Predicting high-level student responses using conceptual clustering

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

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

First Page

757

Last Page

760

Publication Date

12-1-2005

Abstract

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.

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Disciplines

Computer Sciences

Keywords

Cluster analysis—Computer programs; Machine learning; Forecasting

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