Pattern analysis of blooms knowledge level students performance using association rule mining

Learning analytics a variant of educational data mining is a process of collection, analysis and reporting of data about learners and their contexts. Analyzing performance of students is a challenging and important task. Use of temporal association mining methods of data mining technique can be a better solution for real time student performance analysis. By using association rule mining approach, students' performance in courses and faculty performance in course conduction can be analyzed to determine the variation in course performance. This paper attempts an approach of exploiting association mining techniques to determine real time patterns in students' data to analyze students' performance by using the performance of students. This analysis helps in taking remedial actions for the forthcoming batch students' performance. In this paper we have done the analysis, how students' performances vary with respect to different blooms knowledge level mapped questions. We have performed the pattern analysis of students performance with respect to blooms level mapped questions using apriori algorithm of association rule mining to provide the recommendations.