Machine Learning in Higher Education: Predicting Student Attrition Status Using Educational Data Mining

Machine Learning in Higher Education: Predicting Student Attrition Status Using Educational Data Mining

Garima Jaiswal (Indira Gandhi Delhi Technical University for Women, India), Arun Sharma (Indira Gandhi Delhi Technical University for Women, India) and Reeti Sarup (Indira Gandhi Delhi Technical University for Women, India)
DOI: 10.4018/978-1-5225-9643-1.ch002

Abstract

Machine learning aims to give computers the ability to automatically learn from data. It can enable computers to make intelligent decisions by recognizing complex patterns from data. Through data mining, humongous amounts of data can be explored and analyzed to extract useful information and find interesting patterns. Classification, a supervised learning technique, can be beneficial in predicting class labels for test data by referring the already labeled classes from available training data set. In this chapter, educational data mining techniques are applied over a student dataset to analyze the multifarious factors causing alarmingly high number of dropouts. This work focuses on predicting students at risk of dropping out using five classification algorithms, namely, K-NN, naive Bayes, decision tree, random forest, and support vector machine. This can assist in improving pedagogical practices in order to enhance the performance of students predicted at risk of dropping out, thus reducing the dropout rates in higher education.
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Background

Student Attrition in Higher Education

The statistics reported by Ministry of Human Resource Development, Department of School Education & Literacy, New Delhi showed that the percentage enrollment in Engineering & Technology in 2015-16 in India was 15.57%while the percentage of pass out in Engineering & Technology in 2015-16 in India was 13.42% (Ministry of Human Resource Development, 2018). This indicates that the number of students graduating or completing their higher educational studies is getting alarmingly lesser as compared to the number of initial enrollments. The problem of student attrition in higher education is of great concern and it may even discourage the incoming prospective candidates from pursuing higher education courses. When a student leaves a higher education program after getting enrolled, it causes loss of seat, money and time for both the institute and the student. Thus, it is essential to identify the impact of various factors such as parent income, previous scores, gender, demographic details etc. to assist in improving the student's performance. By building a model to predict and assist the students at risk of dropping out, the student retention rates can be improved in higher education programs.

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