The findings demonstrate the capability of AI models in minimising dropout risks and enhancing academic performance through prompt, data-informed assistance.
Abstract
Increasing student dropout rates, caused by social, academic, and personal obstacles, raise significant concerns for educational systems. This research introduces an AI-based method for the early detection and support of at-risk students utilizing the Student Final Grade Prediction dataset from two schools in Portugal. Following data preprocessing, which involved cleaning and feature extraction via t-distributed Stochastic Neighbor Embedding (t-SNE), a hybrid model integrating Tabular Data Network and Capsule Networks (TC-Net) was created for performance forecasting. The model obtained a Mean Squared Error (MSE) of 0.43 and an R-squared value of 60.57%, demonstrating robust predictive accuracy. Personalised strategies were then implemented, targeting 90% of at-risk students. Specifically, 80% participated in tailored learning plans, and 70% accessed tutoring support. The findings demonstrate the capability of AI models in minimising dropout risks and enhancing academic performance through prompt, data-informed assistance.
Student dropout remains one of the most significant challenges in higher education, affecting academic performance, financial sustainability, and strategic planning within universities. This study presents an approach to predicting student dropout risk using machine learning methods and educational analytics. The resea...
Arūnas Mincevičius· New Trends in Computer Scien...· 0 citations
: Student dropout in higher education constitutes a structural problem that affects academic quality and institutional sustainability. In Colombia, between 30% and 50% of students left their studies, highlighting the need to strengthen early detection systems. However, the performance of supervised models is often affe...
L. Caicedo, Juan Muñoz, N. Díaz· International Conference on...· 0 citations
Student dropout is a significant issue in higher education, affecting both students and institutions. Early identification of at-risk students can help universities improve retention. This study addresses student dropout prediction as a binary classification problem using 4,424 student records. To support realistic ear...
Saeed Al Sagherji, Rania Alhalaseh, Mohammad Abbadi· IEEE Jordan Conference on Ap...· 0 citations
Educational data can help identify, before the end of a semester, academic trajectories that deserve timely
attention. This study develops an academic risk profiling and tailored student guidance using machine learning techniques
for universities in Kinshasa. A quantitative, experimental, and predictive design was appl...
Augustin Pambi Tadiamba, Pierre K. Kafunda, David M. Kutangila et al.· International Journal of Inn...· 0 citations
Student dropout remains a major challenge for higher education institutions due to its academic, social, and economic consequences. Early identification of students at risk of dropping out is essential for implementing timely interventions; however, existing prediction approaches often rely on standalone machine learni...
Abdulalim M. Ibrahim, M. Abonazel, Abdul-Hadi N. Ahmed· Statistics, Optimization &am...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
