Learning Recommendation System based on Micro-Learning Materials and Data Mining Algorithm

• Main Authors: Huang, Sheng-Bo, 黃聖博
• Other Authors: Jeng,Yu-Lin
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/bhb7v9

碩士 === 南臺科技大學 === 資訊管理系 === 105 === Information overload is a most encountered problem in learning, especially for college students. Learners need to complete a lot of compulsory and elective subjects within limited time. Besides, the content of those subjects keeps diversity due to improvement of knowledge and technology. Therefore, it is difficult to learn full knowledge only through textbook materials. Learners needs to seek extra learning materials via Internet. But it contains a lot amount of information which makes learning or reading time-consuming. And it also causes information overload issue. Besides, every learner has different learning ability and prerequisite knowledge due to individual difference situation. An individual difference situation means that learners have same learning materials and tutors but with different learning outcomes. In order to improve information overload issue and individual differences situation, this research proposes a learning recommendation system based on micro-learning materials and data mining algorithm. The system utilizes automatic summarization technology and personal recommendation mechanism to overcome the issues mentioned above. The experiment reveals that the automatic summarization technology produces highly readable content for readers. And the experiment also reveals that most learners are able to get different recommended learning path according to their learning history from the proposed system.