Exploring online students’ self-regulated learning with self-reported surveys and log files: a data mining approach

References

• Abar, B., & Loken, E. (2010). Self-regulated learning and self-directed study in a pre-college sample. Learning and Individual Differences, 20, 25–29. doi: 10.1016/j.lindif.2009.09.002
   PubMed Web of Science ®Google Scholar
• Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In S. Buckingham Shum, D. Gašević, & R. Ferguson (Eds.), Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). New York, NY: ACM Press.
   Google Scholar
• Artino, A. R. (2007). Online military training: Using a social cognitive view of motivation and self-regulation to understand students’ satisfaction, perceived learning, and choice. The Quarterly Review of Distance Education, 8(3), 191–202.
   Google Scholar
• Artino, A. R., & Stephens, J. M. (2009). Academic motivation and self-regulation: A comparative analysis of undergraduate and graduate students learning online. Internet and Higher Education, 12, 146–151. doi: 10.1016/j.iheduc.2009.02.001
   Web of Science ®Google Scholar
• Baker, R. S. J. d. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International encyclopedia of education (pp. 112–118). Oxford: Elsevier.
   Google Scholar
• Baker, R. S. J. d., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Education Data Mining, 1(1), 3–16.
   Google Scholar
• Black, E. W., Dawson, K., & Priem, J. (2008). Data for free: Using LMS activity logs to measure community in online courses. Internet and Higher Education, 11, 65–70. doi: 10.1016/j.iheduc.2008.03.002
   Web of Science ®Google Scholar
• Carletta, J. (1996). Assessing agreement on classification tasks: The kappa statistic. Computational Linguistics, 22(2), 249–254.
   Web of Science ®Google Scholar
• Castro, F., Vellido, A., Nebot, A., & Mugica, F. (2007). Applying data mining techniques to e-learning problems. In L. C. Jain., R. Tedman, & D. Tedman (Eds.), Evolution of teaching and learning paradigms in intelligent environment: Studies in computational intelligence (pp. 183–221). Berlin: Springer-Verlag.
   Google Scholar
• Chen, S. Y., & Liu, X. (2011). Mining students’ learning patterns and performance in web-based instruction: A cognitive style approach. Interactive Learning Environments, 19(2), 179–192. doi: 10.1080/10494820802667256
   Web of Science ®Google Scholar
• Chen, Y., & Weng, C. (2009). Mining fuzzy association rules from questionnaire data. Knowledge-Based Systems Journal, 22(1), 46–56. doi: 10.1016/j.knosys.2008.06.003
   Web of Science ®Google Scholar
• 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(1), 80–99. doi: 10.1080/01587919.2015.1019963
   Web of Science ®Google Scholar
• Cho, M.-H., & Shen, D. (2013). Self-regulation in online learning. Distance Education, 34(3), 290–301. doi: 10.1080/01587919.2013.835770
   Web of Science ®Google Scholar
• Cho, M.-H., & Summers, J. (2012). Factor validity of the Motivated Strategies for Learning Questionnaire (MSLQ) in asynchronous online learning environments (AOLE). Journal of Interactive Learning Research, 23(1), 5–28.
   Google Scholar
• Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. doi: 10.1080/13562517.2013.827653
   Web of Science ®Google Scholar
• Cohen, A., & Nachmias, R. (2011). What can instructors and policy makers learn about Web-supported learning through Web-usage mining. Internet and Higher Education, 14, 67–76. doi: 10.1016/j.iheduc.2010.07.008
   Web of Science ®Google Scholar
• Dabbagh, N., & Kitsantas, A. (2005). Using web-based pedagogical tools as scaffolds for self-regulated learning. Instructional Science, 33, 513–540. doi: 10.1007/s11251-005-1278-3
   Web of Science ®Google Scholar
• Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. doi: 10.1016/j.patrec.2005.10.010
   Web of Science ®Google Scholar
• Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition Learning, 2, 107–124. doi: 10.1007/s11409-007-9016-7
   Google Scholar
• Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18. doi: 10.1145/1656274.1656278
   Google Scholar
• Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques. Burlington, MA: Morgan Kaufmann.
   Google Scholar
• Howard-Rose, D., & Winne, P. H. (1993). Measuring component and sets of cognitive processes in self-regulated learning. Journal of Educational Psychology, 85(4), 591–604. doi: 10.1037/0022-0663.85.4.591
   Web of Science ®Google Scholar
• Kinnebrew, J. S., & Biswas, G. (2011). Modeling and measuring self-regulated learning in teachable agent environments. Journal of e-Learning and Knowledge Society, 7(2), 19–35.
   Google Scholar
• Koedinger, K. R., Cunningham, K., Skogsholm, A., & Leber, B. (2008). An open repository and analysis tools for fine-grained, longitudinal learner data. In R. S. J. d. Baker, T. Barnes, & J. E. Beck (Eds.). Educational Data Mining 2008: 1st International Conference on Educational Data Mining (pp. 157–166). Montreal, Canada.
   Google Scholar
• Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In C. S. Mellish (Ed.), Proceedings of the 14th international joint conference on artificial intelligence (pp. 1137–1143). Burlington, MA: Morgan Kaufmann.
   Google Scholar
• Macfadyen, L., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for education: A proof of concept. Computers & Education, 54, 588–599. doi: 10.1016/j.compedu.2009.09.008
   Web of Science ®Google Scholar
• MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of Berkeley Symposium on Mathematical Statistics and Probability (pp. 281–297). Berkeley, CA: University of California Press.
   Google Scholar
• Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385–407. doi: 10.1007/s10648-004-0006-x
   Web of Science ®Google Scholar
• Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53, 801–813. doi: 10.1177/0013164493053003024
   Web of Science ®Google Scholar
• Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.
   Google Scholar
• Riding, R. J. (1991). Cognitive styles analysis. Birmingham: Learning and Training Technology.
   Google Scholar
• Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), 601–618. doi: 10.1109/TSMCC.2010.2053532
   Web of Science ®Google Scholar
• Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51, 368–384. doi: 10.1016/j.compedu.2007.05.016
   Web of Science ®Google Scholar
• Romesburg, H. C. (2004). Cluster analysis for researchers. Morrisville, NC: Lulu Press.
   Google Scholar
• Wang, F. H., & Shao, H. M. (2004). Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert System Application, 27, 365–377. doi: 10.1016/j.eswa.2004.05.005
   Web of Science ®Google Scholar
• Winne, P. H., & Baker, R. S. J. d. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1), 1–8.
   Google Scholar
• Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). Orlando, FL: Academic Press.
   Google Scholar
• Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.). San Francisco, CA: Morgan Kaufmann.
   Google Scholar
• Wolters, C., Benzon, M., & Arroyo-Giner, C. (2011). Assessing self-motivational strategies in self-regulated learning. In D. Schunk & B. Zimmerman (Eds.), Handbook of self-regulation of learning and performance (pp. 298–312). New York, NY: Routledge.
   Google Scholar
• Zaïane, O. R. (2002). Building a recommender agent for e-learning system. In Proceedings of international conference on computers in education (pp. 55–59). Auckland, New Zealand.
   Google Scholar
• Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183. doi: 10.3102/0002831207312909
   Web of Science ®Google Scholar
• Zimmerman, B. J. (2011). Motivational sources and outcomes of self-regulated learning and performance. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 49–64). New York, NY: Routledge.
   Google Scholar
• Zimmerman, B. J., & Schunk, D. (2011). Handbook of self-regulation of learning and performance. New York, NY: Routledge.
   Google Scholar