Comparative Analysis of Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics
- Title
- Comparative Analysis of Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics
- Creator
- Maheshwari A.; Malhotra A.; Hada B.S.; Ranka M.; Basha M.S.A.
- Description
- In the evolving landscape of educational research, the predictive analysis of student performance using data science has garnered significant interest. This study investigates the influence of diverse factors on academic outcomes, ranging from personal demographics to socioeconomic conditions, to enhance educational strategies and support mechanisms. We employed a diverse ml models to analyze a information containing academic records and socioeconomic information. The models tested include Logistic Regression, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Decision Trees. The process involved comprehensive data preprocessing, exploratory analysis, model training, and evaluation based on metrics such as precision, recall, accuracy, and F1 score. The results indicate that ensemble methods, specifically RF and GB, demonstrate superior efficacy in accurately predicting categories of student performance such as 'Enrolled,' 'Graduated,' and 'Dropped Out.' These models excelled in handling the complex interplay of varied predictors affecting student success. The results further underline the potential of advanced ensemble ML techniques in significantly outperforming the prediction accuracy in the academic domain, hence facilitating the tailoring of educational interventions to foster improved engagement and better outcomes for students. This has provided a comparative analysis of the methods that guide the future application of predictive analytics in education. 2024 IEEE.
- Source
- International Conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Academic outcomes; Educational Data Analytics; Machine Learning models
- Coverage
- Maheshwari A., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Malhotra A., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Hada B.S., CHRIST (Deemed to be University), School of Commerce, Finance and Accountancy, NCR, Delhi, India; Ranka M., Dayananda Sagar College of Arts, Science and Commerce Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to be University), GITAM School of Business, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835036404-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Maheshwari A.; Malhotra A.; Hada B.S.; Ranka M.; Basha M.S.A., “Comparative Analysis of Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19394.