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

doi:10.1007/11528043_27

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

Department of Engineering Science

Research output: Contribution to journal › Conference article › peer-review

20 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 language	English
Pages (from-to)	273-278
Number of pages	6
Journal	Lecture Notes in Computer Science
Volume	3583
DOIs	https://doi.org/10.1007/11528043_27
Publication status	Published - 2005
Event	4th International Conference on Web- Based Learning - ICWL 2005 - Hong Kong, China Duration: 2005 Jul 31 → 2005 Aug 3

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

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10.1007/11528043_27

Cite this

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title = "Automatic leveling system for e-learning examination pool using entropy-based decision tree",

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.",

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AU - Cheng, Shu Chen

AU - Huang, Yueh Min

AU - Chen, Juei Nan

AU - Lin, Yen Ting

PY - 2005

Y1 - 2005

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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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ER -

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

Abstract

All Science Journal Classification (ASJC) codes

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