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For decades, self-report instruments – which rely heavily on students’ perceptions and beliefs – have been the dominant way of measuring motivation and strategy use. An event-based measure based on online trace data arguably has the potential to remove analytical restrictions of self-report measures. The purpose of this study is therefore to triangulate constructs suggested in theory and measured using self-reported data with revealed online traces of learning behaviour. The results show that online trace data of learning behaviour are complementary to self-reports, as they explained a unique proportion of variance in student academic performance and reveal that self-reports explain more variance in online learning behaviour of prior weeks than variance in learning behaviour in succeeding weeks. Student motivation is, however, to a lesser extent captured with online trace data, likely because of its covert nature. In that respect, it is of importance to recognize the crucial role of self-reports in capturing student learning holistically. This manuscript is ‘frontline’ in the sense that event-based measurement methodologies using online trace data are relatively unexplored. The comparison with self-report data made in this manuscript sheds new light on the added value of innovative and traditional methods of measuring motivation and strategy use.
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Bera, S., & Liu, M. (2006). Cognitive tools, individual differences, and group processing as mediating factors in a hypermedia environment. Computers in Human Behavior, 22, 295-319. http://doi.org/10.1016/j.chb.2004.05.001
Boekaerts, M., & Corno, L. (2005). Self‐regulation in the classroom: A perspective on assessment and intervention. Applied Psychology, 54, 199-231. http://doi.org/10.1016/j.chb.2004.05.001
Boekaerts, M., Pintrich, P. R., & Zeidner, M. (2000). Self-regulation: An introductory overview. In Handbook of self-regulation (pp. 1-9). Academic Press.
Charleer, S., Moere, A. V., Klerkx, J., Verbert, K., & De Laet, T. (2017). Learning analytics dashboards to support adviser-student dialogue. IEEE Transactions on Learning Technologies, 11, 389-399. http://doi.org/10.1109/TLT.2017.2720670
Cho, M. H., & Heron, M. L. (2015). Self-regulated learning: the role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course. Distance Education, 36, 80-99. http://doi.org/10.1080/01587919.2015.1019963
Cho, M. H., & Yoo, J. S. (2017). Exploring online students’ self-regulated learning with self-reported surveys and log files: a data mining approach. Interactive Learning Environments, 25, 970-982. http://doi.org/10.1080/10494820.2016.1232278
Coogan, J., Dancey, C. P., & Attree, E. A. (2005). WebCT: a useful support tool for psychology undergraduates–a Q methodological study. Psychology Learning and Teaching, 5, 61-66. http://doi.org/10.2304/plat.2005.5.1.61
Cornelisz, I., & Van Klaveren, C. (2018). Student engagement with computerized practising: Ability, task value, and difficulty perceptions. Journal of Computer Assisted Learning, 34(6), 828-842. http://doi.org/10.1111/jcal.12292
DeNeui, D. L., & Dodge, T. L. (2006). Asynchronous learning networks and student outcomes: The utility of online learning components in hybrid courses. Journal of Instructional Psychology, 33, 256-260.
Dinsmore, D. L., Alexander, P. A., & Loughlin, S. M. (2008). Focusing the conceptual lens on metacognition, self-regulation, and self-regulated learning. Educational Psychology Review, 20, 391-409. http://doi.org/10.1007/s10648-008-9083-6
Greller, W. & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of Educational Technology & Society, 15, 42-57. Retrieved from http://www.jstor.org/stable/jeductechsoci.15.3.42
Chauliac, M., Catrysse, L., Gijbels, D., & Donche, V. (2020). It is all in the surv-eye: Can eye tracking data shed light on the internal consistency in self-report questionnaires on cognitive processing strategies? Frontline Learning Research, 8(3), 26–39. http://doi.org/10.14786/flr.v8i3.489
Duncan, T. G., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational psychologist, 40, 117-128. http://doi.org/10.1207/s15326985ep4002_6
Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46, 6-25. http://doi.org/10.1080/00461520.2011.538645
Ellis, R. A., Marcus, G., & Taylor, R. (2005). Learning through inquiry: student difficulties with online course‐based Material. Journal of Computer Assisted Learning, 21, 239-252. http://doi.org/10.1111/j.1365-2729.2005.00131.x.
Entwistle, N., & McCune, V. (2004). The conceptual bases of study strategy inventories. Educational Psychology Review, 16, 325-345. http://doi.org/10.1007/s10648-004-0003-0
Fryer, L. K. (2017). Building bridges: Seeking structure and direction for higher education motivated learning strategy models. Educational Psychology Review, 29, 325-344. http://doi.org/10.1007/s10648-017-9405-7
Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84. http://doi.org/10.1016/j.iheduc.2015.10.002.
Gašević, D., Kovanovic, V., Joksimovic, S., & Siemens, G. (2014). Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. The International Review of Research in Open and Distributed Learning, 15, 134-176. http://doi.org/10.19173/irrodl.v15i5.1954.
Guerra, J., Hosseini, R., Somyurek, S., & Brusilovsky, P. (2016). An intelligent interface for learning content: Combining an open learner model and social comparison to support self-regulated learning and engagement. In Proceedings of the 21st international conference on intelligent user interfaces (pp. 152-163). ACM.
Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2(2-3), 107-124. http://doi.org/10.1007/s11409-007-9016-7
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Heffner, M., & Cohen, S. H. (2005). Evaluating student use of web-based course material. Journal of Instructional Psychology, 32, 74-82.
Hilpert, J. C., Stempien, J., van der Hoeven Kraft, K. J., & Husman, J. (2013). Evidence for the latent factor structure of the MSLQ: A new conceptualization of an established questionnaire. SAGE Open, 3, 2158244013510305.
Hoskins, S. L., & Van Hooff, J. C. (2005). Motivation and ability: which students use online learning and what influence does it have on their achievement? British Journal of Educational Technology, 36, 177-192. http://doi.org/10.1111/j.1467-8535.2005.00451.x.
Howard-Rose, D., & Winne, P. H. (1993). Measuring component and sets of cognitive processes in self-regulated learning. Journal of Educational Psychology, 85, 591. http://doi.org/10.1111/j.1464-0597.2005.00205.x
Huon, G., Spehar, B., Adam, P., & Rifkin, W. (2007). Resource use and academic performance among first year psychology students. Higher Education, 53, 1-27. http://doi.org/10.1007/s10734-005-1727-6
Iaconelli, R., & Wolters, C. A. (2020). Insufficient effort responding in surveys assessing self-regulated learning: Nuisance or fatal flaw? Frontline Learning Research, 8(3), 105–127. http://doi.org/10.14786/flr.v8i3.521
Itmazi, J. A., & Megías, M. G. (2005). Survey: Comparison and evaluation studies of learning content management systems. Unpublished manuscript.
Jamieson-Noel, D., & Winne, P. H. (2003). Comparing Self-Reports to Traces of Studying Behavior as Representations of Students' Studying and Achievement. Zeitschrift für Pädagogische Psychologie/German Journal of Educational Psychology. http://doi.org/10.1024//1010-06220.127.116.11
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough. Pitfalls of learning analytics dashboards in the educational practice. In É. L., H. D., K. V., J. B., & M. P-S. (Eds.), Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings (Lecture Notes in Computer Science (LNCS); Vol. 10474). Cham: Springer International Publishing AG. http://doi.org/10.1007/978-3-319-66610-5_7
Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate: preparing learning analytics dashboards for educational practice. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 31-40). ACM. http://doi.org/10.1145/3170358.3170421
Jiang, L., Elen, J., & Clarebout, G. (2009). The relationships between learner variables, tool-usage behaviour and performance. Computers in Human Behavior, 25, 501-509. http://doi.org/10.1016/j.chb.2008.12.010
Jupp, V. (2006). The Sage dictionary of social research methods. Sage.
Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea. Computers & Education, 127, 233-251. http://doi.org/10.1016/j.compedu.2018.08.023.
Knight, J. (2010). Distinguishing the learning approaches adopted by undergraduates in their use of online resources. Active Learning in Higher Education, 11, 67–76. http://doi.org/10.1177/1469787409355873
Lust, G., Collazo, N. A. J., Elen, J., & Clarebout, G. (2012). Content management systems: enriched learning opportunities for all? Computers in Human Behavior, 28, 795-808. http://doi.org/10.1016/j.chb.2011.12.009
Lust, G., Elen, J., & Clarebout, G. (2013). Regulation of tool-use within a blended course: Student differences and performance effects. Computers & Education, 60, 385-395. http://doi.org/10.1016/j.compedu.2012.09.001
McKeachie, W. J., Pintrich, P. R., & Lin, Y. G. (1985). Teaching learning strategies. Educational Psychologist, 20, 153-160. http://doi.org/10.1207/s15326985ep2003_5
Molenda, M. (2008). Historical foundations. In M. J. Spector, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll (Eds.). Handbook of research for educational communications and technology (pp. 5–20). Routledge.
Moeller, J., Dietrich, J., Viljaranta, J., & Kracke, B. (2020). Disentangling objective characteristics of learning situations from subjective perceptions thereof, using an experience sampling method design. Frontline Learning Research, 8(3), 63–85. http://doi.org/10.14786/flr.v8i3.529
Muis, K. R., Winne, P. H., & Jamieson-Noel, D. (2007). Using a multitrait-multimethod analysis to examine conceptual similarities of three self-regulated learning inventories. British Journal of Educational Psychology, 77, 177–195. http://doi.org/10.1348/000709905X90876
Munoz, KD, & Van Duzer, J. (2005). BlackBoard vs. Moodle: A Comparison of Satisfaction with Online Teaching and Learning Tools. Humboldt State University.
Panadero, E. (2017). A review of self-regulated learning: six models and four directions for research. Frontiers in Psychology, 8, 422. http://doi.org/10.3389/fpsyg.2017.00422
Panadero, E., Klug, J., & Järvelä, S. (2016). Third wave of measurement in the self-regulated learning field: when measurement and intervention come hand in hand. Scandinavian Journal of Educational Research, 60, 723-735. http://doi.org/10.1080/00313831.2015.1066436
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92, 544. http://doi.org/10.1037/0022-0618.104.22.1684
Pintrich, P. R., McKeachie, W. J., & Lin, Y. G. (1987). Teaching a course in learning to learn. Teaching of Psychology, 14, 81-86. http://doi.org/10.1207/s15328023top1402_3
Rogiers, A.; Merchie, E., & van Keer, H. (2020). Opening the black box of students’ text-learning processes: A process mining perspective. Frontline Learning Research, 8(3), 40–62. http://doi.org/10.14786/flr.v8i3.527
Schmitz, M., Scheffel, M., van Limbeek, E., van Halem, N., Cornelisz, I., van Klaveren, C., ... & Drachsler, H. (2018). Investigating the Relationships Between Online Activity, Learning Strategies and Grades to Create Learning Analytics-Supported Learning Designs. In European Conference on Technology Enhanced Learning (pp. 311-325). Springer, Cham.
Taylor, S. E. & Brown, J. D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychological Bulletin, 103, 193–210. http://doi.org/10.1037/0033-2909.103.
Tock, J. L., & Moxley, J. H. (2017). A comprehensive reanalysis of the metacognitive self-regulation scale from the MSLQ. Metacognition and Learning, 12, 79-111. http://doi.org/10.1007/s11409-016-9161-y
Veenman, M. (2011). Learning to self-monitor and self-regulate. In R. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction (pp. 197–218). New York: Routledge.
Vriesema, C. C., & McCaslin, M. (2020) Experience and meaning in small-group contexts: Fusing observational and self-report data to capture self and other dynamics. Frontline Learning Research, 8(3), 128–141. http://doi.org/10.14786/flr.v8i3.493
Wang, A. Y., & Newlin, M. H. (2000). Characteristics of students who enroll and succeed in psychology web-based classes. Journal of Educational Psychology, 92, 137. http://doi.org/10.1037/0022-0622.214.171.124.
Winne, P. H., & Baker, R. S. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5, 1-8. Retrieved from https://jedm.educationaldatamining.org/index.php/JEDM/article/view/28
Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation. Academic Press.
Zhou, M., & Winne, P. H. (2012). Modelling academic performance by self-reported versus traced goal orientation. Learning and Instruction, 22, 413-419. http://doi.org//j.learninstruc.2012.03.004
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45, 166–183. http://doi.org/10.3102/0002831207312909