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.