Abstract
Educational Data Mining (EDM) is an emerging, promising and interdisciplinary research area, which has an immense impact on student’s academic performance by analyzing educational data for improving teaching, learning experiences and, of course improving the institutional effectiveness. One of the most key facts in educational data is the significant growth of data and these statistics are rising swiftly without any benefit to the institution. In this short study, different studies conducted on the theme “EDM and interdisciplinary” were consulted and examined. Most of the authors have conducted studies on different systems which mostly include e-learning systems, student performance recommender systems (SPRS), conventional educational systems, adaptive and intelligent web based educational systems. The study of the past research has revealed that immense progress has been done in various domains of education and in different phases as well, but there still exists reasonable gap which needs to be examined in details as a future research study. Keywords: Educational Data Mining; Neural Networks; Classification; Association rules; Clustering; Prediction; k-means.
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