Shanmuganathan Appukutti

Work place: Northern Institute of Engineering and Technology, Alwar, India



Research Interests: Educational Technology, Higher Education, Special Education


Prof. (Dr.) A.Shanmuganathan is a committed academician, researcher and speaker, has vast professional experiences over 26 years in Academics and in Industries. As a professor in the Dept. of Mechanical Engg. He has published patents and a number of research papers in leading international journals and conferences including Elsevier and Scopus indexed journals. He has worked abroad in Government Universities for two decades and worked as Dean, Principal and Director-Academics during his tenure emphasizing collaborated-working-model in Higher Educational Institutions (HEIs). He has extended his consultancy services to industries and local communities ensuring excellence of University-Industry-Government Linkage. He did research works contributing to “University-Industry-Government” linkage in King Khalid University, Saudi Arabia. He did mechanical projects where students are involved to improve their technical skills in making them Industry-Ready

Author Articles
Analysis of Student’s Academic Performance based on their Time Spent on Extra-Curricular Activities using Machine Learning Techniques

By Neeta Sharma Shanmuganathan Appukutti Umang Garg Jayati Mukherjee Sneha Mishra

DOI:, Pub. Date: 8 Feb. 2023

The foundational tenet of any nation's prosperity, character, and progress is education. Thus, a lot of emphasis is laid on quality of education and education delivery system in India with current financial year (2022-23) education budget outlay of Rs. 1,04,277.72 crores. This research contributes in analyzing how students perform in academics depending upon the time spent on their extracurricular activities with the help of three Machine Learning prediction algorithms namely Decision Tree, Random Forest and KNN. Additionally, in order to comprehend the underlying causes of the shortcomings in each machine learning technique, comparisons of the prediction outcomes obtained by these various techniques are made. On our dataset, the Decision Tree outscored all other algorithms, achieving F1 84 and an accuracy of 85%. The research, which is at an introductory level, is meant to open the door for more complexes, specialised, and in-depth studies in the area of predicting the performance in academics.

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