Academic Performance Analysis Framework for Higher Education by Applying Data Mining Techniques

Technology advancement and applications of various digital systems will help academic institutions to generate large amounts of data from different types of operational processes. Appropriate applications of data mining techniques in these large datasets can help the institutions to efficiently analyze hidden information and hidden patterns of data. Application of effective data mining techniques on educational dataset will help converting available data into knowledge. Then the mined knowledge can be filtered for decision-making. The application of appropriate data mining techniques and the results will support the academic institutions in their performance evaluation and improvement. Application of data mining techniques in e-learning systems will advance and enhance learning process in an educational institution. This paper presents a framework for predicting learner performance in higher education institutions by applying appropriate data mining techniques using data collected mainly from data collected from the Learning Management System (LMS) and other systems within the organization. This framework will help the institutions to make appropriate decisions about the learners' performance. Various clustering algorithms are suggested to apply in the data collected from various activities of LMS.