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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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Legaspi, Roberto S., Sison, Raymund C., Fukui, Ken Ichi, Numao, Masayuki
التنسيق: text
منشور في: Animo Repository 2005
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الوصول للمادة أونلاين:https://animorepository.dlsu.edu.ph/faculty_research/2948
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المؤسسة: De La Salle University
id oai:animorepository.dlsu.edu.ph:faculty_research-3947
record_format eprints
spelling 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
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Cluster analysis—Computer programs
Machine learning
Forecasting
Computer Sciences
spellingShingle 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
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