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

doi:10.1117/12.2052978

24 December 2013 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

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90672F (2013) https://doi.org/10.1117/12.2052978
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

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.

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Keng-Hou Liew, Yu-Shih Lin, Yi-Chun Chang, and Chih-Ping Chu "An effective self-assessment based on concept map extraction from test-sheet for personalized learning", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90672F (24 December 2013); https://doi.org/10.1117/12.2052978

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