Modelling Temporality in Person- and Variable-Centred Approaches
DOI:
https://doi.org/10.18608/jla.2023.7841Keywords:
temporal analysis, learning analytics, dispositional learning analytics, time, event-based models, research paperAbstract
Learning analytics needs to pay more attention to the temporal aspect of learning processes, especially in self-regulated learning (SRL) research. In doing so, learning analytics models should incorporate both the duration and frequency of learning activities, the passage of time, and the temporal order of learning activities. However, where this exhortation is widely supported, there is less agreement on its consequences. Does paying tribute to temporal aspects of learning processes necessarily imply that event-based models are to replace variable-based models, and analytic discovery methods substitute traditional statistical methods? We do not necessarily require such a paradigm shift to give temporal aspects their position. First, temporal aspects can be integrated into variable-based models that apply statistical methods by carefully choosing appropriate time windows and granularity levels. Second, in addressing temporality in learning analytic models that describe authentic learning settings, heterogeneity is of crucial importance in both variable- and event-based models. Variable-based person-centred modelling, where a heterogeneous sample is split into homogeneous subsamples, is suggested as a solution. Our conjecture is illustrated by an application of dispositional learning analytics, describing authentic learning processes over an eight-week full module of 2,360 students.
References
Chen, B., Knight, S., & Wise, A. F. (2018). Critical issues in designing and implementing temporal analytics. Journal of Learning Analytics, 5(1), 1–9. https://doi.org/10.18608/jla.2018.53.1
Han, F., Pardo, A., & Ellis, R. A. (2020). Students’ self-report and observed learning orientations in blended university course design: How are they related to each other and to academic performance? Journal of Computer Assisted Learning, 36(6), 969–980. https://doi.org/10.1111/jcal.12453
Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16, 235–266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
Howard, M. C., & Hoffman, M. E. (2018). Variable-centred, person-centred, and person-specific approaches: Where theory meets the method. Organizational Research Methods, 21(4) 846–876. https://doi.org/10.1177/1094428117744021
Knight, S., Wise, A. F., & Chen, B. (2017). Time for change: Why learning analytics needs temporal analysis. Journal of Learning Analytics, 4(3), 7–17. https://doi.org/10.18608/jla.2017.43.2
Loyens, S. M. M., Gijbels, D., Coertjens, L., & Coté, D. J. (2013). Students’ approaches to learning in problem-based learning: Taking into account professional behavior in the tutorial groups, self-study time, and different assessment aspects. Studies in Educational Evaluation, 39(1), 23–32. https://doi.org/10.1016/j.stueduc.2012.10.004
Malcom-Piqueux, L. (2015). Application of person-centred approaches to critical quantitative research: Exploring inequities in college financing strategies. New Directions for Institutional Research, 2014(163), 59–73. https://doi.org/10.1002/ir.20086
Martin, A. J. (2007). Examining a multidimensional model of student motivation and engagement using a construct validation approach. British Journal of Educational Psychology, 77(2), 413–440. https://doi.org/10.1348/000709906X118036
Molenaar, I. (2014). Advances in temporal analysis in learning and instruction. Frontline Learning Research, 2(4), 15–24. https://doi.org/10.14786/flr.v2i4.118
Molenaar, I., de Mooij, S., Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540. https://doi.org/10.1016/j.chb.2022.107540
Molenaar, I., & Wise, A. F. (2022). Temporal aspects of learning analytics: Grounding analyses in concepts of time. In C. Lang, G. Siemens, A. F. Wise, D. Gašević, & A. Merceron (Eds.), The Handbook of Learning Analytics, 2nd ed. (pp. 66–76). SoLAR.
Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research and Perspectives, 2(4), 201–218. https://doi.org/10.1207/s15366359mea0204_1
Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18(2), 112–117. https://doi.org/10.1111/j.1467-8721.2009.01619.x
Nguyen, Q., Huptych, M., & Rienties, B. (2018). Using temporal analytics to detect inconsistencies between learning design and students’ behaviours. Journal of Learning Analytics, 5(3), 120–135. https://doi.org/10.18608/jla.2018.53.8
Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, F., & Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76, 703–714. https://doi.org/10.1016/j.chb.2017.03.028
Nguyen, Q., Tempelaar, D., Rienties, B., & Giesbers, B. (2016). What learning analytics based prediction models tell us about feedback preferences of students. Quarterly Review of Distance Education, 17, 13–33.
Pardo, A., Han, F., & Ellis, R. A. (2017). Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Transactions on Learning Technologies, 10(1), 82–92. https://doi.org/10.1109/TLT.2016.2639508
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341. https://doi.org/10.1007/s10648-006-9029-9
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. http://doi.org/10.1016/j.cedpsych.2010.10.002
Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4, 239–257. https://doi.org/10.1007/s11412-009-9070-z
Reimann, P., Markauskaite, L., & Bannert, M. (2014). e-Research and learning theory: What do sequence and process mining methods contribute? British Journal of Educational Technology, 45(3), 528–540. https://doi.org/10.1111/bjet.12146
Rienties, B., Tempelaar, D., Nguyen, Q., & Littlejohn, A. (2019). Unpacking the intertemporal impact of self-regulation in a blended mathematics environment. Computers in Human Behavior, 100, 345–357. https://doi.org/10.1016/j.chb.2019.07.007
Rizvi, S., Rienties, B., Rogaten, J., & Kizilcec, R. F. (2022). Beyond one-size-fits-all in MOOCs: Variation in learning design and persistence of learners in different cultural and socioeconomic contexts. Computers in Human Behavior, 126, 106973. https://doi.org/10.1016/j.chb.2021.106973
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, 100060. https://doi.org/10.1016/j.caeai.2022.100060
Tempelaar, D. T., Gijselaers, W. H., van der Loeff, S. S., & Nijhuis, J. F. H., (2007). A structural equation model analyzing the relationship of student achievement motivations and personality factors in a range of academic subject-matter areas. Contemporary Educational Psychology, 32(1), 105–131. https://doi.org/10.1016/j.cedpsych.2006.10.004
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. https://doi.org/10.1016/j.chb.2014.05.038
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2017). Towards actionable learning analytics using dispositions. IEEE Transactions on Learning Technologies, 10(1), 6–16. https://doi.org/10.1109/TLT.2017.2662679
Tempelaar, D., Nguyen, Q., & Rienties, B. (2020a). Learning analytics and the measurement of learning engagement. In D. Ifenthaler & D. Gibson (Eds.), Adoption of Data Analytics in Higher Education Learning and Teaching (pp. 159–176). Springer. https://doi.org/10.1007/978-3-030-47392-1_9
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2020b). Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analytics. Applied Cognitive Psychology, 34(4), 890–905. https://doi.org/10.1002/acp.3652
Tempelaar, D., Rienties, B., & Nguyen, Q. (2021). Dispositional learning analytics for supporting individualized learning feedback. Frontiers in Education, 6, 703773. https://doi.org/10.3389/feduc.2021.703773
Uzir, N. A., Gašević, D., Jovanović, J., Matcha, W., Lim, L.-A., & Fudge, A. (2020). Analytics of time management and learning strategies for effective online learning in blended environments. Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK ’20), 23–27 March 2020, Frankfurt, Germany (pp. 392–401). ACM Press. https://doi.org/10.1145/3375462.3375493
Uzir, N. A., 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. Proceedings of the 14th European Conference on Technology Enhanced Learning (EC-TEL 2019), 16–19 September 2019, Delft, The Netherlands. (Lecture Notes in Computer Science, vol. 11722, pp. 555–569). Springer. https://doi.org/10.1007/978-3-030-29736-7_41
Vermunt, J. D. (1996). Metacognitive, cognitive and affective aspects of learning styles and strategies: A phenomenographic analysis. Higher Education, 31, 25–50. https://doi.org/10.1007/BF00129106
Voelkle, M. C., Brose, A., Schmiedek, F., & Lindenberger, U. (2014). Toward a unified framework for the study of between-person and within-person structures: Building a bridge between two research paradigms. Multivariate Behavioral Research, 49(3), 193–213. https://doi.org/10.1080/00273171.2014.889593
Zamecnik, A., Kovanović, V., Joksimović, S., & Liu, L. (2022). Exploring non-traditional learner motivations and characteristics in online learning: A learner profile study. Computers and Education: Artificial Intelligence, 3, 100051. https://doi.org/10.1016/j.caeai.2022.100051
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