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Using a user-interactive QA system for personalized e-learning

  • Dawei Hu
  • , Wei Chen
  • , Qingtian Zeng
  • , Tianyong Hao
  • , Feng Min
  • , Liu Wenyin

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 12 - Chapter in an edited book (Author)peer-review

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. © 2011 by IGI Global. All rights reserved.
Original languageEnglish
Title of host publicationTechnologies Shaping Instruction and Distance Education: New Studies and Utilizations
PublisherIGI Global Publishing
Pages239-257
ISBN (Print)9781605669359, 9781605669342
DOIs
Publication statusPublished - 31 Dec 2009

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

The work described in this article was fully supported by a grant from City Univer­sity of Hong Kong (Project No. 7002137), the China Semantic Grid Research Plan (National Grand Fundamental Research 973 Program, Project no. 2003CB317002), and Natural Science Foundation of China (Project No.60603090). The author thanks the students enrolled in the CS3343 course at the City University of Hong Kong for their efforts in using and evaluating the system.

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