Abstract
When modeling student knowledge and predicting student performance, adaptive educational systems frequently rely on content models that connect learning content (i.e., problems) with its underlying domain knowledge (i.e., knowledge components, KCs) required to complete it. In some domains, such as programming, the number of KCs associated with advanced learning contents is quite large. It complicates modeling due to increasing noise and decreases efficiency. We argue that the efficiency of modeling and prediction in such domains could be improved without the loss of quality by reducing problems content models to a subset of most important KCs. To prove this hypothesis, we evaluate several KC reduction methods varying reduction size by assessing the prediction performance of Knowledge Tracing and Performance Factor Analysis. The results show that the predictive performance using reduced content models can be significantly better than using original one, with extra benefits of reducing time and space.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barnes, T., Bitzer, D.L., Vouk, M.A.: Experimental Analysis of the Q-Matrix Method in Knowledge Discovery. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 603–611. Springer, Heidelberg (2005)
Cen, H., Koedinger, K.R., Junker, B.: Learning factors analysis – a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164–175. Springer, Heidelberg (2006)
Cen, H., Koedinger, K.R., Junker, B.: Comparing Two IRT Models for Conjunctive Skills. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 796–798. Springer, Heidelberg (2008)
Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4(4), 253–278 (1994)
Desmarais, M.C., Naceur, R.: A Matrix Factorization Method for Mapping Items to Skills and for Enhancing Expert-Based Q-Matrices. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 441–450. Springer, Heidelberg (2013)
Gong, Y., Beck, J.E., Heffernan, N.T.: Comparing Knowledge Tracing and Performance Factor Analysis by Using Multiple Model Fitting Procedures. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 35–44. Springer, Heidelberg (2010)
González-Brenes, J.P., Huang, Y., Brusilovsky, P.: General Features in Knowledge Tracing: Applications to Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. In: Proceedings of the 7th International Conference on Educational Data Mining (accepted, 2014)
González-Brenes, J.P., Mostow, J.: What and When do Students Learn? Fully Data-Driven Joint Estimation of Cognitive and Student Models. In: The 6th International Conference on Educational Data Mining, Memphis, TN (2013)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Hosseini, R., Brusilovsky, P.: JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems. In: The First Workshop on AI-supported Education for Computer Science (2013)
Hsiao, I.-H., Sosnovsky, S., Brusilovsky, P.: Guiding students to the right questions: adaptive navigation support in an E-Learning system for Java programming. Journal of Computer Assisted Learning 26(4) (2010)
Koedinger, K.R., Pavlik Jr., P.I., Stamper, J.C., Nixon, T., Ritter, S.: Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing. In: Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, NL, pp. 91–100 (2011)
Pavlik, P.I., Cen, H., Koedinger, K.R.: Performance Factors Analysis – A New Alternative to Knowledge Tracing. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 531–538 (2009)
Xu, Y., Mostow, J.: Comparison of methods to trace multiple subskills: Is LR-DBN best? In: Proceedings of the Fifth International Conference on Educational Data Mining, Chania, Crete, Greece, pp. 41–48 (2012)
Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized Bayesian Knowledge Tracing Models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 171–180. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, Y., Xu, Y., Brusilovsky, P. (2014). Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-08786-3_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08785-6
Online ISBN: 978-3-319-08786-3
eBook Packages: Computer ScienceComputer Science (R0)