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Chapter 27 - Learning Analytics Enriched by Emotions

from Part VII - Futuristic and Ultramodern Higher Education

Published online by Cambridge University Press:  09 June 2022

By
Veronica Liesaputra  and
Claudia Ott
Edited by
Andreas Kaplan
Andreas Kaplan
Affiliation:
ESCP Business School Berlin
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Summary

There is no doubt that students’ emotional states influence their general well-being and their learning success. Although current advances of computer hardware and Artificial Intelligence (AI) techniques made it possible to include real-time emotion-sensing as one of the ways to improve students’ learning experience and performance, there are many challenges that might inhibit or delay the deployment and usage of emotional learning analytics (LA) in education. In this chapter, we will critically review the current state-of-the art of emotion detection techniques, analysis and visualisation, the benefits of emotion analysis in education and the ethical issues surrounding emotion-aware systems in education. Finally, we hope that our guidelines on how to tackle each of those issues can support research in this area.

Keywords

affectsartificial intelligencedashboardsdeep learningethicsemotionslearning analyticsmachine learningprivacyvisualisations
Type
Chapter
Information
Digital Transformation and Disruption of Higher Education , pp. 361 - 376
Publisher: Cambridge University Press
Print publication year: 2022

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References

Barrett, L. F. and Russell, J. A. (2015) The Psychological Construction of Emotion. New York: Guilford.Google Scholar
Bosch, N., D’Mello, S. K., Baker, R. S., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., and Zhao, W. (2016) Detecting Student Emotions in Computer-Enabled Classrooms. Proceedings of the 25th International Joint Conference on Artificial Intelligence, 4125–4129.Google Scholar
Boyd, D., and Crawford, K. (2012) Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon. Information Communication and Society, 15(5), 662–679.CrossRefGoogle Scholar
Canales, L., Daelemans, W., Boldrini, E., and Martínez-Barco, P. (2019) Emolabel: Semi-automatic Methodology for Emotion Annotation of Social Media Text. IEEE Transactions on Affective Computing.Google Scholar
Culnan, M. J., and Armstrong, P. K. (1999) Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation. Organization Science, 10(1), 104–115.CrossRefGoogle Scholar
Data Ethics Commission of the Federal Government. Opinion of the Data Ethics Commission. (2018). www.bmi.bund.de/SharedDocs/downloads/EN/themen/it-digital-policy/datenethikkommission-abschlussgutachten-kurz.pdf.Google Scholar
David, B., Chalon, R., Zhang, B., and Yin, C. (2019) Design of a Collaborative Learning Environment Integrating Emotions and Virtual Assistants (Chatbots). IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, 51–56.CrossRefGoogle Scholar
Deng, J., and Ren, F. (2021) A Survey of Textual Emotion Recognition and Its Challenges. IEEE Transactions on Affective Computing.Google Scholar
D’mello, S., and Graesser, A. (2013) Autotutor and Affective Autotutor: Learning by Talking with Cognitively and Emotionally Intelligent Computers That Talk Back. ACM Transactions on Interactive Intelligent Systems, 2(4), 1–39.CrossRefGoogle Scholar
Ekman, P. (1984) Basic Emotions. Handbook of Cognition and Emotion, 98(45–60), 16.Google Scholar
Ez-zaouia, M., Tabard, A., and Lavoué, E. (2020) EMODASH: A Dashboard Supporting Retrospective Awareness of Emotions in Online Learning. International Journal of Human Computer Studies, 139, 102411.Google Scholar
Feng, K., and Chaspari, T. (2020) A Review of Generalizable Transfer Learning in Automatic Emotion Recognition. Frontiers in Computer Science, 2(9), 1–14.Google Scholar
Ghaleb, E., Popa, M., and Asteriadis, S. (2019) Multimodal and Temporal Perception of Audio-Visual Cues for Emotion Recognition. 8th International Conference on Affective Computing and Intelligent Interaction, 552–558.CrossRefGoogle Scholar
Goetz, T., Haag, L., Lipnevich, A. A., Keller, M. M., Frenzel, A. C., and Collier, A. P. (2014) Between-Domain Relations of Students’ Academic Emotions and Their Judgments of School Domain Similarity. Frontiers in Psychology, 5, 1153.Google Scholar
Inventado, P. S., Legaspi, R., Suarez, M., and Numao, M. (2011) Predicting Student Emotions Resulting from Appraisal of Its Feedback. Research & Practice in Technology Enhanced Learning, 6(2).Google Scholar
Jack, R. E., Garrod, O. G., and Schyns, P. G. (2014) Dynamic Facial Expressions of Emotion Transmit an Evolving Hierarchy of Signals over Time. Current Biology, 24(2), 187–192.Google Scholar
Jivet, I., Scheffel, M., Drachsler, H., and Specht, M. (2017) Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10474 LNCS. New York: Springer.Google Scholar
Kort, B., Reilly, R., and Picard, R. W. (2001) An Affective Model of Interplay between Emotions and Learning: Reengineering Educational Pedagogy-Building a Learning Companion. Proceedings IEEE International Conference on Advanced Learning Technologies, 43–46.Google Scholar
Lin, S.-C., Chen, C.-J., and Lee, T.-J. (2020) A Multi-label Classification with Hybrid Label-Based Meta-learning Method in Internet of Things. IEEE Access, 8, 42261–42269.Google Scholar
Liu, Z., Pataranutaporn, V., Ocumpaugh, J., and Baker, R. (2013) Sequences of Frustration and Confusion, and Learning. Proceedings of the 6th International Conference on Educational Data Mining, 114–120.Google Scholar
Liu, Z., Wang, T., Pinkwart, N., Liu, S., and Kang, L. (2018) An Emotion-Oriented Topic Modelling Approach to Discover What Students Are Concerned about in Course Forums. IEEE 18th International Conference on Advanced Learning Technologies, pp. 170–172.Google Scholar
Luzeckyj, A., West, D. S., Searle, B. K., Toohey, D. P., Vanderlelie, J. J., and Bell, K. R. (2020) Stakeholder Perspectives (Staff and Students) on Institution-Wide Use of Learning Analytics to Improve Learning and Teaching Outcomes. In Ifenthaler, D. and Gibson, D., eds., Adoption of Data Analytics in Higher Education Learning and Teaching. New York: Springer, 177–200.CrossRefGoogle Scholar
Matlovic, T., Gaspar, P., Moro, R., Simko, J., and Bielikova, M. (2016) Emotions Detection Using Facial Expressions Recognition and EEG. 11th International Workshop on Semantic and Social Media Adaptation and Personalization, pp. 18–23.CrossRefGoogle Scholar
McStay, A. (2020) Emotional AI and EdTech: Serving the Public Food? Learning, Media and Technology, 45(3), 270–283.CrossRefGoogle Scholar
Montero, C. S. and Suhonen, J. (2014) Emotion Analysis Meets Learning Analytics – Online Learner Profiling beyond Numerical Data. Proceedings of the 14th International Koli Calling Conference on Computing Education Research, 165–169.Google Scholar
Ocumpaugh, J., Baker, R. S., Karumbaiah, S., Crossley, S. A., and Labrum, M. (2020) Affective Sequences and Student Actions within Reasoning Mind. In International Conference on Artificial Intelligence in Education. New York: Springer, 437–447.Google Scholar
Pekrun, R., and Linnenbrink-Garcia, L. (2012) Academic Emotions and Student Engagement. In Handbook of Research on Student Engagement. New York: Springer.Google Scholar
Roberts, L. D., Howell, J. A., Seaman, K., and Gibson, D. C. (2016) Student Attitudes toward Learning Analytics in Higher Education: ‘The Fitbit Version of the Learning World’. Frontiers in Psychology, 7, 1–11. www.researchgate.net/profile/Joel-Howell/publication/311779993_Student_Attitudes_toward_Learning_Analytics_in_Higher_Education_The_Fitbit_Version_of_the_Learning_World/links/586c6dd008aebf17d3a5b7b1/Student-Attitudes-toward-Learning-Analytics-in-Higher-Education-The-Fitbit-Version-of-the-Learning-World.pdf.Google Scholar
Ruiz, S., Klerkx, J., Charleer, S., Fernández-Castro, I., Urretavizcaya, M., and Duval, E. (2016) Supporting Learning by Considering Emotions: Tracking and Visualization. A Case Study. Proceedings of the 6th International Conference on Learning Analytics & Knowledge. New York: ACM, 254–263.CrossRefGoogle Scholar
Sarmiento, J. P., Campos, F., and Wise, A. (2020) Engaging Students as Co-Designers of Learning Analytics. Proceedings 10th International Conference on Learning Analytics & Knowledge.Google Scholar
Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., and Dillenbourg, P. (2017) Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research. IEEE Transactions on Learning Technologies, 10(1), 30–41.Google Scholar
Sedrakyan, G., Leony, D., Muñoz-Merino, P. J., Kloos, C. D., and Verbert, K. (2017) Evaluating Student-Facing Learning Dashboards of Affective States. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10474 LNCS. New York: Springer, 224–237.Google Scholar
Sedrakyan, G., Mannens, E., and Verbert, K. (2019) Guiding the Choice of Learning Dashboard Visualizations: Linking Dashboard Design and Data Visualization Concepts. Journal of Visual Languages and Computing, 50, 19–38.CrossRefGoogle Scholar
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., and Kirschner, P. A. (2020) Linking Learning Behaviour Analytics and Learning Science Soncepts: Designing a Learning Analytics Dashboard for Feedback to Support Learning Regulation. Computers in Human Behaviour, 107, 105512.Google Scholar
Slade, S., Prinsloo, P., and Khalil, M. (2019) Learning Analytics at the Intersections of Student Trust, Disclosure and Benefit. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge. New York: ACM, 235–244.Google Scholar
Unankard, S., and Nadee, W. (2020) Topic Detection for Online Course Feedback Using lda. In Popescu, E., Hao, T., Hsu, T.-C., Xie, H., Temperini, M. and Chen, W., eds., Emerging Technologies for Education. Cham. Springer, 133–142.Google Scholar
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., and Santos, J. L. (2013) Learning Analytics Dashboard Applications. American Behavioural Scientist, 57(10), 1500–1509.Google Scholar
Wang, Q., Jing, S., Joyner, D., Wilcox, L., Li, H., Plötz, T., and Disalvo, B.(2020)Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology in Large Educational Contexts. In Proceedings of the 7th ACM Conference on Learning @ Scale. New York: ACM, 63–76.Google Scholar
West, D., Luzeckyj, A., Searle, B., Toohey, D., Vanderlelie, J., and Bell, K. R. (2020) Perspectives from the Stakeholder: Students’ Views regarding Learning Analytics and Data Collection. Australasian Journal of Educational Technology, 36(6), 72–88.CrossRefGoogle Scholar

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