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Using learning style‐based diagnosis tool to enhance collaborative learning in an undergraduate engineering curriculum
Author(s) -
Huang ChennJung,
Liao JiaJian,
Shen HungYen,
Aye Nwe Ni,
Wang YuWu,
Chen HongXin,
Yang DianXiu,
Luo YunCheng,
Chuang YiTa
Publication year - 2011
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.20359
Subject(s) - learning styles , computer science , style (visual arts) , cooperative learning , collaborative learning , artificial intelligence , active learning (machine learning) , educational technology , synchronous learning , curriculum , experiential learning , open learning , personalized learning , machine learning , knowledge management , mathematics education , teaching method , psychology , pedagogy , archaeology , history
In this study, an intelligent learning style aware diagnosis agent for computer‐supported cooperative learning is proposed. Learners are first assigned to heterogeneous groups based on their learning styles questionnaire given right before the beginning of learning activities on the e‐learning platform. The proposed diagnosis agent then scrutinizes each learner's learning portfolio on e‐learning platform and automatically issues feedback messages in case some learner's behavior that is unfitted to his/her learning styles or devious argument on discussion board or wiki is detected. The Moodle, an open‐source software e‐learning platform, is used to establish the cooperative learning environment for this study. The experimental results reveal that the proposed learning style aware diagnosis agent indeed boosts the performance of the learners. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 739–746, 2011