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Investigating Report Cards to Predict the Academic Performance of New MBA Students


Affiliations
1 Associate Professor, University School of Business, Chandigarh University, Gharuan, Mohali - 140 413, Punjab, India
2 Assistant Professor, Sharda School of Business Studies (SSBS), Sharda University, Gr. Noida - 201 310, Uttar Pradesh, India
     

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Report cards not only attest to students’ academic performance in a program, but they also contain several valuable insights. The study was based on the analysis of information readily available on the report cards of MBA Ist semester students. Report cards of 1,301 students enrolled in Dr. APJ Abdul Kalam Technical University, India (Asia’s largest affiliating University) affiliated institutions were examined. The study identified two types of courses: quantitative courses and non-quantitative courses. Furthermore, students’ performance varied significantly between categories, and quantitative courses were more difficult to score than non-quantitative courses. Decision models capable of predicting ‘overall academic performance’ were developed using multiple linear regression analysis. Report cards were used to collect data on the dependent variable (overall academic performance) and five predictor variables (gender, ethnicity, and students’ performance in three quantitative courses, namely managerial economics, business statistics, and business accounting). Finally, the study discovered that the predictor variable ‘AGP in Managerial Economics’ had the most significant impact on overall academic performance. Then came ‘AGP in Business Statistics,’ ‘AGP in Business Accounting,’ and the gender of the students.

Keywords

Academic Performance, Quantitative Courses, Gender, Ethnicity, AGP (average Grade Points), SGPA (Semester Grade Point Average).

JEL Classification Codes : I20, I23, M00, M10.

Paper Submission Date : June 15, 2021 ; Paper Sent Back for Revision : January 18, 2022 ; Paper Acceptance Date :February 20, 2022 ; Paper Published Online : March 15, 2022.

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  • Investigating Report Cards to Predict the Academic Performance of New MBA Students

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Authors

Amitabh Mishra
Associate Professor, University School of Business, Chandigarh University, Gharuan, Mohali - 140 413, Punjab, India
Ankur Agrawal
Assistant Professor, Sharda School of Business Studies (SSBS), Sharda University, Gr. Noida - 201 310, Uttar Pradesh, India

Abstract


Report cards not only attest to students’ academic performance in a program, but they also contain several valuable insights. The study was based on the analysis of information readily available on the report cards of MBA Ist semester students. Report cards of 1,301 students enrolled in Dr. APJ Abdul Kalam Technical University, India (Asia’s largest affiliating University) affiliated institutions were examined. The study identified two types of courses: quantitative courses and non-quantitative courses. Furthermore, students’ performance varied significantly between categories, and quantitative courses were more difficult to score than non-quantitative courses. Decision models capable of predicting ‘overall academic performance’ were developed using multiple linear regression analysis. Report cards were used to collect data on the dependent variable (overall academic performance) and five predictor variables (gender, ethnicity, and students’ performance in three quantitative courses, namely managerial economics, business statistics, and business accounting). Finally, the study discovered that the predictor variable ‘AGP in Managerial Economics’ had the most significant impact on overall academic performance. Then came ‘AGP in Business Statistics,’ ‘AGP in Business Accounting,’ and the gender of the students.

Keywords


Academic Performance, Quantitative Courses, Gender, Ethnicity, AGP (average Grade Points), SGPA (Semester Grade Point Average).

JEL Classification Codes : I20, I23, M00, M10.

Paper Submission Date : June 15, 2021 ; Paper Sent Back for Revision : January 18, 2022 ; Paper Acceptance Date :February 20, 2022 ; Paper Published Online : March 15, 2022.




DOI: https://doi.org/10.17010/pijom%2F2022%2Fv15i3%2F168846