Abstract:
With the rapid development of information technology, computer technology has been getting more widely used in daily life, thus, it is necessary for each university graduates grasp basic technical skills of computers. However, In Sri Lanka a teacher will be responsible for teaching many university students basic computer courses, so it is difficult to ensure the quality of the teaching with the enlarged students’ enrollment in Sri Lankan Universities.
In this researchweproposed combining data mining and online examination system to improve teaching of basic computer courses through Student Academic Performance Monitoring and Evaluation.The data mining methodology while extracting useful, valid patterns from higher education database environment contribute to proactively ensuring students maximize their academic output. This work 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.
