Predicting high-level student responses using conceptual clustering
College of Computer Studies
Proc. Int. Conf. on Computers in Education 2005: "Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences"- Sharing Research Results and Exemplary Innovations, ICCE
A conceptual clustering algorithm can search through huge amounts of data looking for multi-dimensional structures, where each structure or cluster represents a relevant concept in the problem-solving domain. We investigated on the effect of cluster knowledge for a learning agent to improve its prediction of higher level student response aspects. Our empirical results show that when cluster knowledge is utilized by a function approximator, prediction is improved as compared to treating the entire data population as a single cluster. © 2005 Asia-Pacific Society for Computers in Education.
Legaspi, R., Sison, R. C., Fukui, K., & Numao, M. (2005). Predicting high-level student responses using conceptual clustering. Proc. Int. Conf. on Computers in Education 2005: "Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences"- Sharing Research Results and Exemplary Innovations, ICCE, 757-760. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/2948
Cluster analysis—Computer programs; Machine learning; Forecasting