Course Content Information Gain Analysis Based on ID3－Case on SCJP

• Main Authors: Yu Chen Shan, 游振山
• Other Authors: Lawrence Y. Deng
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/65270964158128680978

碩士 === 聖約翰科技大學 === 電機工程系碩士在職專班 === 97 === ID3 is one of efficient techniques of making a decision tree to mine and classify from symbolic data without much computation. In this paper, we applied the ID3-like algorithm to build up decision tree by analysis the best information gain of the student’s SCJP testing attributes as the root of the decision tree. The recursive procedure is applied to remains branches for inducing remains level. The main purpose of this paper is to derive the decision tree as the SCJP tester’s learning achievement and then to help the learners to regulate their learning topic/chapter to get the SCJP certification in near future. The primarily result showed that not only the decision tree providing the self-regulation suggestion for testers but also helping the lecturer to adapt the priorities of their teaching topics/chapters. This approach is a helpful guidance in vocational education that could help the learner to get the professional certification in near future. Anyway, the more professional certifications the more competition of learner gets. We addressed in analysis the real testing data by ID3 algorithm and then to construct the e-learning system to provide the learner can get the decision tree for self regulation. We believed this approach can apply to more online professional certifications.