Bayesian Based Student Knowledge Modeling In Intelligent Tutoring Systems

被引：0

作者：

Khodeir, Nabila ^{[1
]}

Wanas, Nayer ^{[1
]}

Hegazy, Nadia ^{[1
]}

Darwish, Nevin ^{[2
]}

机构：

[1] Elect Res Inst, Informat Dep, Tahrir St, Giza, Egypt

[2] Cairo Univ, Fac Engn, Dept Comp Engn, Giza, Egypt

来源：

2012 6TH IEEE INTERNATIONAL CONFERENCE ON E-LEARNING IN INDUSTRIAL ELECTRONICS (ICELIE) | 2012年

关键词：

Artificial intelligence; Intelligent Tutoring Systems; Systems engineering and theory; Student Modeling; Mathematics; Bayesian Networks; Recursive estimation; Abduction;

D O I：

暂无

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

In this paper we present student knowledge modeling algorithm in a probabilistic domain within an intelligent tutoring system. The student answers to questions requiring diagnosing skills are used to estimate the actual student model. Updating and verification of the model are conducted based on the matching between the student's and model answers. Three different approaches to updating are suggested, namely coarse, refined, and blended updating. In addition, different granularity levels are evaluated by changing the value of the updating step and the output of this parametric study is indicated. Results suggest that the refined model provides better approximation of the student model while utilizing blended model decreases the required trial numbers to model the student knowledge with limited reduction in accuracy.

引用

页码：12 / 17

页数：6

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