A Convergence of Mining and Machine Learning: The New Angle for Educational Data Mining

Abstract

In the digital era, there is an accumulation of large volume of data, and the biggest challenge being faced by humans is to derive meaningful information from these data. In such a scenario, data mining techniques become important to unearth the hitherto unknown relationship from data. There is an urgent need for scientific research in education, as it aimed at enhancing student’s cognitive learning and social development. With increasing educational institutions, the large amount of data is accumulated, which is unstructured and not useful neither for students nor for teachers. Therefore, the major stress is on improving the quality of learning in schools and institutions, and there is a need to make significant progress on access to schooling and quality of learning in students. The computational intelligence is achieved through data mining techniques, which can break down information to enable it to make predictions. This chapter aims at discussing the utilization of educational data mining, learning analytics, and predictive intelligence techniques in educational field and models of machine learning, that is, unsupervised and supervised learning. There are some inbuilt libraries, such as Tensor flow, Keras, and Python libraries, discussed in this chapter, which helps in building, training, and applying it into educational data classification.