Version 1
: Received: 9 August 2021 / Approved: 11 August 2021 / Online: 11 August 2021 (11:23:48 CEST)
How to cite:
Lokhande, S.; Bahel, V. Effect of Non-Academic Parameters on Student’s Performance. Preprints2021, 2021080256. https://doi.org/10.20944/preprints202108.0256.v1
Lokhande, S.; Bahel, V. Effect of Non-Academic Parameters on Student’s Performance. Preprints 2021, 2021080256. https://doi.org/10.20944/preprints202108.0256.v1
Lokhande, S.; Bahel, V. Effect of Non-Academic Parameters on Student’s Performance. Preprints2021, 2021080256. https://doi.org/10.20944/preprints202108.0256.v1
APA Style
Lokhande, S., & Bahel, V. (2021). Effect of Non-Academic Parameters on Student’s Performance. Preprints. https://doi.org/10.20944/preprints202108.0256.v1
Chicago/Turabian Style
Lokhande, S. and Vedant Bahel. 2021 "Effect of Non-Academic Parameters on Student’s Performance" Preprints. https://doi.org/10.20944/preprints202108.0256.v1
Abstract
With the exponential growth in today’s technology and its expanding areas of application it has become vital to incorporate it in education. One such application is Knowledge Discovery in Databases (KDD) which is a subset of data mining. KDD deals with extracting useful information and meaningful patterns from the database that were not known before. This study is a detailed application of KDD and focuses on analyzing why a particular set of students performed better than others and what factors influenced the results. The study is conducted on a dataset of 480 students and across 16 different features. The authors implemented 4 major classification techniques namely Logistic Regression, Decision Tree, Random Forest and XGB classifier. Obtaining the key features from the top performing ML algorithms that have a major impact on the performance of the student, the study takes these features as a baseline for further analysis. Further data analysis highlights patterns in the data. The study concludes that there are a lot of non-academic factors that influence the overall performance of a student and should be taken into consideration by universities and other relevant bodies.
Keywords
Learning Analytics, Education, Educational Data Mining, Pattern Recognition, Data Visualization.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
