Paper Contents
Abstract
This systematic review investigates the current land-scape of student performance prediction by integrating socio-economic and behavioral data with machine learning techniques.The review examines advancements, common datasets, methodologies, and limitations in predictive models across various educational settings. Ten selected papers provide insight into how socio-economic and behavioral data enhance the predictive accuracyof models, particularly using machine learning algorithms likeRandom Forest and Support Vector Machines (SVM). Futureresearch directions include expanding datasets and incorporatingreal-time behavioral data for more robust predictions.
Copyright
Copyright © 2024 Mohd. Afshaan Zuber Shaikh. This is an open access article distributed under the Creative Commons Attribution License.
