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:	Legaspi, Roberto S., Sison, Raymund C., Fukui, Ken Ichi, Numao, Masayuki
Format:	text
Published:	Animo Repository 2005
Subjects:	Cluster analysis—Computer programs Machine learning Forecasting Computer Sciences
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/2948
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Institution:	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
_version_	1749181758779162624

Predicting high-level student responses using conceptual clustering

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