gives inappropriate responses remain a focus point for improvement in the future.
The growing acknowledgement of the power of communication in healthcare is a positive development that results in an increase in communication training programs for healthcare providers. Existing communication training courses often focus on shared decision making [ 32 ], person centered care [ 33 ], or serious illness communication [ 34 , 35 , 36 ]. Fewer training courses focus on how to utilize placebo effects in clinical practice [ 37 , 38 , 39 ]. What our training adds to the existing training courses is that we focus on both optimizing placebo effects, and also minimizing nocebo effects. In addition to educating healthcare providers about the potential impact of expectations and empathy, we also train them in effectively informing patients about placebo and nocebo effects. We utilize various learning methods, including text, video, assignments, and virtual reality, and aim to be accessible to healthcare providers in all disciplines.
Setting up this e-learning and VR training presented some limitations and taught us some lessons that may also be helpful for others. First an issue, common in interdisciplinary collaborations [ 40 ], that arose at the initial stage of the development was that the researchers and educational experts (IVM and TSC) experienced lack of expertise in each other’s field. Learning each other's language was time-consuming, but frequent consultation at the beginning of the project has been helpful. The growth of knowledge of each other's field is reflected in the finding that VR case 2, which was developed after a first version of case 1 was evaluated, was immediately assessed better than case 1. Second, a well-known problem of VR is that it remains difficult to be authentic (i.e. to what extent the virtual patient reacts like a real patient) due to technical challenges [ 23 , 40 , 41 ]. In our VR training, we decided to use the technique natural language processing , instead of the more conventional choice-based dialogue . The use of natural language processing enables a real conversation with the virtual patient, however it is also more challenging and time-consuming to ensure a smooth conversation flow. Our results reveal that the authenticity did improve as we progressed in the development. More use of the VR training will improve speech recognition, due to the self-learning abilities of the applied AI. Third, during the final evaluation of the e-learning, we were not able to ascertain the specific medical roles of the participants involved, as the response option 'other' could not be elaborated upon. Fourth, the initial plan was to develop and evaluate the e-learning and the VR training simultaneously as one product. However, due to practical considerations (e.g. time constraints and the distribution of required expertise among multiple partners) separate developmental and evaluation phases were needed. Consequently, this separation led to relatively small sample sizes for all evaluations, which are a limitation of this study. Nonetheless, the separate development has also resulted in an additional benefit: the e-learning and VR training are two self-contained, full-fledged and complementary training tools. These tools can be offered independently or combined as a full training. Combining both training tools, starting with the e-learning followed by the VR training, may enhance the effectiveness of the training [ 35 ].
Development of this first-of-its-kind communication training offers opportunities for future directions. In a follow-up study the effect of this training on healthcare providers’ communication should be studied. To assess the improvement of healthcare providers' theoretical knowledge, the e-learning test can serve as a measurement instrument for both pre- and post-training evaluations. In the VR training, healthcare providers' communication is already being assessed through a scoring system, which is currently used to determine the personalized feedback. The score could potentially serve as a pre- and post-measurement, or it can be studied whether there is an enhancement in the scores when healthcare providers go through the case studies multiple times. Next, it can be investigated whether the acquired communication skills impact patient outcomes on both short- and long-term levels. Some potentially expected outcomes may include increased treatment effectiveness, higher levels of satisfaction and trust, as well as reduced anxiety and perceived side effects [ 18 , 42 , 43 , 44 ]. Another direction for the future is translation of the training. The current training has been developed from a Dutch (East European) perspective and is only available in Dutch. Translating the training to other languages and cultures is an important next step, where cultural differences and preferences must be taken into account [ 45 , 46 ]. A last valuable direction is expanding the VR training with more specific cases to connect even better with healthcare providers from all (para)medical disciplines (e.g. physiotherapists and psychologists). When developing new cases in the future, it is important to strive for diversity in patient features, such as gender, age, and culture. In future AI developments, it's essential to stay informed about ongoing advancements, potential biases, and ethical discussions.
Availability
The e-learning and VR training (2D and 3D) are already offered in The Netherlands and available via the websites of IVM and TSC. After completing the e-learning, Dutch accreditation is available for: ABC 1, Kwaliteitsregister V&V and Verpleegkundig Specialisten Register.
Training introduction video: https://www.youtube.com/watch?v=3N6r_Syk2SA
IVM: https://www.medicijngebruik.nl/scholing/e-learning/4942/behandeleffecten-verbeteren-via-communicatie
TSC: https://thesimulationcrew.com/producten/placebo/
To conclude, we have developed an innovative and user-friendly communication training that can be used to teach healthcare providers how to optimize placebo effects and minimize nocebo effects through healthcare provider-patient communication. The training consists of an e-learning and VR training (2D and 3D) which can be followed separately or together. Placebo/communication researchers and healthcare providers have provided a favorable evaluation of the training. However, the training’s potential effect on the communication of healthcare providers has not yet been studied. Future studies can focus on translating the training into other languages and cultures, improving the authenticity of the VR training, expanding with additional VR cases, and measuring the expected effects on healthcare provider communication skills, and subsequently, on patient outcomes.
Availability of data and materials
The data generated and/or analyzed during the current study will be made available upon request (corresponding author: [email protected]) after publication via the DataverseNL research data repository.
Abbreviations
Dutch Institute for Rational Use of Medicine (Instituut Verantwoord Medicijngebruik)
The simulation crew
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Acknowledgements
We thank the expert group of national and international clinicians and placebo/communication researchers for their involvement in the development of this communication training: Adam Hirsh, Jeremy Howick, Luana Colloca, Fabian Wolters, Judy Veldhuijzen, Antoinette van Laarhoven, Henriët van Middendorp, Aleksandrina Skvortsova, Hans van Lennep, Simone Meijer, Marc Godfried, Bram Thiel. We thank nurses Liz Tenhagen, Suzanne Kok en Kim Nijboer-Vliegen for their contribution to the role plays. We would like to thank the employees of the Dutch Institute for Rational Use of Medicine (IVM) and The Simulation crew (TSC) for their contribution to the development of the training. We would like to thank research assistants Marrit Veenstra and Eva Rümke for their support in data collection and analysis. Last, we would like to express our gratitude to all healthcare providers and placebo/communication researchers who participated in the evaluation studies of the training.
This project was funded by a European Research Council grant awarded to prof. dr. A.W.M. Evers (ERC proof of concept grant; 966785-COMMUNICATE-HEAL-TH).
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Study conceptualization: AE,SM, KP and LvV; Training development: JW, LvV, KP, SM, ToH, AS, EJ, MD, and AE. Data collection and analyzation: JW. JW drafted the full manuscript and all authors contributed to the revision of the manuscript. All authors read and approved the final manuscript.
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Ethical permission was obtained from the Ethical Committee of Psychology Research of Leiden University (2022–03-01-A.W.M. Evers-V2-3783 and 2022–06-10-A.W.M. Evers-V2-4051). Informed consent was obtained from all participants.
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Westendorp, J., van Vliet, L.M., Meeuwis, S.H. et al. Optimizing placebo and minimizing nocebo effects through communication: e-learning and virtual reality training development. BMC Med Educ 24 , 707 (2024). https://doi.org/10.1186/s12909-024-05671-0
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Predicting Customer Revenue in E-commerce Using Machine Learning a Case Study of the Google Merchandise Store
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This research paper explores the use of machine learning algorithms to predict customer revenue in e-commerce, using the Google Analytics Customer Revenue Prediction dataset as a case study. The dataset contains anonymized data from the Google Merchandise Store, an e-commerce site that sells Google-branded merchandise. We use the data to build and evaluate different machine learning models that predict the natural log of the revenue per customer for each session, based on various features such as demographic information, traffic source, and behavior on the website. Our findings suggest that machine learning algorithms like (LGBM Regressor) can effectively predict customer revenue in e-commerce, with root mean squared error (7.18e-11), Mean squared error(5.1e-21), R-squared(0.3260359), Mean Absolute Error(1.43e-11) and time performance(32 s). We also identify the key features that are most predictive of customer revenue, including visit number, total page views, total hits, hours, session ID, and day of the month. Overall, our research demonstrates the potential of machine learning in improving customer revenue prediction in e-commerce and provides insights for e-commerce businesses to optimize their marketing and sales strategies.
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Abunasser, B.S., Abu-Naser, S.S. (2024). Predicting Customer Revenue in E-commerce Using Machine Learning a Case Study of the Google Merchandise Store. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 210. Springer, Cham. https://doi.org/10.1007/978-3-031-59711-4_3
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- DOI: 10.1332/03055736y2024d000000042
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Drivers of fintech policy evolution: the role of policy learning and institutions
- Ringa Raudla , Egert Juuse , +2 authors Matti Ylönen
- Published in Policy & Politics 1 July 2024
- Political Science, Economics
- Policy & Politics
69 References
From tax havens to cryptocurrencies: secrecy-seeking capital in the global economy, fintech platform regulation: regulating with/against platforms in the uk and china, how does policy learning take place across a multilevel governance architecture during crises, policy learning from crises: lessons learned from the italian food stamp programme, independence, conservatism, and beyond: monetary policy, central bank governance and central banker preferences (1981-2021), the rise of corporate lobbying in the european union: an agenda for future research, fintech: what’s old, what’s new, institutional architecture for financial supervision: a case study, the impacts of technological innovation on regulatory structure: fintech in post-crisis europe, the age of fintech: implications for research, policy and practice, related papers.
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Research: Using AI at Work Makes Us Lonelier and Less Healthy
- David De Cremer
- Joel Koopman
Employees who use AI as a core part of their jobs report feeling more isolated, drinking more, and sleeping less than employees who don’t.
The promise of AI is alluring — optimized productivity, lightning-fast data analysis, and freedom from mundane tasks — and both companies and workers alike are fascinated (and more than a little dumbfounded) by how these tools allow them to do more and better work faster than ever before. Yet in fervor to keep pace with competitors and reap the efficiency gains associated with deploying AI, many organizations have lost sight of their most important asset: the humans whose jobs are being fragmented into tasks that are increasingly becoming automated. Across four studies, employees who use it as a core part of their jobs reported feeling lonelier, drinking more, and suffering from insomnia more than employees who don’t.
Imagine this: Jia, a marketing analyst, arrives at work, logs into her computer, and is greeted by an AI assistant that has already sorted through her emails, prioritized her tasks for the day, and generated first drafts of reports that used to take hours to write. Jia (like everyone who has spent time working with these tools) marvels at how much time she can save by using AI. Inspired by the efficiency-enhancing effects of AI, Jia feels that she can be so much more productive than before. As a result, she gets focused on completing as many tasks as possible in conjunction with her AI assistant.
- David De Cremer is a professor of management and technology at Northeastern University and the Dunton Family Dean of its D’Amore-McKim School of Business. His website is daviddecremer.com .
- JK Joel Koopman is the TJ Barlow Professor of Business Administration at the Mays Business School of Texas A&M University. His research interests include prosocial behavior, organizational justice, motivational processes, and research methodology. He has won multiple awards from Academy of Management’s HR Division (Early Career Achievement Award and David P. Lepak Service Award) along with the 2022 SIOP Distinguished Early Career Contributions award, and currently serves on the Leadership Committee for the HR Division of the Academy of Management .
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6. Produce A Character-Driven eLearning Video. Create a memorable eLearning character or use one who personifies your case study. Then produce an engaging eLearning video for them to star in. Character-driven videos improve knowledge retention and recall, as online learners are able to go at their own pace.
Chandigarh University. This exciting partnership between Chandigarh University and LinkedIn is shaping the future of students by giving them the skills to be future ready. LinkedIn Learning Helps Children's Healthcare of Atlanta Nurses Earn Specialty Certifications and Advance in Their Careers. →.
Case studies are a form of problem-based learning, where you present a situation that needs a resolution. A typical business case study is a detailed account, or story, of what happened in a particular company, industry, or project over a set period of time. The learner is given details about the situation, often in a historical context.
In an eLearning environment, case studies are a rich mixture of multimedia. In the following cases, using a scenario-based approach will work well: 1. When you can create a simulation of an actual system or have extensive video content on it. Find documentaries related to the content, in the form of YouTube videos.
Case Study: Ryman Healthcare. 5. Marley Spoon & SC Training (formerly EdApp) The last elearning case study is how Marley Spoon used SC Training (formerly EdApp)'s microlearning tools to onboard hundreds of new employees during their unprecedented growth brought on by COVID-19.
However, while many universities in the world use e-learning platforms, in our case this was for the first time used and it was a great teaching/learning experience. This paper aims to be easy to read and understand by proving the importance of using e-learning platforms in higher education. ... Case Study D. Benta a , G. Bologa a , I. Dzitac a ...
1. Identify a problem to investigate: This should be something accessible and relevant to students' lives. The problem should also be challenging and complex enough to yield multiple solutions with many layers. 2. Give context: Think of this step as a movie preview or book summary.
Higher Education Development, Evaluation, and Research Associates. This study explored the perceptions and experiences of a group of students enrolled in an online course in Economic Evaluation. A mixed methods approach was adopted for the data collection, and thematic analysis was used to synthesize the data collected and highlight key findings.
What the Case Study Method Really Teaches. Summary. It's been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study ...
Case Study: E-Learning Certification Program. Petra Mayer. FEBRUARY 14, 2023. Transcription: In this eLearning case study, I will cover a certification course project for a provider of software as a service. Lessons could be video based lessons, demos, case studies, or application exercises. My name is Petra Mayer, Petra Mayer Consulting and I ...
Similarly, Derr et al. (2018) and Fischer (2014) reported that students of different programs show a greater increase in performance when they participate in a distance or blended learning course.
Wayne was looking to take his eLearning design to the next level - instead of a series of PowerPoint-like slides that learners click through followed by a quiz at the end, he wanted something more engaging and effective. Part 1 of this eLearning case study includes the background and choices that were made. Part 2 of this eLearning case study ...
3.3 Perceived ease of use of e-learning. Perceived ease of use is defined as "the extent to which students believe that e-learning will be easy to use" (Lee et al., 2009, p. 1324).Cheng (2012) stated that the PEOU of e-learning impacts the intention to use e-learning, although it may be that PEOU has a weaker effect on the intention to use e-learning, than PU (Lee et al., 2009, p. 1327).
Online learners have the power to take a different approach to resolve the issue. 2. Emotionally Connect With Compelling Stories. One of the major drawbacks of case studies is that they lack emotionality. Hence, such courses can be enhanced by framing the case study with a compelling plot and eLearning characters. 3.
Case Study: E-Learning Certification Program. Petra Mayer. FEBRUARY 14, 2023. Transcription: In this eLearning case study, I will cover a certification course project for a provider of software as a service. Lessons could be video based lessons, demos, case studies, or application exercises. My name is Petra Mayer, Petra Mayer Consulting and I ...
Note: The in-person interviews and contextual inquires described in this case study were done pre-COVID-19. Stay safe! Overview. Learnbox is an all-in-one e-learning platform for full-time employees who want to sharpen their skills for their jobs or prepare for a new job altogether.
EduNow is an e-learning mobile application that provides courses from different fields for self-study students or full-time employees who want to sharpen their skills for their jobs. It has a modern, clean and very detailed UI design. You can easily customize the screens, allowing for great flexibility and ease-of-use when it comes to putting ...
Case Study: E-Learning Certification Program. Petra Mayer. FEBRUARY 14, 2023. Transcription: In this eLearning case study, I will cover a certification course project for a provider of software as a service.Lessons could be video based lessons, demos, case studies, or application exercises.My name is Petra Mayer, Petra Mayer Consulting and I was the E-learning consultant on this project.
Evaluating E-Learning: A Case Study. Investigating the experiences and perceptions of students who completed self-paced, independent study online courses as a project within a traditional, instructorled graduate course finds that technical e-learning units were evaluated less favorably by users than non-technical units. Expand.
Evaluating E-Learning: A Case Study. As e-learning proliferates in the business and academic communities, there is an ongoing need to evaluate its effectiveness. This paper investigates the experiences and perceptions of students who completed self-paced, independent study online courses as a project within a traditional, instructorled graduate ...
UI/UX,Product Design,App Design,Figma,Adobe Illustrator CC. eStudy - A Complete Case Study of eLearning Platform
Initially, e-Learning was used to train IBM's newly recruited managers. IBM saved millions of dollars by training employees through e-learning. E-Learning also created a better learning environment for the company's employees, compared to the traditional training methods. The company reportedly saved about $166 million within one year of ...
A global pharma giant has been working with Lionbridge for over two decades for life sciences translation services.Recently, we assembled a team of eLearning and pharmaceutical translation linguistic experts to translate their onboarding eLearning course into eight target languages. With a custom-tailored eLearning localization solution, Lionbridge's team managed to:
The paper analyses the value mindsets of pre-service teachers in terms of the digital learning environment (DLE). DLE is considered to be both a system of new tools and methods for teaching activity and a prospective educational eco-system for learning. The DLE value model is based on a psychodidactic approach, which concerns three main constructs—digital content values, communication values ...
The effects of many treatments in healthcare are determined by factors other than the treatment itself. Patients' expectations and the relationship with their healthcare provider can significantly affect treatment outcomes and thereby play a major role in eliciting placebo and nocebo effects. We aim to develop and evaluate an innovative communication training, consisting of an e-learning and ...
This research paper explores the use of machine learning algorithms to predict customer revenue in e-commerce, using the Google Analytics Customer Revenue Prediction dataset as a case study. The dataset contains anonymized data from the Google Merchandise Store, an e-commerce site that sells Google-branded merchandise.
The authors also recommend three strategies for marketers to make the shift from leading messaging to guiding messaging: 1) Enable co-creation and co-ownership, 2) Create directed learning ...
The OECD designs international standards and guidelines for development co-operation, based on best practices, and monitors their implementation by its members. It works closely with member and partner countries, and other stakeholders (such as the United Nations and other multilateral entities) to help them implement their development commitments. It also invites developing country ...
This article contributes to theoretical discussions in the public policy literature by outlining how policy learning and institutions may affect the evolution of national fintech policy stances. To test our theoretical framework, we analyse a comparative case study of the three Baltic states, which have experienced diverging fintech policy…
Joel Koopman is the TJ Barlow Professor of Business Administration at the Mays Business School of Texas A&M University. His research interests include prosocial behavior, organizational justice ...