Publication: A group decision approach to developing concept-effect models for diagnosing student learning problems in mathematics
Issued Date
2013-05-01
Resource Type
ISSN
14678535
00071013
00071013
Other identifier(s)
2-s2.0-84876114085
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
British Journal of Educational Technology. Vol.44, No.3 (2013), 453-468
Suggested Citation
Gwo Jen Hwang, Patcharin Panjaburee, Wannapong Triampo, Bo Ying Shih A group decision approach to developing concept-effect models for diagnosing student learning problems in mathematics. British Journal of Educational Technology. Vol.44, No.3 (2013), 453-468. doi:10.1111/j.1467-8535.2012.01319.x Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/32790
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Title
A group decision approach to developing concept-effect models for diagnosing student learning problems in mathematics
Abstract
Diagnosing student learning barriers has been recognized as the most fundamental and important issue for improving the learning achievements of students. In the past decade, several learning diagnosis approaches have been proposed based on the concept-effect relationship (CER) model. However, past studies have shown that the effectiveness of this model heavily depends on the concept relationship knowledge provided by the domain experts (eg, experienced teachers or educators for a specified subject); ie, the performance of the developed learning diagnosis systems could be significantly affected by subjective opinions, ignorance or insufficient knowledge if those concept relationships are derived from a single domain expert. To cope with this problem, this study proposes a group decision approach for developing the CER model with the cooperation of multiple domain experts. Based on the proposed approach, a testing and diagnostic system has been implemented; moreover, an experiment has been conducted to evaluate the effectiveness of this new approach. The experimental results show that this approach is able to develop quality CER models, and hence the low-achievement students who received the generated learning suggestions had significantly better learning achievements than those who learned with the previous approach. © 2012 The Authors. British Journal of Educational Technology © 2012 BERA.