Assistance in Building Student Models Using Knowledge Representation and Machine Learning
We propose a method and a first authoring tool to assist the design and implementation of diagnostic techniques. This method is independent from the domain and allows building more than one technique at once. The method is based on knowledge representation and a semi-automatic machine learning algorithm. We tested the method in two domains, surgery and reading English. Techniques built with our method beat the majority class in terms of accuracy.
KeywordsKnowledge diagnostic authoring tool machine learning
Unable to display preview. Download preview PDF.
- 2.Beck, J.E., Woolf, J.E., Beal, C.R.: ADVISOR: a machine-learning architecture for intelligent tutor construction. In: 17th AAAI Conference on Artificial Intelligence, pp. 552–557 (2000)Google Scholar
- 6.Gong, Y., Beck, J.E., Heffernan, N.T.: How to Construct More Accurate Student Models: Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis. International Journal of Artificial Intelligence in Education 21(1), 27–46 (2011)Google Scholar
- 7.Gonzales-Brenes, J., Mostow, J.: Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models. In: 5th International Conference on Educational Data Mining, pp. 49–56 (2012)Google Scholar
- 8.Minh Chieu, V., Luengo, V., Vadcard, L., Tonetti, J.: Student modeling in complex domains: Exploiting symbiosis between temporal Bayesian networks and fine-grained didactical analysis. International Journal of Artificial Intelligence in Education 20(3), 269–301 (2010)Google Scholar
- 10.Mostow, J., Aist, G.: Evaluating tutors that listen: An overview of Project LISTEN. In: Smart Machines in Education, pp. 169–234. MIT/AAAI Press (2001)Google Scholar
- 11.Murray, T.: Eon: Authoring tools for content, instructional strategy, student model, and interface design. In: Authoring Tools for Advanced Technology Learning Environments, pp. 309–340 (2003)Google Scholar
- 12.Ohlsson, S.: Constraint-based student modeling. NATO ASI Series F Computer and Systems Sciences 125, 167–189 (1994)Google Scholar
- 13.Sison, R., Shimura, M.: Student modeling and machine learning. International Journal of Artificial Intelligence in Education 9(1-2), 128–158 (1998)Google Scholar