Implementing Learning Analytic Tools in Predicting Students’ Performance in a Business School

R. Sujatha () and B. Uma Maheswari ()
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R. Sujatha: PSG Institute of Management
B. Uma Maheswari: PSG Institute of Management

A chapter in Applied Advanced Analytics, 2021, pp 135-146 from Springer

Abstract: Abstract In recent times, information technology and big data are two buzz words that have impacted all sectors including education. Research in the field of educational data mining and learning analytics is in its nascent stage. Applying analytics in education is the need of the hour, especially in the context of a developing economy like India. It is time for educational institutions to use machine learning tools to enhance teaching–learning experience. This study deploys learning analytics technique using the data of students undergoing a post-graduate management program and attempts to create a system of preventive feedback mechanism for faculty and students. In the first part, logistic regression was used to identify the academic status of foundation courses in the first semester. Six models were developed, and ‘specificity’ scores were used to test the validity of the models. In the second part of the study, the stepwise regression model was used to predict the marks of the student in the capstone course. The results showed that as the student progresses into second semester courses, the tenth and higher secondary board examination scores become irrelevant. Performance in the first semester courses greatly influences the results of the second semester. Deployment of the models developed in this study would go a long way in not only enhancing students’ performance but also more fruitful student–faculty engagement.

Keywords: Predictive analytics; Academic status; Education; Model building (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-33-6656-5_12

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DOI: 10.1007/978-981-33-6656-5_12

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