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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2005
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oai:animorepository.dlsu.edu.ph:faculty_research-39472022-11-08T02:20:24Z Predicting high-level student responses using conceptual clustering Legaspi, Roberto S. Sison, Raymund C. Fukui, Ken Ichi Numao, Masayuki 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. 2005-12-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2948 Faculty Research Work Animo Repository Cluster analysis—Computer programs Machine learning Forecasting Computer Sciences |
| institution |
De La Salle University |
| building |
De La Salle University Library |
| continent |
Asia |
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Philippines Philippines |
| content_provider |
De La Salle University Library |
| collection |
DLSU Institutional Repository |
| topic |
Cluster analysis—Computer programs Machine learning Forecasting Computer Sciences |
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Cluster analysis—Computer programs Machine learning Forecasting Computer Sciences Legaspi, Roberto S. Sison, Raymund C. Fukui, Ken Ichi Numao, Masayuki Predicting high-level student responses using conceptual clustering |
| description |
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. |
| format |
text |
| author |
Legaspi, Roberto S. Sison, Raymund C. Fukui, Ken Ichi Numao, Masayuki |
| author_facet |
Legaspi, Roberto S. Sison, Raymund C. Fukui, Ken Ichi Numao, Masayuki |
| author_sort |
Legaspi, Roberto S. |
| title |
Predicting high-level student responses using conceptual clustering |
| title_short |
Predicting high-level student responses using conceptual clustering |
| title_full |
Predicting high-level student responses using conceptual clustering |
| title_fullStr |
Predicting high-level student responses using conceptual clustering |
| title_full_unstemmed |
Predicting high-level student responses using conceptual clustering |
| title_sort |
predicting high-level student responses using conceptual clustering |
| publisher |
Animo Repository |
| publishDate |
2005 |
| url |
https://animorepository.dlsu.edu.ph/faculty_research/2948 |
| _version_ |
1749181758779162624 |