Automatic leveling system for e-learning examination pool using entropy-based decision tree

Shu Chen Cheng, Yueh Min Huang, Juei Nan Chen, Yen Ting Lin

Research output: Contribution to journalConference article

19 Citations (Scopus)

Abstract

In this paper, we propose an automatic leveling system for e-learning examination pool using the algorithm of the decision tree. The automatic leveling system is built to automatically level each question in the examination pool according its difficulty. Thus, an e-learning system can choose questions that are suitable for each learner according to individual background. Not all attributes are relevant to the classification, in other words, the decision tree tells the importance of each attribute.

Original languageEnglish
Pages (from-to)273-278
Number of pages6
JournalLecture Notes in Computer Science
Volume3583
Publication statusPublished - 2005 Oct 19
Event4th International Conference on Web- Based Learning - ICWL 2005 - Hong Kong, China
Duration: 2005 Jul 312005 Aug 3

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Electronic Learning
Decision trees
Decision tree
Entropy
Attribute
Learning Systems
Learning systems
Choose
Background

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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Automatic leveling system for e-learning examination pool using entropy-based decision tree. / Cheng, Shu Chen; Huang, Yueh Min; Chen, Juei Nan; Lin, Yen Ting.

In: Lecture Notes in Computer Science, Vol. 3583, 19.10.2005, p. 273-278.

Research output: Contribution to journalConference article

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AU - Cheng, Shu Chen

AU - Huang, Yueh Min

AU - Chen, Juei Nan

AU - Lin, Yen Ting

PY - 2005/10/19

Y1 - 2005/10/19

N2 - In this paper, we propose an automatic leveling system for e-learning examination pool using the algorithm of the decision tree. The automatic leveling system is built to automatically level each question in the examination pool according its difficulty. Thus, an e-learning system can choose questions that are suitable for each learner according to individual background. Not all attributes are relevant to the classification, in other words, the decision tree tells the importance of each attribute.

AB - In this paper, we propose an automatic leveling system for e-learning examination pool using the algorithm of the decision tree. The automatic leveling system is built to automatically level each question in the examination pool according its difficulty. Thus, an e-learning system can choose questions that are suitable for each learner according to individual background. Not all attributes are relevant to the classification, in other words, the decision tree tells the importance of each attribute.

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