The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning
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Published
May 1, 2013
Philip H. Winne
Ryan S.J.d. Baker
Abstract
Our article introduces the Journal of Educational Data Mining's Special Issue on Educational Data Mining on Motivation, Metacognition, and Self-Regulated Learning. We outline general research challenges for data mining researchers who conduct investigations in these areas, the potential of EDM to advance research in this area, and issues in validating findings generated by EDM.
How to Cite
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), 1–8. https://doi.org/10.5281/zenodo.3554619
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Keywords
educational data mining, metacognition, motivation, self-regulated learning
References
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AMERSHI, S. and CONATI, C. 2009. Combining Unsupervised and Supervised Machine Learning to Build User Models for Exploratory Learning Environments. Journal of Educational Data Mining 1, 1, 18-71.
ARNOLD, K.E. 2010. Signals: Applying Academic Analytics. Educause Quarterly 33, 1 (2010). Ivon Arroyo and Beverly Park Woolf. 2005. Inferring learning and attitudes from a Bayesian Network of log file data. Proceedings of the 12th International Conference on Artificial Intelligence and Education, 33-40.
BAKER, R.S., CORBETT, A.T., and KOEDINGER, K.R. 2004. Detecting Student Misuse of Intelligent Tutoring Systems.Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 531-540.
BAKER, R.S.J.D., D’MELLO, S., RODRIGO, M.M.T., and GRAESSER, A.C. 2010. Better to Be Frustrated than Bored: The Incidence, Persistence, and Impact of Learners' Cognitive-Affective States during Interactions with Three Different Computer-Based Learning Environments. International Journal of Human-Computer Studies 68, 4, 223-241.
BAKER, R.S.J.D. and YACEF, K.. 2009. The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining 1, 1, 3-17.
BECK, J.E., CHANG, K., MOSTOW, J., and CORBETT, A. 2008. Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology. Proceedings of the 9th International Conference on Intelligent Tutoring
Systems, 383-394. D’MELLO, S.K., CRAIG, S.D., WITHERSPOON, A., MCDANIEL, B., and GRAESSER, A. 2008. Automatic detection of learner’s affect from conversational cues. User Modeling and User Adapted Interaction 18, 45-80.
NELSON, T.O. and NARENS, L. 1990. Metamemory: A theoretical framework and new findings. In G.H. BOWER (Ed.), The psychology of learning and motivation, Vol. 26, Academic Press, New York, 125-173.
PEKRUN, R., GOETZ, T., and TITZ, W. 2002. Academic emotions in students' self-regulated learning and achievement: A program of qualitative and quantitative research, Educational Psychologist 37, 2, 91-105.
ROMERO, C. and VENTURA, S. 2010. Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40, 6, 601-618.
SALOMAN, G. and PERKINS, D.N. 1989. Rocky Roads to Transfer: Rethinking Mechanism of a Neglected Phenomenon. Educational Psychologist 24, 2, 113-142.
SAN PEDRO, M.O.C.Z., BAKER, R.S.J.D., BOWERS, A.J., and HEFFERNAN, N.T. In press. Predicting college enrollment from student interaction with an Intelligent Tutoring System in middle school. To appear in Proceedings of the 5th International Conference on Educational Data Mining.
SHIH, B., KOEDINGER, K.R., and SCHEINES, R. 2008. A response time model for bottom-out hints as worked examples. Proceedings of the 1st International Conference on Educational Data Mining, 117-126.
WINNE, P.H. 2001. Self-regulated learning viewed from models of information processing. In B.J. ZIMMERMAN and D.H. SCHUNK (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd Ed.). Lawrence Erlbaum Associates, Mahwah, 153-189.
WINNE, P.H. 2006. How software technologies can improve research on learning and bolster school reform. Educational Psychologist 41, 5–17.
WINNE, P.H. 2011. A cognitive and metacognitive analysis of self-regulated learning. In B.J. ZIMMERMAN and D.H. SCHUNK (Eds.), Handbook of Self-Regulation of Learning and Performance, New York, Routledge, 15-32.
WINNE, P.H. and HADWIN, A.F. 1998. Studying as self-regulated learning. In D.J. HACKER, J.E. DUNLOSKY, and A.C. GRAESSER (Eds.), Metacognition in Educational Theory and Practice, Lawrence Erlbaum Associates, Mahwah, 277-304.
WINNE, P.H. and NESBIT, J.C. 2010. The psychology of academic achievement. Annual Review of Psychology 61, 653-578.
AMERSHI, S. and CONATI, C. 2009. Combining Unsupervised and Supervised Machine Learning to Build User Models for Exploratory Learning Environments. Journal of Educational Data Mining 1, 1, 18-71.
ARNOLD, K.E. 2010. Signals: Applying Academic Analytics. Educause Quarterly 33, 1 (2010). Ivon Arroyo and Beverly Park Woolf. 2005. Inferring learning and attitudes from a Bayesian Network of log file data. Proceedings of the 12th International Conference on Artificial Intelligence and Education, 33-40.
BAKER, R.S., CORBETT, A.T., and KOEDINGER, K.R. 2004. Detecting Student Misuse of Intelligent Tutoring Systems.Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 531-540.
BAKER, R.S.J.D., D’MELLO, S., RODRIGO, M.M.T., and GRAESSER, A.C. 2010. Better to Be Frustrated than Bored: The Incidence, Persistence, and Impact of Learners' Cognitive-Affective States during Interactions with Three Different Computer-Based Learning Environments. International Journal of Human-Computer Studies 68, 4, 223-241.
BAKER, R.S.J.D. and YACEF, K.. 2009. The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining 1, 1, 3-17.
BECK, J.E., CHANG, K., MOSTOW, J., and CORBETT, A. 2008. Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology. Proceedings of the 9th International Conference on Intelligent Tutoring
Systems, 383-394. D’MELLO, S.K., CRAIG, S.D., WITHERSPOON, A., MCDANIEL, B., and GRAESSER, A. 2008. Automatic detection of learner’s affect from conversational cues. User Modeling and User Adapted Interaction 18, 45-80.
NELSON, T.O. and NARENS, L. 1990. Metamemory: A theoretical framework and new findings. In G.H. BOWER (Ed.), The psychology of learning and motivation, Vol. 26, Academic Press, New York, 125-173.
PEKRUN, R., GOETZ, T., and TITZ, W. 2002. Academic emotions in students' self-regulated learning and achievement: A program of qualitative and quantitative research, Educational Psychologist 37, 2, 91-105.
ROMERO, C. and VENTURA, S. 2010. Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40, 6, 601-618.
SALOMAN, G. and PERKINS, D.N. 1989. Rocky Roads to Transfer: Rethinking Mechanism of a Neglected Phenomenon. Educational Psychologist 24, 2, 113-142.
SAN PEDRO, M.O.C.Z., BAKER, R.S.J.D., BOWERS, A.J., and HEFFERNAN, N.T. In press. Predicting college enrollment from student interaction with an Intelligent Tutoring System in middle school. To appear in Proceedings of the 5th International Conference on Educational Data Mining.
SHIH, B., KOEDINGER, K.R., and SCHEINES, R. 2008. A response time model for bottom-out hints as worked examples. Proceedings of the 1st International Conference on Educational Data Mining, 117-126.
WINNE, P.H. 2001. Self-regulated learning viewed from models of information processing. In B.J. ZIMMERMAN and D.H. SCHUNK (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd Ed.). Lawrence Erlbaum Associates, Mahwah, 153-189.
WINNE, P.H. 2006. How software technologies can improve research on learning and bolster school reform. Educational Psychologist 41, 5–17.
WINNE, P.H. 2011. A cognitive and metacognitive analysis of self-regulated learning. In B.J. ZIMMERMAN and D.H. SCHUNK (Eds.), Handbook of Self-Regulation of Learning and Performance, New York, Routledge, 15-32.
WINNE, P.H. and HADWIN, A.F. 1998. Studying as self-regulated learning. In D.J. HACKER, J.E. DUNLOSKY, and A.C. GRAESSER (Eds.), Metacognition in Educational Theory and Practice, Lawrence Erlbaum Associates, Mahwah, 277-304.
WINNE, P.H. and NESBIT, J.C. 2010. The psychology of academic achievement. Annual Review of Psychology 61, 653-578.
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