An Exploratory Study on the Use of Machine Learning to Predict Student Academic Performance

An Exploratory Study on the Use of Machine Learning to Predict Student Academic Performance

Patrick Kenekayoro
Copyright: © 2018 |Pages: 13
DOI: 10.4018/IJKBO.2018100104
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Optimal student performance is integral for successful higher education institutions. The consensus is that big data analytics can be used to identify ways for achieving better student academic performance. This article used support vector machines to predict future student performance in computing and mathematics disciplines based on past scores in computing, mathematics and statistics subjects. Past subjects passed by students were ranked with state of art feature selection techniques in an attempt to identify any connection between good performance in a particular discipline and past subject knowledge. Up to 80% classification accuracy was achieved with support vector machines, demonstrating that this method can be developed to produce recommender or guidance systems for students, however the classification model will still benefit from more training examples. The results from this research reemphasizes the possibility and benefits of using machine learning techniques to improve teaching and learning in higher education institutions.
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Literature Review

Educational Data Mining (EDM) has been defined as “…scientific inquiry focused on developments of methods for making discoveries with data from educational settings and using those methods to better understand students and the settings which they learn in…” (Baker, 2010). Understanding students through educational data mining can give new insights to ways that can improve student academic performance. Academic success is seen as a critical factor for individual success in contemporary society (Pritchard & Wilson, 2003). If students’ academic performance can be previously predicted, it gives policy makers the opportunity to introduce policies that will improve student academic success rate, thereby increasing the likelihood of successful completion of a higher education degree. Also, creating predictive models that can be used for early identification of weak students who will be at risk is beneficial for reducing failure or dropout rates in higher education institutions (Raju & Schumacker, 2016).

Techniques for predicting student performance have been researched extensively. Historically, correlation and multiple regression are the traditional methods used to investigate the extent to which socioeconomic or psychological factors can positively or negatively affect a student’s academic performance. For example, Figilo and Kenny (2007) have used correlation to investigate the relationship between teacher incentives and student performance. However, machine learning techniques can use these socioeconomic, psychological or other factors to also predict how well a student will perform in a particular subject, thus it is a useful technique that can be used in the design of systems that can provide real time guidance to students.

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