Predicting high-level student responses using conceptual clustering
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 pred...
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| Main Authors: | , , , |
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| 格式: | text |
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Animo Repository
2005
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| 主題: | |
| 在線閱讀: | https://animorepository.dlsu.edu.ph/faculty_research/2948 |
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| 機構: | De La Salle University |
| 總結: | 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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