ABSTRACT
Knowledge tracing is a significant research topic in student modeling. It is a task to model students' mastery level of knowledge points by mining their historical exercise performance. Literature has shown that Dynamic Key-Value Memory Networks (DKVMN) which has been proposed to handle the knowledge tracing task generally outperform traditional methods. However, through our experimentation, we have noticed a problem in the DKVMN model that it ignored behavior features collected by intelligence tutoring system (ITS) but only regard the exercise and the correctness as input. Behavior features, such as the response time, the hint request and the number of attempt, can be used to capture the student's learning behavior information and are very helpful in modeling the student's knowledge status. Therefore, the performance of the model can be improved. This work aims to improve the performance of the DKVMN model by incorporating more features to the input. More specifically, we apply decision tree classifier to preprocess the behavior features, which is an effective way to capture how the student deviates from others in the exercise. The predicted response concatenated with the exercise tag to train a DKVMN model, which can output the probability that a student will answer the exercise correctly. The experiment results show that our adapted DKVMN model, incorporating more combinations of behavior features can effectively improve accuracy. On the ASSISTments 2009 education dataset, the AUC value of our experiment is eight percent higher than the original DKVMN model.
- Albert T. Corbett and John R. Anderson. 1995. Knowledge tracing: modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 4 (March 1995), 253--278Google Scholar
- Agrawal, R.: Data-driven education: some opportunities and challenges. In: EDM, p. 2 (2016)Google Scholar
- Sweeney, M., Lester, J., Rangwala, H., Johri, A.: Next-term student performance prediction: a recommender systems approach. In: EDM, p. 7 (2016)Google Scholar
- M. Feng, N.T. Heffernan, and K. R. Koedinger. 2009. Addressing the assessment challenge with an online system that tutors as it assesses. User Modeling and User-Adapted Interaction 19, 3 (2009), 243--266. Google ScholarDigital Library
- W. J. van der Linden and R. K. Hambleton, Handbook of modern item response theory. Springer Science & Business Media, 2013. II-AGoogle Scholar
- Lan, A. S., Studer, C., and Baraniuk, R. G. Time-varying learning and content analytics via sparse factor analysis. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (2014), ACM, pp. 452--461. Google ScholarDigital Library
- Corbett, A. T., and Anderson, J. R. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 4 (1994), 253--278Google Scholar
- Z. A. Pardos and N. T. Heffernan. Modeling individualization in a bayesian networks implementation of knowledge tracing. In User Modeling, Adaptation, and Personalization, pages 255--266. Springer, 2010. Google ScholarDigital Library
- J. Reye. Student modelling based on belief networks. International Journal of Artificial Intelligence in Education, 14(1):63--96, 2004. Google ScholarDigital Library
- M. V. Yudelson, K. R. Koedinger, and G. J. Gordon. Individualized bayesian knowledge tracing models. In Artificial intelligence in education, pages 171--180. Springer, 2013Google ScholarCross Ref
- Baker, R.S.J., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In: Woolf, B.P., Aimeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 406--415. Springer, Heidelberg (2008). 44 Google ScholarDigital Library
- Piech C, Bassen J, Huang J, Ganguli S, Sahami M, Guibas LJ, Sohl-Dickstein J. Deep knowledge tracing. In: NIPS; 2015. p. 505--513.F.N.M Surname, Article Title, https://www.acm.org/publications/proceedings-templat Google ScholarDigital Library
- X. Xiong, S. Zhao, E. G. Van Inwegen, and J. E. Beck. Going deeper with deep knowledge tracing. In Educational Data Mining 2016, 2016Google Scholar
- Khajah, M., Lindsey, R.V., Mozer, M.: How deep is knowledge tracing? In: EDM (2016)Google Scholar
- Cheung LP, Yang H. Heterogeneous features integration in deep knowledge tracing. In: ICONIP; 2017.Google ScholarCross Ref
- Zhang L, Xiong X, Zhao S, Botelho A, Heffernan NT. Incorporating rich features into deep knowledge tracing. In: L@S; 2017. p. 169--172. Google ScholarDigital Library
- Zhang, J., Shi, X., King, I., Yeung, D.: Dynamic key-value memory networks for knowledge tracing. In: WWW, pp. 765--774 (2017). Google ScholarDigital Library
- 2009-2010 ASSISTment Data: https://sites.google.com/site/assistmentsdata/home/assistment-2009-2010-data.Google Scholar
Recommendations
Dynamic Key-Value Memory Networks for Knowledge Tracing
WWW '17: Proceedings of the 26th International Conference on World Wide WebKnowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help ...
Knowledge Tracing Model with Learning and Forgetting Behavior
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementThe Knowledge Tracing (KT) task aims to trace the changes of students' knowledge state in real time according to students' historical learning behavior, and predict students' future learning performance. The modern KT models have two problems. One is ...
Knowledge tracing based on multi-feature fusion
AbstractKnowledge tracing involves modeling student knowledge states over time so that we can accurately predict student performance in future interactions and recommend personalized student learning paths. However, existing methods, such as deep ...
Comments