Designing and Implementing a Personalized Remedial Learning System for Enhancing the Programming Learning
Journal of Educational Technology & Society Volume 16, Number 4, ISSN 1176-3647 e-ISSN 1176-3647
In recent years, the demand for computer programming professionals has increased rapidly. These computer engineers not only play a key role in the national development of the computing and software industries, they also have a significant influence on the broader national knowledge industry. Therefore, one of the objectives of information education in Taiwan is to cultivate elite talents specializing in computer programming so as to improve Taiwan's national competitiveness. Although programming is a major fundamental subject for students in information sciences, learning to master programming languages is far from easy. Accordingly, this study aimed to establish a personalized remedial learning system to assist learners in remedial learning after an online assessment. The proposed system adopted the fuzzy logic theory to construct an appropriate learning path based on the learners' misconceptions found in a preceding quiz. With concepts of each course constructed in a learning path, the proposed system will select the most suitable remedial material for a learner in terms of learner preference to facilitate more efficient remedial learning. Finally, the system, proven by several conducted experiments, can offer a comprehensive and stable remedial learning environment for any e-learning programs. The analysis of learners' achievements confirmed that the method of this study has achieved the effects of remedial learning and adaptive learning. (Contains 7 tables and 8 figures.)
Hsieh, T.C., Lee, M.C. & Su, C.Y. (2013). Designing and Implementing a Personalized Remedial Learning System for Enhancing the Programming Learning. Journal of Educational Technology & Society, 16(4), 32-46.
Cited ByView References & Citations Map
Chen-Wei Hsieh; Sherry Chen, Graduate Institute of Network Learning Technology, National Central University
The International Review of Research in Open and Distributed Learning Vol. 17, No. 1 (Feb 02, 2016)
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