Unraveling Academic Failure: Examining the Influence of Course Correlations on Student Performance Through Association Rules Algorithms

The field of information technology has witnessed rapid developments in data mining applications in education. In this context, association rule mining, one of the key aspects of data mining, has emerged as a specialized technique for extracting meaningful insights from sets of frequent items. Academic institutions face persistent challenges, including high failure rates and suboptimal academic achievement among students. This study examined the use of the Apriori algorithm to analyze data within an academic setting, focusing on students enrolled in the Department of Zoology at Sebha University. The primary objective was to study the effect of interconnectedness between academic courses on the overall academic performance of students in the Department of Zoology. The results revealed that high failure rates were observed consistently during the first two academic years. When the Arabic language course was linked to the botany course for second-semester students, an alarming failure rate of 80% occurred. Similarly, linking Arabic with Chemistry in the same semester resulted in a high failure rate of 75%. The English course during the same period showed a failure rate of 52%, which dropped significantly to 51% when linked to the zoology and chemistry courses. Conversely, linking other zoology courses to chemistry increased the failure rate by 59%, highlighting the subtle effects of course linkage on academic outcomes. These findings for the Department of Zoology are of fundamental importance to educators, providing valuable insights into improving student performance and identifying those who need additional support.