A data mining algorithm in distance learning
Dai Shangping, Ping Zhang · 2008
Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. One of the challenges in developing data mining systems is to integrate and coordinate existing data mining applications in a seamless manner so that cost- effective systems can be developed without the need of costly proprietary products. The popularity of distance education has grown rapidly over the last decade in higher education, yet many fundamental teaching- learning issues are still in debate. This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. In this paper we take advantage of the genetic algorithm (GA) designed specifically for discovering association rules. We propose a novel spatial mining algorithm, called ARMNGA(Association Rules Mining in Novel Genetic Algorithm), Compared to the algorithm in Reference[2] , the ARMNGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.