Abstract
Education research has identified strategic flexibility as an important aspect of math proficiency and learning. This aspect of student learning has been largely ignored by Intelligent Tutoring Systems (ITSs). In the current study, we demonstrate how Hidden Markov Modeling can be used to identify groups of students who use similar strategies during tutoring and relate these findings to a measure of strategic flexibility. We use these results to explore how strategy use is expressed in an ITS and consider how tutoring systems could integrate a measure of strategy use to improve learning.
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References
Rittle-Johnson, B., Star, J.R.: Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. Journal of Educational Psychology 99(3), 561–574 (2007)
Schneider, M., Rittle-Johnson, B., Star, J.R.: Relations among conceptual knowledge, procedural knowledge, and procedural flexibility in two samples differing in prior knowledge. Developmental Psychology 47(6), 1525–1538 (2011)
Alibali, M., Goldin-Meadow, S.: Gesture-Speech Mismatch and Mechanisms of Learning: What the Hands Reveal about a Child’s State of Mind. Cognitive Psychology 25, 468–523 (1993)
Blöte, A.W., Van der Burg, E., Klein, A.S.: Students’ flexibility in solving two-digit addition and subtraction problems: Instruction effects. Journal of Educational Psychology 93(3), 627 (2001)
National Mathematics Advisory Panel: The Final Report of the National Mathematics Advisory Panel. Technical report, U.S. Department of Education (2008)
Newton, K.J., Star, J.R., Lynch, K.: Understanding the Development of Flexibility in Struggling Algebra Students. Mathematical Thinking and Learning 12(4), 282–305 (2010)
Rittle-Johnson, B., Star, J.R.: Compared with what? The effects of different comparisons on conceptual knowledge and procedural flexibility for equation solving. Journal of Educational Psychology 101(3), 529–544 (2009)
Carpenter, T.P., Franke, M.L., Jacobs, V.R., Fennema, E., Empson, S.B.: A Longitudinal Study of Invention and Understanding in Children’s Multidigit Addition and Subtraction. Journal for Research in Mathematics Education 29, 3–20 (1998)
Beal, C.R., Walles, R., Arroyo, I., Woolf, B.P.: On-line Tutoring for Math Achievement Testing: A Controlled Evaluation. Journal of Interactive Online Learning 6, 1–13 (2007)
Koedinger, K.R., Anderson, J.R.: Intelligent Tutoring Goes To School in the Big City. International journal of Artificial Intelligence in Education 8, 1–14 (1997)
Ritter, S., Anderson, J.R., Koedinger, K.R., Corbett, A.T.: Cognitive Tutor: Applied research in mathematics education. Psychonomic Bulletin & Review 14(2), 249–255 (2007)
Waalkens, M., Aleven, V., Taatgen, N.: Computers & Education. Computers & Education 60(1), 159–171 (2013)
Piech, C., Sahami, M., Koller, D., Cooper, S., Blikstein, P.: Modeling How Students Learn to Program. In: Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, pp. 153–160. ACM (2012)
Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting student misuse of intelligent tutoring systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531–540. Springer, Heidelberg (2004)
Shih, B., Koedinger, K.R., Scheines, R.: A response time model for bottom-out hints as worked examples. Handbook of Educational Data Mining, 201–212 (2011)
Roll, I., Aleven, V., McLaren, B.M., Koedinger, K.R.: Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction 21(2), 267–280 (2011)
Smyth, P.: Clustering sequences with hidden Markov models. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in neural information processing systems, pp. 648–654. Citeseer (1997)
Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis. Wiley Series in Probability and Statistics, vol. 344. John Wiley and Sons, Inc., New Jersey (1990)
Cen, H.: Generalized Learning Factors Analysis: Improving cognitive Models with Machine Learning. PhD thesis, Carnegie Mellon University (2009)
Yudelson, M.V., Koedinger, K.R.: Estimating the benefits of student model improvements on a substantive scale. In: D’Mello, S.K., Calvo, R.A., Olney, A. (eds.) Educational Data Mining, Memphis, TN (2013)
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Tenison, C., MacLellan, C.J. (2014). Modeling Strategy Use in an Intelligent Tutoring System: Implications for Strategic Flexibility. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_58
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DOI: https://doi.org/10.1007/978-3-319-07221-0_58
Publisher Name: Springer, Cham
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