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

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Neeta Sharma 1,* Shanmuganathan Appukutti 2 Umang Garg 3 Jayati Mukherjee 4 Sneha Mishra 4

1. Department of CS, Noida International University, Noida, India

2. Northern Institute of Engineering and Technology, Alwar, India

3. Department of CSE, Graphic Era Hill University, Dehradun, India

4. Department of CSE, Noida International University, Noida, India

* Corresponding author.


Received: 23 Oct. 2022 / Revised: 19 Dec. 2022 / Accepted: 12 Jan. 2023 / Published: 8 Feb. 2023

Index Terms

Algorithms, Extra-curricular activities, KNN, Decision Tree, Random Forest, Machine Learning, Prediction


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.

Cite This Paper

Neeta Sharma, Shanmuganathan Appukutti, Umang Garg, Jayati Mukherjee, Sneha Mishra, "Analysis of Student’s Academic Performance based on their Time Spent on Extra-Curricular Activities using Machine Learning Techniques", International Journal of Modern Education and Computer Science(IJMECS), Vol.15, No.1, pp. 46-57, 2023. DOI:10.5815/ijmecs.2023.01.04


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