Résumés
Abstract
Sentiments expressed on massive open online course (MOOC) discussion forums significantly influence learning effectiveness and academic performance. The evolution of learning sentiments on MOOC discussion forums is a dynamic process; however, a gap exists in the current understanding of the interplay between evolving sentiments and their impact on MOOC efficacy. Consequently, to enhance MOOC effectiveness further empirical research is needed to uncover the underlying patterns and temporal dynamics of learning sentiments. This study collected online discussions from 158 MOOC participants and examined the discussions using epistemic network analysis to identify how learning sentiment patterns differed according to performance level and learning topics. The results showed that learning sentiment patterns were affected by both performance level and learning topics, with participants in the high-score group exhibiting stronger associations between engagement-neutral and neutral-frustration, and fewer connections between frustration-delight and frustration-boredom when compared to those in the low-score group. In addition, this study found that engagement was strongly linked to all learning topics in the high-score group, whereas for the low-score group, only engagement and experience showed strong connections. Based on these findings, we discuss the implications for learners and instructors in paving the way for the development of targeted interventions and instructional strategies tailored to optimize MOOC effectiveness.
Keywords:
- learning sentiments,
- epistemic network analysis,
- MOOC discussion forum,
- learning topics,
- MOOC effectiveness
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Bibliography
- Alonso-Nuez, M. J., Gil-Lacruz, A. I., & Rosell-Martínez, J. (2020). Assessing evaluation: Why student engages or resists to active learning? International Journal of Technology and Design Education, 31(5), 1001–1017. https://doi.org/10.1007/s10798-020-09582-1
- Andrist, S., Ruis, A. R., & Shaffer, D. W. (2018). A network analytic approach to gaze coordination during a collaborative task. Computers in Human Behavior, 89, 339–348. https://doi.org/10.1016/j.chb.2018.07.017
- Arguel, A., Lockyer, L., Chai, K., Pachman, M., & Lipp, O. V. (2019). Puzzle-solving activity as an indicator of epistemic confusion. Frontiers in Psychology, 10, 163. https://doi.org/10.3389/fpsyg.2019.00163
- Avry, S., Chanel, G., Bétrancourt, M., & Molinari, G. (2020). Achievement appraisals, emotions and socio-cognitive processes: How they interplay in collaborative problem-solving? Computers in Human Behavior, 107, 106267. https://doi.org/10.1016/j.chb.2020.106267
- Baker, R. S. J. D., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223-241. https://doi.org/10.1016/j.ijhcs.2009.12.003
- Beymer, P. N., & Schmidt, J. A. (2023). Can you hear it? Toward conceptual clarity of emotional cost and negative emotions. Contemporary Educational Psychology, 74, 102198. https://doi.org/10.1016/j.cedpsych.2023.102198
- Camacho-Morles, J., Slemp, G. R., Pekrun, R., Loderer, K., Hou, H., & Oades, L. G. (2021). Activity achievement emotions and academic performance: A meta-analysis. Educational Psychology Review, 33(3), 1051–1095. https://doi.org/10.1007/s10648-020-09585-3
- D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001
- D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003
- Gasper, K., & Danube, C. L. (2016). The scope of our affective influences: When and how naturally occurring positive, negative, and neutral affects alter judgment. Personality and Social Psychology Bulletin, 42(3), 385–399. https://doi.org/10.1177/0146167216629131
- Han, Z. M. , Huang, C. Q. , Yu, J. H. , & Tsai, C. C. . (2021). Identifying patterns of epistemic emotions with respect to interactions in massive online open courses using deep learning and social network analysis. Computers in Human Behavior, 122(2), 106843. https://doi.org/10.1016/j.chb.2021.106843
- Harley, J. M., Bouchet, F., Hussain, M. S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615–625. https://doi.org/10.1016/j.chb.2015.02.013
- Huang, C. Q., Han, Z. M., Li, M. X., Jong, M. S.-y., & Tsai, C. C. (2019). Investigating students’ interaction patterns and dynamic learning sentiments in online discussions. Computers & Education, 140, 103589. https://doi.org/10.1016/j.compedu.2019.05.015
- Huang, C., Han, Z., Li, M., Wang, X., & Zhao, W. (2021). Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis. Australasian Journal of Educational Technology, 37(2), 81–95. https://doi.org/10.14742/ajet.6749
- Huang, C. , Yu, J. , Wu, F. , Wang, Y. , & Nian‐Shing Chen. (2024). Uncovering emotion sequence patterns in different interaction groups using deep learning and sequential pattern mining. Journal of Computer Assisted Learning, 40(4), 1777-1790. https://doi.org/10.1111/jcal.12977
- King, R. B., McInerney, D. M., Ganotice, F. A., & Villarosa, J. B. (2015). Positive affect catalyzes academic engagement: Cross-sectional, longitudinal, and experimental evidence. Learning and Individual Differences, 39, 64–72. https://doi.org/10.1016/j.lindif.2015.03.005
- Kiuru, N., Malmberg, L.-E., Eklund, K., Penttonen, M., Ahonen, T., & Hirvonen, R. (2022). How are learning experiences and task properties associated with adolescents’ emotions and psychophysiological states? Contemporary Educational Psychology, 71, 102095. https://doi.org/10.1016/j.cedpsych.2022.102095
- Lehman, B., D’Mello, S., & Graesser, A. (2012). Confusion and complex learning during interactions with computer learning environments. The Internet and Higher Education, 15(3), 184–194. https://doi.org/10.1016/j.iheduc.2012.01.002
- Liu, S., Liu, S., Liu, Z., Peng, X., & Yang, Z. (2022). Automated detection of emotional and cognitive engagement in MOOC discussions to predict learning achievement. Computers & Education, 181, 104461. https://doi.org/10.1016/j.compedu.2022.104461
- Lund, K., Quignard, M., & Williamson Shaffer, D. (2017). Gaining insight by transforming between temporal representations of human interaction. Journal of Learning Analytics, 4(3). https://doi.org/10.18608/jla.2017.43.6
- Parker, P. C., Perry, R. P., Hamm, J. M., Chipperfield, J. G., Pekrun, R., Dryden, R. P., Daniels, L. M., & Tze, V. M. C. (2021). A motivation perspective on achievement appraisals, emotions, and performance in an online learning environment. International Journal of Educational Research, 108, 101772. https://doi.org/10.1016/j.ijer.2021.101772
- Pekrun, R., & Marsh, H. W. (2022). Research on situated motivation and emotion: Progress and open problems. Learning and Instruction, 81, 101664. https://doi.org/10.1016/j.learninstruc.2022.101664
- Peterson, E. G., & Zengilowski, A. (2024). Educators’ perceptions of expectancy, value, and cost for supporting student emotions. Contemporary Educational Psychology, 78, 102294. https://doi.org/10.1016/j.cedpsych.2024.102294
- Rebolledo-Mendez, G., Huerta-Pacheco, N. S., Baker, R. S., & du Boulay, B. (2021). Meta-affective behaviour within an intelligent tutoring system for mathematics. International Journal of Artificial Intelligence in Education, 32(1), 174–195. https://doi.org/10.1007/s40593-021-00247-1
- Richey, J. E., Andres-Bray, J. M. L., Mogessie, M., Scruggs, R., Andres, J. M. A. L., Star, J. R., Baker, R. S., & McLaren, B. M. (2019). More confusion and frustration, better learning: The impact of erroneous examples. Computers & Education, 139, 173–190. https://doi.org/10.1016/j.compedu.2019.05.012
- Shao, K., Kutuk, G., Fryer, L. K., Nicholson, L. J., & Guo, J. (2023). Factors influencing Chinese undergraduate students’ emotions in an online EFL learning context during the COVID pandemic. Journal of Computer Assisted Learning, 39(5), 1465–1478. https://doi.org/10.1111/jcal.12791
- Tan, S. E., & Jung, I. (2024). Unveiling the dynamics and impact of emotional presence in collaborative learning. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00477-y
- Wei, X., Chen, Y., Shen, J., & Zhou, L. (2024). Fail or pass? Investigating learning experiences and interactive roles in MOOC discussion board. Computers & Education, 217, 105073. https://doi.org/10.1016/j.compedu.2024.105073
- Xing, W., Tang, H., & Pei, B. (2019). Beyond positive and negative emotions: Looking into the role of achievement emotions in discussion forums of MOOCs. The Internet and Higher Education, 43, 100690. https://doi.org/10.1016/j.iheduc.2019.100690
- Yang, S., Shu, D., & Yin, H. (2021). ‘Frustration drives me to grow’: Unraveling EFL teachers’ emotional trajectory interacting with identity development. Teaching and Teacher Education, 105, 103420. https://doi.org/10.1016/j.tate.2021.103420
- Yang, Y., Yuan, K., Zhu, G., & Jiao, L. (2024). Collaborative analytics-enhanced reflective assessment to foster conducive epistemic emotions in knowledge building. Computers & Education, 209, 104950. https://doi.org/10.1016/j.compedu.2023.104950
- Ye, J.-m., & Zhou, J. (2022). Exploring the relationship between learning sentiments and cognitive processing in online collaborative learning: A network analytic approach. The Internet and Higher Education, 55, 100875. https://doi.org/10.1016/j.iheduc.2022.100875
- Zheng, L., & Huang, R. (2016). The effects of sentiments and co-regulation on group performance in computer supported collaborative learning. The Internet and Higher Education, 28, 59–67. https://doi.org/10.1016/j.iheduc.2015.10.001