The analysis of data generated by higher educational institutes has the potential of revealing interesting facets of student learning behavior. Classification is a popularly explored area in Educational Data Mining for predicting student performance. Using student behavioral data, this study compares the performance of a broad range of classification techniques to find a qualitative model for the prediction of student performance. Rebalancing of data has also been explored to verify if it leads to the creation of better classification models. The experimental results, validated using well-established evaluation matrices, presented potentially significant outcomes which may be used for reshaping the learning paradigm.
In higher education, the demand for improved information in relation to educational and learning outcomes is greater than ever before. Leveraging technology, new models of education have emerged that are not only improving modes of lecture delivery and information retention, but also generating huge amounts of data. This data is potentially a gold mine that needs to be explored to uncover patterns associated with student behavior and how information is processed, retained and used by the students. This chapter proposes a generic model that uses the techniques of educational data mining to explore and analyze Big Data being generated by the education sector. This chapter also examines the various questions that can be answered using educational data mining methods and how the discovered patterns can be used to enrich the learning experience of a student as well as help teachers make pedagogical decisions.
Higher Educational Institutes hold a central and vital role in the knowledge economy. Knowledge generated by these institutes needs to be collected and disseminated to bring about improvement in not only student learning and performance, but also in educational practices, processes and the society in general. Sadly, in many institutes, this knowledge is rarely recognized, captured, analysed or utilized. Apart from the need for improved learning outcomes, rapid growth in educational data and technology has motivated several educational institutes to implement Knowledge Management (KM). KM is the key to enable educational institutes reach their goals and achieve their objectives. This paper explores the basic concept of KM and addresses the need of KM in higher educational institutes by proposing a framework to improve knowledge processes and practices in academia. This paper also highlights the role of data mining in facilitating KM in higher education through a case study that focuses on aspects of student management. Using data mining methods of pattern discovery and association rule mining, interesting patterns have been discovered from academic data that can be used for effective pedagogical decision making. The applications and challenges faced in the implementation of KM within higher educational institutes have also been briefly discussed.
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