Analysing Student’s Academic Performance in Relation to Psychosocial Aspects Using AI
Analysing Student’s Academic Performance in Relation to Psychosocial Aspects Using AI |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-1 |
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| Year of Publication : 2024 | ||
| Author : Jaya Gera, Ekta Bhambri Marwaha,Reema Thareja, Rashi Thareja, Shefali Gupta, Aruna Jain |
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| DOI : 10.14445/22315381/IJETT-V72I1P124 | ||
How to Cite?
Jaya Gera, Ekta Bhambri Marwaha,Reema Thareja, Rashi Thareja, Shefali Gupta, Aruna Jain, "Analysing Student’s Academic Performance in Relation to Psychosocial Aspects Using AI," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 243-251, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P124
Abstract
During the pandemic and after the reopening of colleges, we all are witnessing a new normal situation, and teachers and students are trying to cope with it to stay strongly connected with the roots of the education system. Given today’s uncertainties, it is vital to understand the psychosocial aspects that have a strong influence on the academic performance of a student. The academic performance of any student depends on a complex interplay of various factors, such as mental health, emotional health, physical health, social and spiritual health. This paper, therefore, examines the importance of these factors on students’ academic performance during the shift from offline teaching to online teaching and hybrid mode in the current situation. The data was collected through a self-designed comprehensive questionnaire, which consisted of statements from 214 undergraduate students studying in various streams of the University of Delhi. The data collected was analysed using techniques like Feature Selection, Regression, Neural Networks, Naïve Bayes machine learning algorithm, and multi-dimensional analysis was used to analyse as well as predict a student’s academic performance based on psychosocial parameters. Statistical tools, SPSS and programming languages like R and Python were used to implement codes to dig into data and predict results.
Keywords
Academic Performance, Psychosocial aspects, Mental Health, Social-Media, Spiritual health.
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