Discover Frequent Patterns from Academic Data Of Student Information System

Abstract

Numerous researchers have explored the realm of data mining in education. The primary goal is knowledge discovery, aiming to support staff in efficiently managing educational units, refining student activities, and ultimately elevating learning outcomes. In this study, we utilize association rules mining, implementing the Apriori algorithm to extract insights from academic datasets sourced from the student information system of Sebha University, Libya. Genuine data is sourced from the cloud server. The algorithm is then applied to unveil relationships among 11 attributes within students' academic records spanning four years. The resulting patterns undergo experimental evaluation, considering support and confidence values. These specific rules are subsequently categorised into four classes and scrutinised for further validation. The proposed method yields valuable patterns pertaining to students' academic progress and retains crucial insights for predicting decisions regarding course additions and drops.