Monitoring Student Performance Based on Educational Measurements

This paper presented an approach to monitoring student learning performance based on their assessment. The proposed information technology consists of two phases: modeling and diagnostics. During the modeling phase, each student's average grades for each semester are calculated, and then the dataset is clustered to identify patterns of student learning performance. The diagnostic phase uses the set of patterns and the student's current grade set to search for the closest pattern to the student's average grade set. The proposed information technology was tested on the success rate of undergraduate students in the Software Engineering program. The results showed that the proposed “dynamic” prediction method improves the work of supervisors with students by providing them with additional information.