Prerequisite Structure Discovery In Intelligent Tutoring Systems

被引：0

作者：

Annabi, Louis ^{[1
,2
]}

Sao Mai Nguyen ^{[1
,2
]}

机构：

[1] Inst Polytech Paris, Flowers Team, ENSTA Paris, U2IS, Palaiseau, France

[2] INRIA, Palaiseau, France

来源：

2023 IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING, ICDL | 2023年

关键词：

Intelligent Tutoring Systems; Knowledge Tracing; Knowledge Structure Discovery;

D O I：

10.1109/ICDL55364.2023.10364416

中图分类号：

B84 [心理学]; C [社会科学总论]; Q98 [人类学];

学科分类号：

03 ; 0303 ; 030303 ; 04 ; 0402 ;

摘要：

This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. The KS represents the relations between different Knowledge Components (KCs), while KT predicts a learner's success based on her past history. The contribution of this research includes proposing a KT model that incorporates the KS as a learnable parameter, enabling the discovery of the underlying KS from learner trajectories. The quality of the uncovered KS is assessed by using it to recommend content and evaluating the recommendation algorithm with simulated students.

引用

页码：176 / 181

页数：6

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