This comprehensive study constructs an advanced predictive analytics framework leveraging machine learning algorithms to forecast undergraduate academic performance within higher education institutions. Utilizing empirical student data from a technical institute, including Learning Management System (LMS) interaction logs and historical semester metrics, we implemented and evaluated four robust supervised classification architectures. Data preprocessing strictly utilized SMOTE techniques to systematically resolve extreme class imbalances. Experimental results demonstrated that ensemble methods, specifically XGBoost and Random Forest, significantly outperform baseline models, achieving a peak predictive accuracy of 92.4%. Mapped feature hierarchies reveal that early assignment submission latency and historical grade points act as critical early predictors for data-driven student intervention frameworks.
Abstract- The intersection of Artificial Intelligence (AI), learning analytics, and digital twin is revolutionising higher education into an intelligent, data-driven, and ultimately personalised educational ecosystem. In contrast with today’s Learning Management Systems (LMSs), w…
The CLAPE (Contextual Learner Attributes and Pupil Engagement) dataset is a validated multimodal educational dataset designed to support research on physiological student engagement using low-cost, non-invasive RGB webcam technology. The dataset integrates physiological pupil var…
Mobile Ad Hoc Networks (MANETs) operate without fixed infrastructure and are affected by node mobility, dynamic topology, unstable links, limited energy, and traffic congestion. This research introduces a Machine Learning-Based Performance Prediction Framework (ML-PPF) to predict…
This paper develops a machine learning framework for detecting and predicting liquidity sweep events in XAUUSD using event-based market microstructure analysis. Using 15-minute data from 2014–2024, the study formalizes liquidity sweeps as a binary classification problem evaluated…
This study focuses on the analysis and comparison of machine learning classification algorithms and hybrid machine learning models for predicting student academic performance. Educational Data Mining techniques are used to extract meaningful insights from student datasets. Variou…
Personalized learning is increasingly essential in higher education due to variations in student abilities, learning pace, and academic preparedness. This paper presents EduMentor-AI, a hybrid adaptive intelligence model designed to support personalized learning through the integ…