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Using a User-Interactive QA System for Personalized E-Learning

Using a User-Interactive QA System for Personalized E-Learning

Dawei Hu, Wei Chen, Qingtian Zeng, Tianyong Hao, Feng Min, Liu Wenyin
Copyright: © 2008 |Volume: 6 |Issue: 3 |Pages: 22
ISSN: 1539-3100|EISSN: 1539-3119|ISSN: 1539-3100|EISBN13: 9781615202256|EISSN: 1539-3119|DOI: 10.4018/jdet.2008070101
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MLA

Hu, Dawei, et al. "Using a User-Interactive QA System for Personalized E-Learning." IJDET vol.6, no.3 2008: pp.1-22. http://doi.org/10.4018/jdet.2008070101

APA

Hu, D., Chen, W., Zeng, Q., Hao, T., Min, F., & Wenyin, L. (2008). Using a User-Interactive QA System for Personalized E-Learning. International Journal of Distance Education Technologies (IJDET), 6(3), 1-22. http://doi.org/10.4018/jdet.2008070101

Chicago

Hu, Dawei, et al. "Using a User-Interactive QA System for Personalized E-Learning," International Journal of Distance Education Technologies (IJDET) 6, no.3: 1-22. http://doi.org/10.4018/jdet.2008070101

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Abstract

A personalized e-learning framework based on a user-interactive question-answering (QA) system is proposed, in which a user-modeling approach is used to capture personal information of students and a personalized answer extraction algorithm is proposed for personalized automatic answering. In our approach, a topic ontology (or concept hierarchy) of course content defined by an instructor is used for the system to generate the corresponding structure of boards for holding relevant questions. Students can interactively post questions, and also browse, select, and answer others’ questions in their interested boards. A knowledge base is accumulated using historical question/answer (Q/A) pairs for knowledge reuse. The students’ log data are used to build an association space to compute the interest and authority of the students for each board and each topic. The personal information of students can help instructors design suitable teaching materials to enhance instruction efficiency, be used to implement the personalized automatic answering and distribute unsolved questions to relevant students to enhance the learning efficiency. The experiment results show the efficacy of our user-modeling approach.

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