A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns

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Published May 1, 2013
John S. Kinnebrew Kirk M. Loretz Gautam Biswas

Abstract

Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify differentially frequent patterns between groups of students (e.g., experimental versus control conditions or high versus low performers). We extend this technique by contextualizing the sequence mining with information about the stu- dent's performance over the course of the learning interactions. Specifically, we employ a piecewise linear segmentation algorithm in concert with the differential sequence mining technique to identify and compare segments of students' productive and unproductive learning behaviors. We present the results from the application of this exploratory data mining methodology to learning interaction trace data gathered during a recent middle school class study with the Betty's Brain learning environment. These results illustrate the potential of this methodology in identifying learning behavior patterns relevant to the investigation of metacognition and strategy use.

How to Cite

Kinnebrew, J. S., Loretz, K. M., & Biswas, G. (2013). A Contextualized, Differential Sequence Mining Method to Derive Students’ Learning Behavior Patterns. JEDM | Journal of Educational Data Mining, 5(1), 190-219. https://doi.org/10.5281/zenodo.3554617
Abstract 1047 | PDF Downloads 742

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Keywords

sequence mining, di erential sequence mining, piecewise linear representation, learning behaviors, metacognition, computer-based learning environments

References
Azevedo, R. 2005. Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist 40, 4, 199-209.

Azevedo, R. and Witherspoon, A. 2009. Self-regulated use of hypermedia. In Handbook of metacognition in education, A. Graesser, J. Dunlosky, and D. Hacker, Eds. Erlbaum, Mahwah, NJ.

Bandura, A. 1997. Self-effcacy: The exercise of control. Freeman, New York, NY.

Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., and Roscoe, R. 2010. Measuring Self- regulated Learning Skills through Social Interactions in a Teachable Agent Environment. Re- search and Practice in Technology-Enhanced Learning (RPTEL) 5, 2, 123-152.

Biswas, G., Leelawong, K., Schwartz, D., Vye, N., and Vanderbilt, T. 2005. Learning by teaching: A new agent paradigm for educational software. Applied Artificial Intelligence 19, 3, 363-392.

Blair, K., Schwartz, D., Biswas, G., and Leelawong, K. 2007. Pedagogical agents for learning by teaching: teachable agents. Educational Technology & Society: Special Issue on Pedagogical Agents 47, 1.

Bransford, J., Brown, A., and Cocking, R., Eds. 2000. How people learn. National Academy Press Washington, DC, Washington, D.C.

Brown, A. and Palincsar, A. 1989. Guided, cooperative learning and individual knowledge ac- quisition. In Knowing, learning, and instruction: Essays in honor of Robert Glaser, L. Resnick, Ed. Lawrence Erlbaum Associates, Hillsdale, NJ, 393-451.

Burleson, W., Picard, R., Perlin, K., and Lippincott, J. 2004. A platform for affective agent research. In Workshop on Empathetic Agents, International Conference on Autonomous Agents and Multiagent Systems. New York, NY.

Butler, D. and Winne, P. 1995. Feedback and self-regulated learning: A theoretical synthesis. Review of educational research 65, 3, 245.

D'Mello, S., Craig, S., Witherspoon, A., Mcdaniel, B., and Graesser, A. 2008. Auto- matic detection of learner's affect from conversational cues. User Modeling and User-Adapted Interaction 18, 1, 45-80.

D'Mello, S., Picard, R., and Graesser, A. 2007. Toward an affect-sensitive AutoTutor. IEEE Intelligent Systems 22, 53-61.

Hadwin, A., Nesbit, J., Jamieson-Noel, D., Code, J., and Winne, P. 2007. Examining trace data to explore self-regulated learning. Metacognition and Learning 2, 2, 107-124.

Hadwin, A., Winne, P., Stockley, D., Nesbit, J., andWoszczyna, C. 2001. Context moderates students' self-reports about how they study. Journal of Educational Psychology 93, 3, 477-487.

Ho, J., Lukov, L., and Chawla, S. 2005. Sequential pattern mining with constraints on large protein databases. In Proceedings of the 12th International Conference on Management of Data (COMAD). 89-100.

Jeong, H. and Biswas, G. 2008. Mining student behavior models in learning-by-teaching envi- ronments. In Proceedings of The First International Conference on Educational Data Mining. Montreal, Quebec, Canada, 127-136.

Keogh, E., Chu, S., Hart, D., and Pazzani, M. 2004. Segmenting time series: A survey and novel approach. In Data mining in time series databases. Vol. 57. World Scientific, 1-22.

Kinnebrew, J. S. and Biswas, G. 2011. Comparative action sequence analysis with hidden markov models and sequence mining. In Proceedings of the Knowledge Discovery in Educational Data Workshop at the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2011). San Diego, CA.

Kinnebrew, J. S., Biswas, G., Sulcer, B., and Taylor, R. S. 2013. Investigating self-regulated learning in teachable agent environments. In International Handbook of Metacognition and Learning Technologies, R. Azevedo and V. Aleven, Eds. Vol. 26. Springer, New York, 451-470.

Laxman, S., Sastry, P., and Unnikrishnan, K. 2005. Discovering frequent episodes and learning hidden markov models: A formal connection. IEEE Transactions on Knowledge and Data Engineering, 1505-1517.

Leelawong, K. and Biswas, G. 2008. Designing learning by teaching agents: The Betty's Brain system. International Journal of Artificial Intelligence in Education 18, 3, 181-208.

Lester, J., Converse, S., Kahler, S., Barlow, S., Stone, B., and Bhogal, R. 1997. The persona effect: affective impact of animated pedagogical agents. In Proceedings of the SIGCHI conference on Human factors in computing systems (CHI '97). 359-366.

Lo, D., Khoo, S., and Liu, C. 2008. Efficient mining of recurrent rules from a sequence database. In Proceedings of the 13th International Conference on Database Systems for Advanced Appli- cations. Springer-Verlag, 67-83.

Mannila, H., Toivonen, H., and Inkeri Verkamo, A. 1997. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1, 3, 259-289.

Martinez, R., Yacef, K., Kay, J., Al-Qaraghuli, A., and Kharrufa, A. 2011. Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop. In Proceedings of the Fourth International Conference on Educational Data Mining. Eindhoven, Netherlands.

Nesbit, J., Zhou, M., Xu, Y., and Winne, P. 2007. Advancing log analysis of student interactions with cognitive tools. 12th Biennial Conference of the European Association for Research on Learning and Insruction (EARLI).

Paris, S. and Paris, A. 2001. Classroom applications of research on self-regulated learning. Educational Psychologist 36, 2, 89-101.

Perera, D., Kay, J., Koprinska, I., Yacef, K., and Zaiane, O. 2009. Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering 21, 6, 759-772.

Perry, N. and Winne, P. 2006. Learning from learning kits: gStudy traces of students' self- regulated engagements with computerized content. Educational Psychology Review 18, 3, 211- 228.

Pintrich, P. 2000. An Achievement Goal Theory Perspective on Issues in Motivation Terminology, Theory, and Research* 1. Contemporary Educational Psychology 25, 1, 92-104.

Pintrich, P., Marx, R., and Boyle, R. 1993. Beyond cold conceptual change: The role of mo- tivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational research 63, 2, 167-199.

Pintrich, P., Smith, D., Garcia, T., and McKeachie, W. 1993. Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and psychological measurement 53, 3, 801-813.

Rabiner, L. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 2, 257-286.

Schraw, G., Kauffman, D., and Lehman, S. 2002. Self-regulated learning theory. In The encyclopedia of cognitive science, L. Nadel, Ed. Nature Publishing Company, London, 1063- 1073.

Schunk, D. and Zimmerman, B. 1997. Social origins of self-regulatory competence. Educational Psychologist 32, 4, 195-208.

Segedy, J. R., Kinnebrew, J. S., and Biswas, G. 2012. Supporting student learning using conversational agents in a teachable agent environment. In Proceedings of the 10th International Conference of the Learning Sciences.

Segedy, J. R., Kinnebrew, J. S., and Biswas, G. 2013. The effect of contextualized con- versational feedback in a complex open-ended learning environment. Educational Technology Research and Development 61, 1, 71-89.

Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Per- formance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology (EDBT): Advances in Database Technology. Springer-Verlag, 3-17.

Su, J.-M., Tseng, S.-S., Wang, W., Weng, J.-F., Yang, J., and Tsai, W.-N. 2006. Learn- ing portfolio analysis and mining for scorm compliant environment. Journal of Educational Technology and Society 9, 1, 262-275.

Tang, T. and McCalla, G. 2002. Student modeling for a web-based learning environment: A data mining approach. In Proceedings of the 18th National Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence, AAAI Press, 967-968.

Weinstein, C., Schulte, A., and Palmer, D. 1987. The Learning and Study Strategies Inven- tory. H & H Publishing, Clearwater, FL.

Winne, P. and Hadwin, A. 2008. The weave of motivation and self-regulated learning. In Moti- vation and self-regulated learning: Theory, research, and applications, D. Schunk and B. Zimmerman, Eds. Taylor & Francis, NY, 297-314.

Zimmerman, B. 1990. Self-regulating academic learning and achievement: The emergence of a social cognitive perspective. Educational Psychology Review 2, 2, 173-201.

Zimmerman, B. 2001. Theories of self-regulated learning and academic achievement: An overview and analysis. In Self-regulated learning and academic achievement: Theoretical perspectives, B. Zimmerman and D. Schunk, Eds. Erlbaum, Mahwah, NJ, 1-37.

Zimmerman, B. 2008. Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Jour- nal 45, 1, 166-183.

Zimmerman, B., Bandura, A., and Martinez-Pons, M. 1992. Self-motivation for academic attainment: The role of self-eficacy beliefs and personal goal setting. American Educational Research Journal 29, 3, 663-676.

Zimmerman, B. and Martinez-Pons, M. 1986. Development of a structured interview for as- sessing student use of self-regulated learning strategies. American Educational Research Jour- nal 23, 4, 614-628.
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