Exploration of the online learners’ actions: A sequence mining approach
Rusada Natthaphatwirata 1,
Wannisa Matcha 2 * More Detail
1 Faculty of Education, Prince of Songkla University Pattani Campus, Pattani, THAILAND
2 Faculty of Communication Sciences, Prince of Songkla University Pattani Campus, Pattani, THAILAND
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 14, Issue 4, Article No: e202446.
https://doi.org/10.30935/ojcmt/14957
OPEN ACCESS 246 Views 116 Downloads Published online: 16 Aug 2024
ABSTRACT
This paper presents the exploration of the learners’ learning engagement in a self-paced massive open online course (MOOC). Research often claims that engagement contributes to learning success. However, there is still limited understanding of engagement and its characteristics. This research aims to fulfil this gap by exploring how different patterns detected based on the density levels of engagement contribute to learning performance. A total number of 159,804 records of trace data from 971 learners who enrolled in a self-paced MOOC were used in this study. The sequence mining technique was used to formulate the sequence of learning engagement. Hierarchical clustering was then used to automate the pattern recognition of the formulated sequences. As a result, four groups of learners were detected based on a similar pattern of engagement levels. Sequence mining was then used to examine the learning engagement pattern. The Kruskal-Wallis test was used to examine the statistically significant differences in terms of final scores among the detected groups. The results revealed two successful groups of learners with different patterns of engagement and two unsuccessful groups. Successful learners are intensively engaged in learning activities in the short and long run, whereas unsuccessful groups tend to be less engaged. This paper extends the previous exploration of the engagement. That is, the level identified based on the density of interactive engagement as recorded in the system can be used to determine the learning patterns, consequently, reflective of individual’s learning profiles. It has a significant association with academic performance.
CITATION
Natthaphatwirata, R., & Matcha, W. (2024). Exploration of the online learners’ actions: A sequence mining approach.
Online Journal of Communication and Media Technologies, 14(4), e202446.
https://doi.org/10.30935/ojcmt/14957
REFERENCES
- Ahmad Uzir, N., Gašević, D., Matcha, W., Jovanović, J., & Pardo, A. (2019). Analytics of time management strategies in a flipped classroom. Journal of Computer Assisted Learning, 36(1), 70–88. https://doi.org/10.1111/jcal.12392
- Ahmad Uzir, N., Gašević, D., Matcha, W., Jovanović, J., Pardo, A., Lim, L.-A., & Gentili, S. (2019). Discovering time management strategies in learning processes using process mining techniques. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou, & J. Schneider (Eds.), Transforming learning with meaningful technologies (pp. 555–569). Springer. https://doi.org/10.1007/978-3-030-29736-7_41
- Akhuseyinoglu, K., & Brusilovsky, P. (2022). Exploring behavioral patterns for data-driven modeling of learners’ individual differences. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.807320
- Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84–94. https://doi.org/10.1080/00461520.2015.1004069
- Biggs, J. B. (1987). Student approaches to learning and studying. Australian Council for Educational Research.
- Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17, Article 2. https://doi.org/10.1186/s41239-019-0176-8
- Diseth, Å., & Martinsen, Ø. (2003). Approaches to learning, cognitive style, and motives as predictors of academic achievement. Educational Psychology, 23(2), 195–207. https://doi.org/10.1080/01443410303225
- Dunlosky, J. (2013). Strengthening the student toolbox. American Educator, 37(3), 12–21.
- Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266
- Eriksson, T., Adawi, T., & Stöhr, C. (2017). “Time is the bottleneck”: A qualitative study exploring why learners drop out of MOOCs. Journal of Computing in Higher Education, 29(1), 133–146. https://doi.org/10.1007/s12528-016-9127-8
- Fan, Y., van der Graaf, J., Lim, L., Raković, M., Singh, S., Kilgour, J., Moore, J., Molenaar, I., Bannert, M., & Gašević, D. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17, 949–987. https://doi.org/10.1007/s11409-022-09291-1
- Fincham, O. E., Gasevic, D. V., Jovanovic, J. M., & Pardo, A. (2018). From study tactics to learning strategies: An analytical method for extracting interpretable representations. IEEE Transactions on Learning Technologies, 12(1), 59–72. https://doi.org/10.1109/TLT.2018.2823317
- Froiland, J. M., & Worrell, F. C. (2016). Intrinsic motivation, learning goals, engagement, and achievement in a diverse high school. Psychology in the Schools, 53(3), 321–336. https://doi.org/10.1002/pits.21901
- Gabadinho, A., Ritschard, G., Mueller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1–37. https://doi.org/10.18637/jss.v040.i04
- Gabadinho, A., Ritschard, G., Studer, M., & Muller, N. S. (2008). Mining sequence data in R with the TraMineR package: A user’s guide. http://mephisto.unige.ch/pub/TraMineR/doc/TraMineR-Users-Guide.pdf
- Gillett-Swan, J. (2017). The challenges of online learning: Supporting and engaging the isolated learner. Journal of Learning Design, 10(1), Article 20. https://doi.org/10.5204/jld.v9i3.293
- Guo, P. J., & Reinecke, K. (2014). Demographic differences in how students navigate through MOOCs. In Proceedings of the 1st ACM Conference on Learning @ Scale Conference (pp. 21–30). ACM. https://doi.org/10.1145/2556325.2566247
- Hew, K. F. (2016). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), 320–341. https://doi.org/10.1111/bjet.12235
- Ikeda, K. (2022). How beliefs explain the effect of achievement goals on judgments of learning. Metacognition and Learning, 17, 499–530. https://doi.org/10.1007/s11409-022-09294-y
- Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A. (2021). What multimodal data can tell us about the students’ regulation of their learning process? Learning and Instruction, 72, Article 101203. https://doi.org/10.1016/j.learninstruc.2019.04.004
- Jovanovic, J., Gasevic, D., Dawson, S., Pardo, A., & Mirriahi, N. (2017). Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33, 74–85. https://doi.org/10.1016/j.iheduc.2017.02.001
- Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. (2016). Recommending self-regulated learning strategies does not work (in MOOC context). In Proceedings of the 3rd ACM Conference on Learning @Scale (pp. 101–104). ACM. https://doi.org/10.1145/2876034.2893378
- Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online course. In Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (pp. 170–179). https://doi.org/10.1145/2460296.2460330
- Lust, G., Elen, J., & Clarebout, G. (2013). Students’ tool-use within a web enhanced course: Explanatory mechanisms of students’ tool-use pattern. Computers in Human Behavior, 29(5), 2013–2021. https://doi.org/10.1016/j.chb.2013.03.014
- Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., & Munoz-Gama, J. (2018a). Mining theory-based patterns from big data: Identifying self-regulated learning strategies in massive open online courses. Computers in Human Behavior, 80, 179–196. https://doi.org/10.1016/j.chb.2017.11.011
- Maldonado-Mahauad, J., Pérez-Sanagustín, M., Moreno-Marcos, P. M., Alario-Hoyos, C., Merino, P., & Delgado-Kloos, C. (2018b). Predicting learners’ success in a self-paced MOOC through sequence patterns of self-regulated learning. In Proceedings of the 13th European Conference on Technology Enhanced Learning (pp. 355–369). https://doi.org/10.1007/978-3-319-98572-5_27
- Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning Journal, 22(1), 205–222. https://doi.org/10.24059/olj.v22i1.1092
- Matcha, W., Gašević, D., Ahmad Uzir, N., Jovanović, J., & Pardo, A. (2019). Analytics of learning strategies: Associations with academic performance and feedback. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 461–470). https://doi.org/10.1145/3303772.3303787
- Ogunyemi, A. A., Quaicoe, J. S., & Bauters, M. (2022). Indicators for enhancing learners’ engagement in massive open online courses: A systematic review. Computers and Education Open, 3, Article 100088. https://doi.org/10.1016/j.caeo.2022.100088
- Olivier, E., Archambault, I., De Clercq, M., & Galand, B. (2019). Student self-efficacy, classroom engagement, and academic achievement: Comparing three theoretical frameworks. Journal of Youth and Adolescence, 48(2), 326–340. https://doi.org/10.1007/s10964-018-0952-0
- Paulsen, J., & McCormick, A. C. (2020). Reassessing disparities in online learner student engagement in higher education. Educational Researcher, 49(1), 20–29. https://doi.org/10.3102/0013189X19898690
- Reich, J., & Ruipérez-Valiente, J. A. (2019). The MOOC pivot. Science, 363(6423), 130–131. https://doi.org/10.1126/science.aav7958
- Saint, J., Fan, Y., Gašević, D., & Pardo, A. (2022). Temporally-focused analytics of self-regulated learning: A systematic review of literature. Computers and Education: Artificial Intelligence, 3, Article 100060. https://doi.org/10.1016/j.caeai.2022.100060
- Salas- Pilco, S. Z., Yang, Y., & Zhang, Z. (2022). Student engagement in online learning in Latin American higher education during the COVID-19 pandemic: A systematic review. British Journal of Educational Technology, 53(3), 593–619. https://doi.org/10.1111/bjet.13190
- Saqr, M., López-Pernas, S., Helske, S., & Hrastinski, S. (2023). The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: A full program study. Computers and Education, 199, Article 104787. https://doi.org/10.1016/j.compedu.2023.104787
- Schnitzler, K., Holzberger, D., & Seidel, T. (2021). All better than being disengaged: Student engagement patterns and their relations to academic self-concept and achievement. European Journal of Psychology of Education, 36(3), 627–652. https://doi.org/10.1007/s10212-020-00500-6
- Srivastava, N., Fan, Y., Rakovic, M., Singh, S., Jovanovic, J., Van Der Graaf, J., Lim, L., Surendrannair, S., Kilgour, J., Molenaar, I., Bannert, M., Moore, J., & Gasevic, D. (2022). Effects of internal and external conditions on strategies of self-regulated learning: A learning analytics study. In Proceedings of the 12th International Learning Analytics and Knowledge Conference (pp. 392–403). ACM. https://doi.org/10.1145/3506860.3506972
- van den Beemt, A., Buys, J., & van der Aalst, W. (2018). Analysing structured learning behaviour in massive open online courses (MOOCs): An approach based on process mining and clustering. International Review of Research in Open and Distance Learning, 19(5), 38–60. https://doi.org/10.19173/irrodl.v19i5.3748
- van Rooij, E. C. M., Jansen, E. P. W. A., & van de Grift, W. J. C. M. (2017). Secondary school students’ engagement profiles and their relationship with academic adjustment and achievement in university. Learning and Individual Differences, 54, 9–19. https://doi.org/10.1016/j.lindif.2017.01.004
- Vilkova, K. (2022). The promises and pitfalls of self-regulated learning interventions in MOOCs. Technology, Knowledge and Learning, 27(3), 689–705. https://doi.org/10.1007/s10758-021-09580-9
- Wong, J., Khalil, M., Baars, M., de Koning, B. B., & Paas, F. (2019). Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course. Computers and Education, 140, Article 103595. https://doi.org/10.1016/j.compedu.2019.103595
- Zhou, M., & Winne, P. H. (2012). Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction, 22(6), 413–419. https://doi.org/10.1016/j.learninstruc.2012.03.004