ABSTRACT
Knowledge Tracing aims to model student knowledge by predicting the correctness of each next item as students work through an assignment. Through recent developments in deep learning, Deep Knowledge Tracing (DKT) was explored as a method to improve upon traditional methods. Thus far, the DKT model has only considered the knowledge components and correctness as input, neglecting the other important features collected by computer-based learning platforms. This paper seeks to further improve upon DKT by incorporating more problem-level features. With this higher dimensional input, an adaption to the original DKT model structure is also proposed to convert the input into a low dimensional feature vector. Our results show that this adapted DKT model can effectively improve accuracy.
- J. E. Beck and Y. Gong. 2013. Wheel-spinning: Students who fail to master a skill. In International Conference on Artificial Intelligence in Education. Springer, 431--440. Google ScholarCross Ref
- A. T. Corbett and J. R. Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 4 (1994), 253--278. Google ScholarCross Ref
- 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
- G. E. Hinton and R. R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313, 5786 (2006), 504--507. Google ScholarCross Ref
- P.I. PAVLIK JRa, H. Cen, and K. R. Koedinger. 2009. Performance Factors Analysis--A New Alternative to Knowledge Tracing. Online Submission (2009).Google Scholar
- C. Piech, J. Bassen, J. Huang, M. Sahami S. Ganguli, L. Guibas, and J. Sohl-Dickstein. 2015. Deep knowledge tracing. In Advances in Neural Information Processing Systems. 505--513.Google Scholar
- X. Xiong, S. Zhao, E.G. VanInwege, and J. E. Beck. 2016. Going deeper with deep knowledge tracing. In Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016). 545--550.Google Scholar
- Incorporating Rich Features into Deep Knowledge Tracing
Recommendations
What is wrong with deep knowledge tracing? Attention-based knowledge tracing
AbstractScientifically and effectively tracking student knowledge states is a significant and fundamental task in personalized education. Many neural network-based models, e.g., deep knowledge tracing (DKT), have achieved remarkable results on knowledge ...
Towards Interpretable Deep Learning Models for Knowledge Tracing
Artificial Intelligence in EducationAbstractDriven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design knowledge tracing (KT) models for achieving better prediction performance. However, the lack of interpretability of these models ...
Incorporating Item Response Theory into Knowledge Tracing
Artificial Intelligence in EducationAbstractThe popularity of artificial neural networks has brought high predictive power to many difficult machine learning problems. Knowledge tracing (KT), the task of tracking students’ understanding of various concepts over time, is included in this ...
Comments