The project involves a detailed exploratory data analysis (EDA) and the application of machine learning techniques to predict students' final grades. We delve into aspects such as demographics, study …

Dec 29, 2020 · A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of …

In this paper we use ML algorithms in order to predict the performance of students, taking into account both past semester grades and socioeconomic factors.

Some different methods for predicting student’s retention at various institutions or organizations throughout the world have been identified in the recent literature.

Utilizing the Open University Learning Analytics Dataset (OULAD), we integrate student demographic, educational, and assessment data to build a dataset suitable for supervised learning.

Jul 18, 2025 · Using 30 student features researchers processed the dataset and tested regression and decision tree algorithms to forecast student results. Exploring Machine Learning shows its ability to …

Oct 7, 2024 · The Student Performance Metrics Dataset provides a diverse collection of academic and non-academic attributes aimed at evaluating factors influencing student performance in higher …

In online learning, the accurate prediction of student per-formance is essential for timely interventions and personalized learning experiences.

By analyzing historical academic data, attendance, and engagement patterns, machine learning models accurately forecast student success and identify those at risk of underperformance. This enables …

The feature set comprised two types of features; curriculum-based features and student performance-based features. In this review, the authors suggest a cost-sensitive prediction algorithm that is based …