Emotions, Technology, and Learning
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On the other hand, even these concepts were typically operationalized using survey instruments, i.
There is substantial educational research on emotions and virtual learning, but these areas have tended to develop along separate lines. Mixed approaches were used in 30 of the articles, with only 9 articles using qualitative methods alone Table 3. The results may seem unexpected, as the combination of emotion and virtual learning is as yet little researched. Explorative studies might be followed up by instrument development and quantitative approaches to establish the frequency or spread of a phenomenon. We analysed the different theoretical frameworks used in our 91 selected articles and found that most of them drew on theories of virtual learning Table 4.
The small number of affect and learning theorists may be explained, at least in part, by the fact that only 28 of the 91 articles had emotion as their main focus. We found only eight articles that referred to Bandura social theories on affect or learning. Furthermore, the articles focused mainly on the learning environment, learning strategies, flow, anxiety, and experience from the perspective of the individual student.
The social learning theories such as those on collaborative knowledge construction played a minor role. The results of the articles reviewed here were also analysed based on how compelling their evidence was. In general, several weak or isolated relations were found and only a few strong relations. One of these, namely satisfaction, was the most often used concept in the articles.
Emotions, Academic Work, and 'No Hard Feelings’ | Technology and Learning
However, on a more critical note, we observed that only a few of the studies actually pay attention to the fluctuation of emotions in the context and in the flow of events e. Most studies rely on post hoc data and operationalize emotions as traits rather than states e.
With regard to the social aspect of learning, the learning theories, and emotion theories appear to address it with different emphases. The most cited learning theorists and virtual learning theorists include several researchers who address social aspects of learning explicitly. The study is limited to a relatively narrow selection of journals, and it is possible that another selection might have produced different outcomes. However, several rounds of reading were done in order to ensure the thorough and consistent treatment of all data.
The scope of journals could be expanded in future reviews. With respect to emotion terminology, our selection criteria were inclusive resulting in analysing several studies only marginally discussing emotions. We are confident that our criteria have captured all those articles which give emotions a central role. However, it is possible that some articles marginally dealing with emotions might have evaded our criteria.
We have used the terminology introduced by the authors of the articles themselves. It should be noted that these authors use causal language to describe their research findings. This, however, may not always imply causal effects or associations, as we have described previously in a statistical sense, but rather the situation highlights possible relations between different aspects of student learning.
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The earlier studies in our review have been conducted in learning environments that may have had significantly different tools for facilitating interaction than modern virtual learning environments. In addition, both students and teachers may have been less experienced with virtual technologies than average students today.
Thus, the results may not apply to the very latest virtual learning environments. The data of the review consist of articles published in journals. Many of these are not freely available online, but can be accessed for a fee or through subscription. The authors declare no conflicts of interest. No ethics review was required to undertake this review. The study did not involve human research participants. Eija Henritius , M. Computer Science , is a doctoral student at the University of Helsinki.
Her research interests include higher education, academic integrity, and innovations in teaching. Markku S. Hannula , PhD Educ. His research interests include mathematical problem solving, visual attention, motivation and emotions. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries other than missing content should be directed to the corresponding author for the article.
Volume 50 , Issue 1. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Article Free Access. Eija Henritius Corresponding Author E-mail address: eija. Hannula Search for more papers by this author. Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access.
Please review our Terms and Conditions of Use and check box below to share full-text version of article. Introduction The widespread use of digital tools and environments in higher education presents us with new perspectives and forms of learning that benefit from different technologies. Method The selection of journals and articles The review was based on articles drawn from four international journals.
Analyses In order to classify the methods and designs used in the studies, a research typology was constructed using two levels, namely method and design Figure 1. Figure 1 Open in figure viewer PowerPoint. Typology for classifying research methods and designs. Figure 2 Open in figure viewer PowerPoint.
The research designs of the reviewed articles In the articles selected for this review, a quantitative approach was the most commonly used 52 followed by mixed approaches 30 , and a qualitative approach 9. Theorists No.
The key theoretical contributors of the reviewed articles In terms of theoretical background, we identified key researchers based on how many of the articles referred to theorists in total and how many times they appeared in the references of the selected 91 articles as a primary or secondary author. The way of applying virtual learning in the reviewed studies The use of the learning environment was analysed from two perspectives: 1 whether the environment was blended or virtual, and b the nature of the interpersonal context of the environment, i.
The research results of the reviewed articles The research results were divided according to the research focus areas and the relations between variables. Figure 3 Open in figure viewer PowerPoint. The key emotional relationships between variables reported in the reviewed articles For details, see Appendix 1. Discussion There is substantial educational research on emotions and virtual learning, but these areas have tended to develop along separate lines. Statements on open data, ethics, and conflict of interest The data of the review consist of articles published in journals.
Allen, M. The American Journal of Distance Education , 16 2 , 83 — Crossref Google Scholar. Google Scholar. Citing Literature. Volume 50 , Issue 1 January Pages While developing the program, they consulted Paul Ekman, emeritus professor of psychology at the University of California, San Francisco. Decades earlier, Ekman had developed a method to identify minute facial expressions and map them on to corresponding emotions.
But when the program was rolled out in , it was beset with problems. Officers were referring passengers for interrogation more or less at random, and the small number of arrests that came about were on charges unrelated to terrorism. Even more concerning was the fact that the program was allegedly used to justify racial profiling.
Ekman tried to distance himself from Spot, claiming his method was being misapplied. Some developers claim that automatic emotion detection systems will not only be better than humans at discovering true emotions by analyzing the face, but that these algorithms will become attuned to our innermost feelings, vastly improving interaction with our devices. But many experts studying the science of emotion are concerned that these algorithms will fail once again, making high-stakes decisions about our lives based on faulty science. Emotion detection technology requires two techniques: computer vision, to precisely identify facial expressions, and machine learning algorithms to analyze and interpret the emotional content of those facial features.
Typically, the second step employs a technique called supervised learning, a process by which an algorithm is trained to recognize things it has seen before. A graduate student, Rana el Kaliouby, was one of the first people to start experimenting with this approach. In , after moving from Egypt to Cambridge University to undertake a PhD in computer science, she found that she was spending more time with her computer than with other people. She figured that if she could teach the computer to recognize and react to her emotional state, her time spent far away from family and friends would be less lonely.
Kaliouby dedicated the rest of her doctoral studies to work on this problem, eventually developing a device that assisted children with Asperger syndrome read and respond to facial expressions. At first, Affectiva sold their emotion detection technology as a market research product, offering real-time emotional reactions to ads and products.
Picard left Affectiva in and became involved in a different biometrics startup, but the business continued to grow, as did the industry around it. Beyond market research, emotion detection technology is now being used to monitor and detect driver impairment, test user experience for video games and to help medical professionals assess the wellbeing of patients. Considering the affect of learners, teachers and others is something that Learning Experience Designers like myself do on a daily basis. Far from being an absurd proposition, we feel it is a key part of the learner journey towards understanding.
What are examples of strategies you implement to address emotional presence in the courses you develop? You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account.
You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. References Andersen, J. Teacher immediacy as a predictor of teaching effectiveness. Nimmo Ed. Anderson, T. New report on emotional presence in online education [Web log post].