Using data mining technologies to find learning activity rules for online learning

Ke Zhu

COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(12C) 79-83

Department of Information Technology, Henan Normal University, East of Construction Road, Xinxiang, China

Researchers are interested to improve learners’ and instructional designers’ performance in online learning systems. Learning Activity are of great importance at present, since they are the building blocks of different types of online learning systems. As the number of learning activity grows exponentially and our needs for learning expand equally dramatically, the lack of information or rules about the usability of learning activity places a critical and fundamental constraint on our ability to discover, manage, and use learning activity. This study presents a new approach of data mining and an assessment scheme by combining four computational intelligence theories, i.e., the Clustering Algorithms, the Classification Algorithms, and Association Algorithms, to identify the learning activity rules in online learning systems for learner and instructional designers. Experimental results indicate that the evaluation results of the proposed approach and scheme are improving the work of teachers in designing and searching, and also in the management of Los in a web-based learning environment according to the obtained learning objects usability rules.