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Recently, a lot of open source e-learning platforms have been offered for free in the Internet. We thus incorporate the intelligent diagnosis and assessment tool into an open software e-learning platform developed for programming language courses, wherein the proposed learning diagnosis assessment tools based on text mining and machine learning techniques are employed to alleviate the loading of the teachers. Experiments were conducted in two introductory-undergraduate programming courses to examine the effectiveness of the proposed diagnosis and assessment tools. The learners' work including the source code and comments were processed by the proposed text mining and machine learning techniques. This system also provides immediate feedback and high-quality evaluation results to guide the learners with poor performance. Our experimental results reveal that the proposed work can effectively assist the low-ability learners. (Contains 6 tables and 13 figures.)

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 [ABSTRACT FROM AUTHOR]