Mining the Student Programming Performance using Rough Set
One of the powerful data mining analysis is it can generates different set of knowledge when similar problem is presented to different data mining techniques. In this paper, a programming dataset was mined using rough set in order to investigate the significant factors that may influence students programming performance based on information from previous student performance. Then, the result was compared with other researches which had previously explored the data using statistic, clustering, and association rule. The dataset consists of 419 records with 70 attributes were pre-processed and then mined using rough set. The result indicates rough set has identified several new characteristics. The student who has been exposed to programming prior to entering university and obtained average score in Mathematics, English, and Malay Language subject during secondary Malaysian School Certificate (SPM) examination were among strong indicators that contributes to good programming grades. Besides that, the personality factor; the investigative and social type plus average cognitive person were also found as important factors that influence programming. This finding can be a guideline for the faculty to plan teaching and learning program for new registered student.
Mohamad Farhan Mohamad Mohsin Cik Fazilah Hibadullah Norita Md Norwawi Mohd Helmy Abd Wahab
Universiti Utara Malaysia Universti Sains Islam Malaysia Universiti Tun Hussein Onn Malaysia
国际会议
The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)
杭州
英文
478-483
2010-11-15（万方平台首次上网日期，不代表论文的发表时间）