Feature-Aware Student knowledge Tracing (FAST)

Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features into Knowledge Tracing, which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a follow-up study, we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The main paper was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).

Resources

• Download the source code here
• Read the EDM 2014 conference presentation slides here
• Read the tutorial slides presented in EDM 2015 workshop here

Publications

• Gonzalez-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2013) FAST: Feature-Aware Student Knowledge Tracing. In: Proceedings of NIPS 2013 Workshop on Data Driven Education, Lake Tahoe, NV, December 10, 2013, (paper)
• González-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2014) General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 84-91. (First two authors contributed equally. Nominated for Best Paper Award) (presentationpapertutorial code)
• Khajah, M. M., Huang, Y., González-Brenes, J. P., Mozer, M. C., and Brusilovsky, P. (2014) Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. In: I. Cantador, M. Chi, R. Farzan and R. Jäschke (eds.) Proceedings of Workshop on Personalization Approaches in Learning Environments (PALE 2014) at the 22th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Aalborg, Denmark, July 11, 2014, CEUR, pp. 7-12. (First three authors contributed equally) (presentationpaper).
• Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Parameterized Exercises in Java Programming: Using Knowledge Structure for Performance Prediction. In: Proceedings of The second Workshop on AI-supported Education for Computer Science (AIEDCS) at 12th International Conference on Intelligent Tutoring Systems ITS 2014, Honolulu, Hawaii, June 6 2014. (paper)(presentation)
• Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2015) The FAST toolkit for Unsupervised Learning of HMMs with Features. In: The Machine Learning Open Source Software workshop at the 32nd International Conference on Machine Learning (ICML-MLOSS 2015), Lille, France July 10, 2015. (papercode)
• Huang, Y., González-Brenes, J. P., Kumar, R., Brusilovsky, P. (2015) A Framework for Multifaceted Evaluation of Student Models. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, pp. 203-210. (paper) (presentation)