Predicting Academic Performance In Blended Learning By Using Data Mining Classification Techniques

Internet and Web technologies provide students with the ease of communication to lecturers, accessing learning resources, and submitting the assignment. One of that technologies is Blended Learning. Blended learning has been growing in demand and popularity and widely used in the modern higher education system with the implementation of a learning management system (LMS). As a result, Blended Learning generates large amounts of student’s information that can be used to invent valuable patterns and get hidden and useful information. This study analyzed data extracted from a Moodle-based blended learning course in STMIK XYZ that is called SIMPONI and SPON, to build a student model that predicts academic performance. Data Mining Classification Techniques were used to predict it based on Semester GPA. Various factors like demographics, economics and information about previous education are considered in analyzing academic performance. Rapid Miner software package was used for prediction and the result is a model by which could determine academic performance: passed with a CGPA above or equal to 2.5 and failed with a CGPA below 2.5 This model may give information for STMIK XYZ to early intervention and to outcome the poor GPA. Various classification algorithms are used to select the best algorithm in finding the better prediction model.