Identification of Affective States in MOOCs A Systematic Literature Review

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Napoliana Souza
Gabriela Perry

Abstract

Massive Open Online Courses (MOOCs) are a type of online coursewere students have little interaction,  no instructor, and in some cases, no deadlines to finisch assignments. For this reason, a better understanding of student affection in MOOCs is importantant could have potential to open new perspectives for this type of course. The recent popularization of tools, code libraries and algorithms for intensive data analysis made possible collect data from text and interaction with the platforms, which can be used to infer correlations between affection and learning. In this context, a bibliographical review was carried out, considering the period between 2012 and 2018, with the goal of identifying which methods are being to identify affective states. Three databases were used: ACM Digital Library, IEEE Xplore and Scopus, and 46 papers were found. The articles revealed that the most common methods are related to data intensive techinques (i.e. machine learning, sentiment analysis and, more broadly, learning analytics). Methods such as physiological signal recognition andself-report were less frequent.

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Souza, N., & Perry, G. (2018). Identification of Affective States in MOOCs. International Journal for Innovation Education and Research, 6(12), 39-55. https://doi.org/10.31686/ijier.Vol6.Iss12.1250
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References

Afzal, S., Sengupta, B., Syed, M., Chawla, N., Ambrose, G. A., Chetlur, M. (2017).The ABC of MOOCs: Affect and Its Inter-Play with Behavior and Cognition. In: Seventh International Conference on Affective Computing and Intelligent Interaction, pp.279–284.
Alico, J. C., Maraorao, U. D. andMaraorao, R. D. (2017). Personal Variables and Anxiety in English and Mathematics: Correlational and Comparative Investigation among Pre-University Students. International Journal for Innovation Education and Research, 5(11), pp. 48-61.
Aromataris, E. and Riitano, D. (2014). Constructing a search strategy and searching for evidence. A guide to the literature search for a systematic review.Am J Nursing 2014, 114(5), pp. 49–56.
Augustin T. (2016a) Extended Use of Personal Factors in Adaptive e-Learning Environments: Moods in MOOC’s. In: Goonetilleke R., Karwowski W. (eds) Advances in Physical Ergonomics and Human Factors. Advances in Intelligent Systems and Computing, vol 489. Springer, Cham, pp 813-826.
Augustin T. (2016b). Emotion Determination in eLearning Environments Based on Facial Landmarks. In: Uden L., Liberona D., Feldmann B. (eds) Learning Technology for Education in Cloud – The Changing Face of Education. LTEC 2016. Communications in Computer and Information Science, vol. 620. Springer, Cham.
Bakharia, A. (2016). Towards cross-domain mooc forum post classification. In: Learning @ Scale, pages 253–256.
Caballé, S., Barolli, L., Feidakis, M., Matsuo, K.,Xhafa, F. &Daradoumi, T. (2014). A Study of Using SmartBox to Embed Emotion Awareness through Stimulation into e-Learning Environments. In: International Conference on Intelligent Networking and Collaborative Systems, pp. 469-474.
Chaplot, D. S., Rhim, E. & Kim, J. (2015). Predicting student attrition in MOOCs using sentiment analysis and neural networks. In: Proceedings of AIED 2015 fourth workshop on intelligent support for learning in groups, pp. 7-12.
Chen, C. J., Wong, V. S., Teh, C. S., Chuah, K. M. (2017). MOOC Videos-Derived Emotions. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 9, n° 2-9, p. 137-140.
Chen, G., Davis, D., Hauff, C.‚ and Houben, Geert-Jan. (2016). On the impact of personality in massive open online learning. In Proceedings of the 2016 conference on user modeling adaptation and personalization, pp. 121–130.
D’Errico, F., Paciello, M. and Cerniglia, L. (2016). When Emotions Enhance Students’ Engagement in E-Learning Processes.Journal of e-Learning and Knowledge Society, 12(4), pp. 9-23.
D’Errico, F., Pacielloa, M., Carolis, B., Vattanid, A., Palestra, G. and Anzivino, G. (2018). Cognitive Emotions in E-Learning Processes and Their Potential Relationship with Students’ Academic Adjustment.International Journal of Emotional Education, 10(1), pp. 89-111.
Dillon, J., Bosch, N., Chetlur, M., Wanigasekara, N., Ambrose, G. A., Sengupta, B. and D’Mello, S. K. (2016a). Student emotion, co-occurrence, and dropout in a MOOC context.In 9th International Conference on Educational Data Mining, pp. 353–357.
Dillon, J., Ambrose, G. A., Wanigasekara, N., Chetlur, M., Dey, P., Sengupta, B.&D'Mello, S. K. (2016b). Student affect during learning with a MOOC. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 528-529.
Drosos, I.,Guo, P. J., Parnin, C. (2017). HappyFace: Identifying and predicting frustrating obstacles for learning programming at scale. In: Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pp.171-179.
Ekkekakis, P. (2012). Affect, mood, and emotion. In: G. Tenenbaum, R.C. Eklund, and A. Kamata (Eds.), Measurement in sport and exercise psychology, pp. 321-332. Champaign, IL: Human Kinetics.
Ekman, P., &Keltner, D. (1997). Universal facial expressions of emotion: An old controversy and new findings. In U. C. Segerstråle& P. Molnár (Eds.), Nonverbal communication: Where nature meets culture (pp. 27-46).
Ez-Zaouia, M. and Lavoué, E. (2017). EMODA: a Tutor Oriented Multimodal and Contextual Emotional Dashboard. In Seventh International Learning Analytics and Knowledge Conference (LAK), pp.429-438.
Fei H., Li H. (2018). The Study of Learners’ Emotional Analysis Based on MOOC. In: Xiao J., Mao ZH.,Suzumura T., Zhang LJ. (eds) Cognitive Computing – ICCC 2018. ICCC 2018. Lecture Notes in Computer Science, vol 10971. Springer, Cham.
Fernández, A. R, González, F. S., Merino. P. J. M., Kloos, C. D. (2017). A Data Collection Experience with Canvas LMS as a Learning Platform. In: LASI-SPAIN.
Fernández, D. B., Luján-Mora, S. Villegas-Ch, W. (2017). Improvement of massive open online courses by text mining of students' emails: a case study. In Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality.
Goldberg, L. R. (1992). The development of markers for the big-five factor structure. Psychologicalassessment, 4(1):26–42
Guo, P. J., Kim, J., Rubin, R. (2014). How video production affects student engagement: an empirical study of MOOC videos. In: Proceedings of the first ACM conference on Learning @ scale conference, pp. 41-50.
Gupta, A., Jaiswal, R., Adhikari, S., BalasubramanianandVineeth, B. (2016). DAISEE: Dataset for Affective States in E-Learning Environments. https://arxiv.org/abs/1609.01885.
Harris, S. C., Zheng, L., Kumar, V. &Kinshuk. (2014). Multi-Dimensional Sentiment Classification in Online Learning Environment. In: Sixth International Conference on Technology for Education. In: Sixth International Conference on Technology for Education, pp. 172-175.
Heutte, J., KAplan, J., Fenouillet, F., Caron, P-A., Rosselle, M. (2014). MOOC User Persistence - Lessons from French Educational Policy Adoption and Deployment of a Pilot Course. Dans L. Uden, J. Sinclair, Y.-H. Tao, & D. Liberona (dir.), Learning Technology for Education in Cloud. MOOC and Big Data (LTEC'14), Communications in Computer and Information Science Vol. 446, 13–24.
Hu, J., Dowell, N., Brooks, C., & Yan, W. (2018). Temporal Changes in Affiliation and Emotion in MOOC Discussion Forum Discourse. Artificial Intelligence in Education, 145–149.
Huang, B., & Hew, K. F. (2017). Factors Influencing Learning and Factors Influencing Persistence. In: Proceedings of the 2017 International Conference on Information System and Data Mining, pp. 103-110.
Kamath, A., Biswas, A. &Balasubramanian, V. (2016). A crowdsourced approach to student engagement recognition in e-learning environments. In: IEEE Winter Conference on Applications of Computer Vision (WACV).
Kaushik, S. (2017). Virtual Learning and Arising Emotional Concerns.International Journal of Innovations and Advancement in Computer Science, 6(9), pp. 309- 315.
Kay, RH.,Loverock, S. (2008). Assessing emotions related to learning new software: the computer emotion scale. Comput Hum Behav 24:1605–1623.
Kevan, J. M., Menchaca, M. P., & Hoffman, E. S. (2016). Designing MOOCs for success: a student motivation-oriented framework. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 274-278.
Lavoue, E., Molinari, G., Trannois, M. (2017). Emotional Data Collection using Self-Reporting Tools in Distance Learning Courses. In: IEEE 17th International Conference on Advanced Learning Technologies, pp. 377-378.
Leony, D., Muñoz-Merino, P. K., Ruipérez-Valiente, J. A., Pardo, A.andKloos, C. D. (2015). Detection and Evaluation of Emotions in Massive Open Online Courses.Journal of Universal Computer Science, 21(5), 638-655.
Leony, D., Muñoz-Merino, P. K., Ruipérez-Valiente, J. A., Pardo, A. &Kloos, C. D. (2015). Detection and Evaluation of Emotions in Massive Open Online Courses. Journal of Universal Computer Science, 21(5), 638-655.
Li, Q., Baker, R. (2018). The different relationships between engagement and outcomes across participant subgroups in Massive Open Online Courses. Computers & Education, Vol. 127, pp. 41-65.
Liu, Z., Wang, T., Pinkwart, N., Liu, S., Kang, L. (2018). An Emotion Oriented Topic Modeling Approach to Discover What Students are Concerned about in Course Forums. In: 18th International Conference on Advanced Learning Technologies, pp. 170-172.
Liu, Z., Zhang, W., Sun, J., Cheng, H. N. H., Peng, X. & Liu, S. (2016). Emotion and Associated Topic Detection for Course Comments in a MOOC Platform.In International Conference on Educational Innovation through Technology (EITT).
Lubis, F. F., Rosmansyah, Y., Supangkat, S. H. (2016). Experience In Learners Review To Determine Attribute Relation For Course Completion. In: International Conference on ICT For Smart Society, pp. 32-36.
Montero, C. S. &Suhonen, J. (2014).Emotion Analysis Meets Learning Analytics – Online Learner Profiling beyond Numerical Data. In Proceedings of the 14th Koli Calling International Conference on Computing Education Research, pp. 165-169.
Montero, C. S. and Suhonen, J. (2014). Emotion Analysis Meets Learning Analytics – Online Learner Profiling beyond Numerical Data. In: Proceedings of the 14th Koli Calling International Conference on Computing Education Research, pp. 165-169.
Moreno-Marcos, P. M., Alario-Hoyos, C., Muñoz-Merino, P. J. (2018). Sentiment Analysis in MOOCs: A case study. In: IEEE Global Engineering Education Conference (EDUCON), pp. 1489-1496.
Munezero, M. D., Montero, C. S., Sutinen, E. and Pajunen, J. (2014). Are they different? affect, feeling, emotion, sentiment, and opinion detection in text, IEEE Transactions on Affective Computing 5 (2), pp. 1-12.
Oluwalola, F. K. (2015). Effect of Emotion on Distance e-Learning — The Fear of Technology. International Journal of Social Science and Humanity, 5(11), pp. 966- 970.
Pathak, V., Bhatia, MS, Sriniwas, J. and Batra, D. (2011). Emotions and Mood. DELHI PSYCHIATRY JOURNAL, 14:(2), pp. 220-227.
Pekrun, P., Goetz, T., Titz, W & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research, Educational psychologist, 37(2), 91–105.
Pham, P., Wang, J. (2017). AttentiveLearner2: A Multimodal Approach for Improving MOOC Learning on Mobile Devices. In: International Conference on Artificial Intelligence in Education, pp. 561-564.
Pireva, K., Imran, A. S., Dalipi, F. (2015). User behaviour Analysis on LMS and MOOC. In: IEEE Conference on e-Learning, e-Management and e-Services, pp. 21-26.
Ramesh, A, Kumar, S. H., Foulds, J. &Getoor, L. (2015). Weakly supervised models of aspectsentiment for online course discussion forums. In Annual Meeting of the Association for Computational Linguistics (ACL), pp. 74-83.
Rizzardini, R. H, Gütl, C.; Chang, V., Morales, M. (2014). MOOC in Latin America: Implementation and Lessons Learned. In L. Uden, Y.-H. Tao, H.-C. Yang,& I.-H. Ting (Eds.), The 2nd International Workshop on Learning Technology for Education in Cloud (Springer P., pp. 147–158).
Rothkrantz, L. (2017). New Didactic Models for MOOCs. In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017), pp.505-512.
Sandanayake, T. C., Madurapperuma, A. P. (2013). Computational model for affective e-Learning: Developing a model for recognising E-Learner's emotions. In: IEEE International Conference in MOOC, Innovation and Technology in Education (MITE), pp. 174-179.
Sharma, K., Alavi, H. S., Jermann, P., &Dillenbourg, P. (2016). A gaze-based learning analytics model. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, p. 417-421.
Shen, C. W. &Kuo, C. J. (2015). Learning in massive open online courses: Evidence from social media mining. Computers in Human Behavior, pp. 568-577.
Soltani, M., Zarzour, H. &Babahenini, M.C. (2018) Facial Emotion Detection in Massive Open Online Courses. In: Rocha Á., Adeli H., Reis L.P., Costanzo S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham, pp. 277-286.
Stern, R. M.; Ray, W. J.; Quigley, K. S. 2001. Psychophysiological Recording. 2nd edition. New York: Oxford University Press.
Tsai, Y., Lin, C., Hong, J., Tai, K. (2018). The effects of metacognition on online learning interest and continuance to learn with moocs. Computers & Education, 121, 18-29.
Tucker, C. S, Dickens B. &Divinsky, A. (2014). Knowledge Discovery of Student Sentiments in MOOCs and Their Impact on Course Performance. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.
Tucker, C., Pursel, B. K. (2014). Mining Student-Generated Textual Data in MOOCS and Quantifying Their Effects on Student Performance and Learning Outcomes. In 121 stASEE Annual Conference & Exposition.
Tze, V. M. C., Daniels, L. M., Buhr, E. and Le, L. (2017).Affective Profiles in a Massive Open Online Course and Their Relationship with Engagement. Frontiers in Education, vol. 2, Article 65, pp. 1-13.
Wang, H., Li, Y., Hu, X., Yang, Y., Meng, Z. & Chang, K. (2013). Using EEG to Improve Massive Open Online Courses Feedback Interaction. In: Proceedings of the 16th International Conference on Artificial Intelligence in Education.
Wang, L., Hu, G. & Zhou, T. (2018). Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education. Sustainability, vol. 10, pp. 1-19.
Wei. X., Lin, H., Yang, L & Yu, Y. (2017). A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification. Information 8(3), pp. 1–16.
Xiao, X. & Wang, J. (2016). Context and cognitive state triggered interventions for mobile MOOC learning. In 18th ACM International Conference on Multimodal Interaction, pp. 378-385.
Xiao, X., Pham, P. & Wang, J. (2017). Dynamics of Affective States During MOOC Learning. In International Conference on Artificial Intelligence in Education. Springer, 586–589.
Xing, B., Zhang, L., Gao, J., Yu, R. and Lyu, R. (2016). Barrier-free Affective Communication in MOOC Study by Analyzing Pupil Diameter Variation. In 9th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia 2016).
Yang, D., Wen, M., Howley, I., Kraut, R. & Rosé, C. (2015). Exploring the effect of confusion in discussion forums of massive open online courses. In Proceedings of the Second ACM Conference on Learning @ Scale Conference, L@S ’15, pp. 121-130.