The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies

ME Dogan, T Goru Dogan, A Bozkurt - Applied Sciences, 2023 - mdpi.com
Artificial intelligence (AI) technologies are used in many dimensions of our lives, including
education. Motivated by the increasing use of AI technologies and the current state of the art …

Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)

N Sghir, A Adadi, M Lahmer - Education and information technologies, 2023 - Springer
The last few years have witnessed an upsurge in the number of studies using Machine and
Deep learning models to predict vital academic outcomes based on different kinds and …

Improving Learning Outcomes through Predictive Analytics: Enhancing Teaching and Learning with Educational Data Mining

A Alam - 2023 7th International Conference on Intelligent …, 2023 - ieeexplore.ieee.org
Educational Data Mining (EDM) is a promising area of research that leverages
computational methods to improve educational outcomes by extracting valuable insights …

Predicting academic success in higher education: literature review and best practices

E Alyahyan, D Düştegör - … Journal of Educational Technology in Higher …, 2020 - Springer
Student success plays a vital role in educational institutions, as it is often used as a metric for
the institution's performance. Early detection of students at risk, along with preventive …

AI-based personalized e-learning systems: Issues, challenges, and solutions

M Murtaza, Y Ahmed, JA Shamsi, F Sherwani… - IEEE …, 2022 - ieeexplore.ieee.org
A personalized e-learning system is effective in imparting enhanced learning to its users. As
compared to a conventional e-learning system, which provides similar contents to each …

Student performance analysis and prediction in classroom learning: A review of educational data mining studies

A Khan, SK Ghosh - Education and information technologies, 2021 - Springer
Student performance modelling is one of the challenging and popular research topics in
educational data mining (EDM). Multiple factors influence the performance in non-linear …

Predicting academic performance: a systematic literature review

A Hellas, P Ihantola, A Petersen, VV Ajanovski… - … companion of the 23rd …, 2018 - dl.acm.org
The ability to predict student performance in a course or program creates opportunities to
improve educational outcomes. With effective performance prediction approaches …

Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil

E Fernandes, M Holanda, M Victorino, V Borges… - Journal of business …, 2019 - Elsevier
In this article, we present a predictive analysis of the academic performance of students in
public schools of the Federal District of Brazil during the school terms of 2015 and 2016 …

Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature

AE Ezugwu, AK Shukla, MB Agbaje… - Neural Computing and …, 2021 - Springer
Cluster analysis is an essential tool in data mining. Several clustering algorithms have been
proposed and implemented, most of which are able to find good quality clustering results …

Interpretable dropout prediction: towards XAI-based personalized intervention

M Nagy, R Molontay - International Journal of Artificial Intelligence in …, 2024 - Springer
Student drop-out is one of the most burning issues in STEM higher education, which induces
considerable social and economic costs. Using machine learning tools for the early …