Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques

Assessment as a dynamic process produces data that reasonable conclusions are derived by stakeholders for decision making that expectedly impact on students' learning outcomes. The data mining methodology while extracting useful, valid patterns from higher education database environment contribute to proactively ensuring students maximize their academic output. This paper develops a methodology by the derivation of performance prediction indicators to deploying a simple student performance assessment and monitoring system within a teaching and learning environment by mainly focusing on performance monitoring of students' continuous assessment (tests) and examination scores in order to predict their final achievement status upon graduation. Based on various data mining techniques (DMT) and the application of machine learning processes, rules are derived that enable the classification of students in their predicted classes. The deployment of the prototyped solution, integrates measuring, 'recycling' and reporting procedures in the new system to optimize prediction accuracy.

1. Introduction

Performance monitoring involves assessments which serve a vital role in providing information that is geared to help students, teachers, administrators, and policy makers take decisions. [1] The changing factors in contemporary education has led to the quest to effectively and efficiently monitor student performance in educational institutions, which is now moving away from the traditional measurement and evaluation techniques to the use of DMT which employs various intrusive data penetration and investigation methods to isolate vital implicit or hidden information. Due to the fact that several new technologies have contributed and generated huge explicit knowledge, causing implicit knowledge to be unobserved and stacked away within huge amounts of data. The main attribute of data mining is that it subsumes Knowledge Discovery (KD) which according to [2] is a nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data processes, thereby contributing to predicting trends of outcomes by profiling performance attributes that supports effective decisions making. This paper deploys theory and practice of data mining as it relates to student performance and monitoring Associate degree program in a Community College.