Recommending the Rules Related to Course Grades in Education Sector using SKCAAR Algorithm

In Education Sector, Recommendation System (RS) is used to indicate areas where students require strengthening their knowledge. This RS is also used to predict students’ academic performance in terms of grade in the courses. In this research article, we developed the recommendation system for suggesting students’ grade in the course based on grade obtained by other students in the course. For this RS, we collected the data from university result ledgers of four year Computer Science and Engineering Programme for Academic Year 2014-15 and prepared ten datasets. These ten datasets contain courses grade of semester-wise, year-wise and overall performance. We defined the terms – relevant and irrelevant rules for RS. The rule is relevant if it contains courses either prerequisite courses to each other or dependent on each other otherwise the rule is irrelevant. For this RS, we designed the algorithm named as SK
^C
A
^AR
algorithm which uses Simple K-means clustering algorithm and Apriori Association Rule algorithm. We applied this SK
^C
A
^AR
algorithm on generated ten datasets and compared with the result of A
^AR
algorithm which uses only Apriori Association Rule algorithm. It is found that number of recommendation rules obtained using SK
^C
A
^AR
algorithm is more than five to twenty times than number of recommendation rules obtained using A
^AR
algorithm.