Learning Analytics: A Data Mining and Machine Learning Perspective

Abstract

Tremendous proliferation in data generation in the past few years has paved the way for new research and the development of new and improved techniques and algorithms in different fields of science and education. Initially terms like educational data mining emerged as a branch of data mining borrowing techniques from its ancestor. The challenges brought about by this large and heterogeneous data are diverse and needs a greater serious technical treatment. New and emerging fields like learning analytics have been introduced to manage the complexities of this data deluge. Learning analytics deals with data in the context of learner and the learning environment to improve the overall learning experience.  The ultimate aim of the field is to make use of the data about learners and their environments to gain insights into the learning process using some of the well-known techniques and algorithms from the fields of data mining and machine learning.  The process involves collecting, analysis of data and reporting the results to understand and optimize the learning experience.  The fields of data mining and academic analytics closely related to learning analytics. Systematic Literature Review (SLR) is a robust, organized and rigorous literature review and reporting process aimed at identifying, collecting and synthesizing the relevant literature on a research question according to specified criteria. The process is more unbiased and balanced by systematic sequence of steps. This paper presents a systematic literature review by first developing the systematic literature review protocol and then discussing the main findings of the literature review by especially focusing on the applications and uses of machine learning and data mining techniques in the domain of learning analytics.   Index Terms—Systematic Literature Review (SLR), Learning Analytics (LA), Big Data, Educational Data Mining (EDM), Machine Learning (ML).