Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains

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Published Sep 19, 2017
Ran Liu Kenneth R. Koedinger

Abstract

As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it yields better student learning when applied. Such an evaluation has been referred to as "closing the loop", as it completes cycle of system design, deployment, data analysis, and discovery leading back to design. Here, we present an instance of "closing the loop" on an automated cognitive modeling improvement discovered by Learning Factors Analysis (Cen, Koedinger, and Junker, 2006). We discuss our findings from a process in which we interpret the automated improvements yielded by the best-fitting cognitive model, validate the interpretation on novel data, use it to make changes to classroom-deployed educational technology, and show that the changes lead to significant learning gains relative to a control condition.

How to Cite

Liu, R., & Koedinger, K. R. (2017). Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains. JEDM | Journal of Educational Data Mining, 9(1), 25-41. https://doi.org/10.5281/zenodo.3554625
Abstract 1337 | PDF Downloads 769

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Keywords

closing the loop, Learning Factors Analysis, cognitive model, learning gains

References
ALEVEN, V., SEWALL, J., MCLAREN, B.M., AND KOEDINGER, K.R. 2006. Rapid authoring of intelligent tutors for real-world and experimental use. In Proceedings of the 6th ICALT. IEEE, Los Alamitos, CA, pp. 847-851.

BAKER, R. S., CORBETT, A. T., AND KOEDINGER, K. R. 2004. Detecting student misuse of intelligent tutoring systems. In Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 531-540.

BAKER, R. S., CORBETT, A. T., KOEDINGER, K. R., EVENSON, S. E., ROLL, I., WAGNER, A. Z.,

NAIM, M., RASPAT, J., BAKER, D. J., AND BECK, J. 2006. Adapting to when students game an intelligent tutoring system. In Proc Int Conf on Intelligent Tutoring Systems, 392-401. Jhongli, Taiwan.

BARNES, T. 2005. The Q-matrix method: Mining student response data for knowledge. In Proceedings of AAAI 2005: Educational Data Mining Workshop, 978–980.

BERNACKI, M. 2012. Motivation for learning HS geometry 2012 (geo-pa). pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=748 CEN. H. Generalized learning factors analysis: improving cognitive models with machine learning. Doctoral Dissertation, Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 2009.

CEN, H., KOEDINGER, K. R., AND JUNKER, B. 2006. Learning Factors Analysis: A general method for cognitive model evaluation and improvement. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 164-175. Berlin: SpringerVerlag.

CLARK, R. E., FELDON, D., VAN MERRIËNBOER, J., YATES, K., & EARLY, S. 2008. Cognitive task analysis. In Spector, J. M., Merrill, M.D., van Merriënboer, J., & Driscoll, M.P. (Eds.), Handbook of research on educational communications and technology (3rd ed.). Mahwah: Lawrence Erlbaum.

CORBETT, A. T. AND ANDERSON, J. R. 1995. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling & User-Adapted Interaction, 4, 253-278.

DRANEY, K., WILSON, M., & PIROLLI, P. (1996). Measuring learning in LISP: an application of the random coefficients multinomial logit model. In: Engelhard G, Wilson M, eds. Objective Measurement III: Theory into Practice. Norwood, NJ: Ablex.

D’MELLO, S., BLANCHARD, N., BAKER, R. OCUMPAUGH, J., AND BRAWNER, K. 2014. I feel your pain: a selective review of affect sensitive instructional strategies. In Sottilare R, Graesser A, Hu X, & Goldberg B (Eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems: Adaptive Instructional Strategies (Volume 2). Orlando, FL: US Army Research Laboratory.

FENG, M., HEFFERNAN, N. T., AND KOEDINGER, K. R. 2009. Addressing the assessment challenge in an online system that tutors as it assesses. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI), 19(3), 243-266.

GONZALEZ-BRENES, J. P. AND MOSTOW, J. 2012. Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models. In Proceedings of the 5th International Conference on Educational Data Mining. Chania, Greece.

KOEDINGER, K. R. Geometry Area 1996-97. pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=76

KOEDINGER, K. R., BAKER, R. S. J. D., CUNNINGHAM, K., SKOGSHOLM, A., LEBER, B., &

STAMPER, J. C. 2010. A Data Repository for the EDM community: The PSLC DataShop. In Romero C, Ventura S, Pechenizkiy M, Baker RSJd (Eds.), Handbook of Educational Data Mining. Boca Raton, FL: CRC Press.

KOEDINGER, K. R. AND MCLAUGHLIN, E. A. 2010. Seeing language learning inside the math: Cognitive analysis yields transfer. In Proceedings of the 32nd Annual Conference of the Cognitive Science Society, 471–476. Austin, TX.

KOEDINGER, K. R., MCLAUGHLIN, E. A, AND STAMPER, J. C. 2012. Automated cognitive model improvement. In Proceedings of the 5th International Conference on Educational Data Mining, 17-24. Chania, Greece.

KOEDINGER, K. R., STAMPER, J. C., MCLAUGHLIN, E. A., AND NIXON, T. 2013. Using datadriven discovery of better cognitive models to improve student learning. In Proceedings of the 16th International Conference on Artificial Intelligence in Education.

LAN, A. S., STUDER, C., WATERS, A. E., BARANIUK, R. G. 2013. Tag Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics. In Proceedings of the 6th International Conference on Educational Data Mining.

LAN, A. S., STUDER, C., WATERS, A. E., BARANIUK, R. G. 2014. Sparse Factor Analysis for Learning and Content Analytics. Journal of Machine Learning Research, 15, 1959-2008.

LINDSEY, R. V., KHAJAH, M., AND MOZER, M. C. 2014. Automatic discovery of cognitive skills to improve the prediction of student learning. In Advances in Neural Information Processing Systems, 27, pp. 1386-1394. La Jolla, CA.

LIU, R., KOEDINGER, K. R., AND MCLAUGHLIN, E. A. 2014. Interpreting Model Discovery and Testing Generalization to a New Dataset. In Proceedings of the 7th International Conference on Educational Data Mining. London, UK.

NATHAN, M. J, KOEDINGER, K. R., AND ALIBALI, M. W. 2001. Expert blind spot: when content knowledge eclipses pedagogical content knowledge. In Proceedings of the 3rd International Conference on Cognitive Science, pp. 644-648. Beijing, China:USTC Press.

RITTER, S., ANDERSON, J. R., KOEDINGER, K. R. AND CORBETT, A. 2007. Cognitive Tutor: Applied research in mathematics education. Psychonomic bulletin & review, 14(2), 249255.

SAN PEDRO, M., BAKER, R. S., ROWERS, A. J., AND HEFFERNAN, N. T. 2013. Predicting college enrollment from student interaction with an intelligent tutoring system in middle school. In Proceedings of the 6th International Conference on Educational Data Mining. Memphis, TN, pp. 177–184.

SPADA, H. AND MCGAW, B. 1985. The assessment of learning effects with linear logistic test models. In: Embretson SE, ed. Test Design: Developments in Psychology and Psychometrics. New York: Academic Press, 169-193.

STAMPER, J. AND KOEDINGER, K. R. 2011. Human-machine student model discovery and improvement using data. (2011). In Proceedings of the 15th International Conference on Artificial Intelligence in Education, 353–360. Auckland, New Zealand.
Section
EDM 2017 Journal Track