An effective self-assessment based on concept map extraction from test-sheet for personalized learning

Keng Hou Liew, Yu Shih Lin, Yi Chun Chang, Chih Ping Chu

研究成果: Conference contribution

摘要

Examination is a traditional way to assess learners' learning status, progress and performance after a learning activity. Except the test grade, a test sheet hides some implicit information such as test concepts, their relationships, importance, and prerequisite. The implicit information can be extracted and constructed a concept map for considering (1) the test concepts covered in the same question means these test concepts have strong relationships, and (2) questions in the same test sheet means the test concepts are relative. Concept map has been successfully employed in many researches to help instructors and learners organize relationships among concepts. However, concept map construction depends on experts who need to take effort and time for the organization of the domain knowledge. In addition, the previous researches regarding to automatic concept map construction are limited to consider all learners of a class, which have not considered personalized learning. To cope with this problem, this paper proposes a new approach to automatically extract and construct concept map based on implicit information in a test sheet. Furthermore, the proposed approach also can help learner for self-assessment and self-diagnosis. Finally, an example is given to depict the effectiveness of proposed approach.

原文English
主出版物標題Sixth International Conference on Machine Vision, ICMV 2013
發行者SPIE
ISBN(列印)9780819499967
DOIs
出版狀態Published - 2013 一月 1
事件6th International Conference on Machine Vision, ICMV 2013 - London, United Kingdom
持續時間: 2013 十一月 162013 十一月 17

出版系列

名字Proceedings of SPIE - The International Society for Optical Engineering
9067
ISSN(列印)0277-786X
ISSN(電子)1996-756X

Conference

Conference6th International Conference on Machine Vision, ICMV 2013
國家United Kingdom
城市London
期間13-11-1613-11-17

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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