The Exploration of Student Emotion Experience and Learning Experience in E-learning Platform
Abstract
Previous studies have shown that emotion is crucial in student learning. However, most studies in the e-learning environment have yet to consider emotion as part of learning that could lead to successful learning. Thus, this study explored the relationship between student emotion state, emotion sequences, and student learning experience. A preliminary data collection was conducted to explore the relationship between emotional experience and student learning experience, which involved 16 students. Students were asked to learn a programming subject in an e-learning environment. E-learning is designed to store the students' emotional experience and activity during learning. The sequential pattern mining technique was used to extract the data, exploratory data analysis was conducted to visualize the emotional trajectory during the learning process, and regression analysis was used to explain the relationship between students' emotional learning experiences. The results showed that emotional experience might affect student experience in learning. In one-sequence emotion, all emotion states contributed to the learning experience with p-values < 0.01 except for neutral and disgust with p-values < 0.05. The one-sequence emotion model shows R-squared = 0.585; Adj. R-squared = 0.734; F-statistic = 6.920; Prob (F-statistic) = 0.00702. Meanwhile, in two-sequence emotion, none of the emotion sequences contributed to the student learning experience. Lastly, three-sequence emotion models also showed that most sequences did not influence student learning experience. The only sequence of emotions that influenced the student learning experience was surprise-neutral-surprise. These results suggest that emotion should be considered in learning design as it can influence student experience.
References
E. Calcagni and L. Lago, “The three domains for dialogue: A framework for analysing dialogic approaches to teaching and learning,” Learn. Cult. Soc. Interact., vol. 18, pp. 1–12, Sep. 2018, doi: 10.1016/j.lcsi.2018.03.001.
A. Chanaa and N. El Faddouli, “An analysis of learners’ affective and cognitive traits in context-aware recommender systems (CARS) using feature interactions and factorization machines (FMs),” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 4796–4809, Sep. 2022, doi: 10.1016/j.jksuci.2021.06.008.
K.R. Salim, M. Puteh, and S.M. Daud, “Assessing students’ practical skills in basic electronic laboratory based on psychomotor domain model,” in Procedia - Soc. Behav. Sci., 2012, pp. 546–555, doi: 10.1016/j.sbspro.2012.09.687.
W.S. Lee, “An experimental investigation into the application of a learning-from-mistakes approach among freshmen students,” Sage Open, vol. 10, no. 2, pp. 1–10, Apr. 2020, doi: 10.1177/2158244020931938.
S. D’Mello and A. Graesser, “Dynamics of affective states during complex learning,” Learn. Instr., vol. 22, no. 2, pp. 145–157, Apr. 2012, doi: 10.1016/j.learninstruc.2011.10.001.
M. Carmona-Halty, M. Salanova, S. Llorens, and W.B. Schaufeli, “Linking positive emotions and academic performance: The mediated role of academic psychological capital and academic engagement,” Curr. Psychol., vol. 40, no. 6, pp. 2938–2947, Jun. 2021, doi: 10.1007/s12144-019-00227-8.
J. Camacho-Morles et al., “Activity achievement emotions and academic performance: A meta-analysis,” Educ. Psychol. Rev., vol. 33, no. 3, pp. 1051–1095, Sep. 2021, doi: 10.1007/s10648-020-09585-3.
B. Kort, R. Reilly, and R.W. Picard, “An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion,” in Proc. IEEE Int. Conf. Adv. Learn. Technol., 2001, pp 43–46, doi: 10.1109/icalt.2001.943850.
J.M. Lodge et al., “Understanding difficulties and resulting confusion in learning: An integrative review,” Front. Educ., vol. 3, pp. 1–10, Jun. 2018, doi: 10.3389/feduc.2018.00049.
S. D’Mello, B. Lehman, R. Pekrun, and A. Graesser, “Confusion can be beneficial for learning,” Learn. Instr., vol. 29, pp. 153–170, Feb. 2014, doi: 10.1016/j.learninstruc.2012.05.003.
A.Y. Kolb, D.A. Kolb, A. Passarelli, and G. Sharma, “On becoming an experiential educator: The educator role profile,” Simul. Gaming, vol. 45, no. 2, pp. 204–234, Apr. 2014, doi: 10.1177/1046878114534383.
J.C. Richards, “Exploring emotions in language teaching”, RELC J., vol. 53, no. 1, pp. 225–239, Apr. 2022, doi: 10.1177/0033688220927531.
A. Gupta, A. D’Cunha, K. Awasthi, and V. Balasubramanian, “DAiSEE: Towards user engagement recognition in the wild,” 2022, arXiv:1609.01885.
S. Lane, J.G. Hoang, J.P. Leighton, and A. Rissanen, “Engagement and satisfaction: Mixed-method analysis of blended learning in the sciences,” Can. J. Sci. Math. Technol. Educ., vol. 21, no. 1, pp. 100–122, Mar. 2021, doi: 10.1007/s42330-021-00139-5.
J. Hilliard, K. Kear, H. Donelan, and C. Heaney, “Students’ experiences of anxiety in an assessed, online, collaborative project,” Comput. Educ., vol. 143, pp. 1–15, Jan. 2020, doi: 10.1016/j.compedu.2019.103675.
M. Stephan, S. Markus, and M. Gläser-Zikuda, “Students’ achievement emotions and online learning in teacher education,” Front. Educ., vol. 4, pp. 1–12, Oct. 2019, doi: 10.3389/feduc.2019.00109.
S.B. Gupta and M. Gupta, “Technology and e-learning in higher education,” Int. J. Adv. Sci. Technol., vol. 29, no. 4, pp. 1320–1325, Feb. 2020.
F. Gjermeni and B. Percinkova, “Combining intelligent algorithms and e-learning styles to create an improved intelligent system in evaluating an e-learning student's profile,” Anglisticum: Int. J. Lit. Linguist. Interdiscip. Stud., vol. 7, no. 2, pp. 11–21, Feb. 2018, doi: 10.5281/zenodo.1186399.
R. Wu and Z. Yu, “Exploring the effects of achievement emotions on online learning outcomes: A systematic review,” Front. Psychol., vol. 13, pp. 1–15, Sep. 2022, doi: 10.3389/fpsyg.2022.977931.
J.L. Plass and S. Kalyuga, “Four ways of considering emotion in cognitive load theory,” Educ. Psychol. Rev., vol. 31, no. 2, pp. 339–359, Jun. 2019, doi: 10.1007/s10648-019-09473-5.
M. Jiang and K. Koo, “Emotional presence in building an online learning community among non-traditional graduate students,” Online Learn. J., vol. 24, no. 4, pp. 93–111, Dec. 2020, doi: 10.24059/olj.v24i4.2307.
J. Hill, R.L. Healey, H. West, and C. Déry, “Pedagogic partnership in higher education: Encountering emotion in learning and enhancing student wellbeing,” J. Geogr. High. Educ., vol. 45, no. 2, pp. 167–185, May 2021, doi: 10.1080/03098265.2019.1661366.
O. Tapalova and N. Zhiyenbayeva, “Artificial intelligence in education: AIED for personalised learning pathways,” Electron. J. e-Learn., vol. 20, no. 5, pp. 639–653, Dec. 2022, doi: 10.34190/ejel.20.5.2597.
K. Zhang et al., “Detecting faces using inside cascaded contextual CNN,” in 2017 IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 3190–3198, doi: 10.1109/ICCV.2017.344.
B.A. Heiman and M. Beringer, “Mobile device-based offers: Determinants of consumer response in sophisticated (extreme) users,” Int. J. Comput. Appl., pp. 129–140, 2010.
A.P.M.D. Rosa, L.M.M. Villanueva, J.M.R.S. Miguel, and J.E.B. Quinto, “Web-based database courses e-learning application,” Int. J. Comput. Sci. Res., vol. 7, pp. 1531–1543, Jan. 2023, doi: 10.25147/ijcsr.2017.001.1.115.
B. Frost. “Atomic design methodology.” Access date: 31 May 2024. [Online]. Available: http://atomicdesign.bradfrost.com
T.H. Park, B. Dorn, and A. Forte, “An analysis of HTML and CSS syntax errors in a web development course,” ACM Trans. Comput. Educ., vol. 15, no. 1, pp. 1–21, Mar. 2015, doi: 10.1145/2700514.
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