Student retention using educational data mining and predictive analytics: a systematic literature review
Student retention is an essential measurement metric in education, indicated by retention
rates, which are accumulated as students re-enroll from one academic year to the next. High …
rates, which are accumulated as students re-enroll from one academic year to the next. High …
[HTML][HTML] A systematic review of the literature on machine learning application of determining the attributes influencing academic performance
Academic institutions operate in an extremely demanding and competitive environment.
Some difficulties confronting most schools are delivering high-quality education to the …
Some difficulties confronting most schools are delivering high-quality education to the …
Educational data mining to predict students' academic performance: A survey study
Educational data mining is an emerging interdisciplinary research area involving both
education and informatics. It has become an imperative research area due to many …
education and informatics. It has become an imperative research area due to many …
How does learning analytics contribute to prevent students' dropout in higher education: a systematic literature review
CF de Oliveira, SR Sobral, MJ Ferreira… - Big Data and Cognitive …, 2021 - mdpi.com
Retention and dropout of higher education students is a subject that must be analysed
carefully. Learning analytics can be used to help prevent failure cases. The purpose of this …
carefully. Learning analytics can be used to help prevent failure cases. The purpose of this …
Predicting student performance and its influential factors using hybrid regression and multi-label classification
A Alshanqiti, A Namoun - Ieee Access, 2020 - ieeexplore.ieee.org
Understanding, modeling, and predicting student performance in higher education poses
significant challenges concerning the design of accurate and robust diagnostic models …
significant challenges concerning the design of accurate and robust diagnostic models …
Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature
Recently, machine learning (ML) has evolved and finds its application in higher education
(HE) for various data analysis. Studies have shown that such an emerging field in …
(HE) for various data analysis. Studies have shown that such an emerging field in …
Predicting academic performance using an efficient model based on fusion of classifiers
In the past few years, educational data mining (EDM) has attracted the attention of
researchers to enhance the quality of education. Predicting student academic performance …
researchers to enhance the quality of education. Predicting student academic performance …
Systematic literature review on machine learning and student performance prediction: Critical gaps and possible remedies
Improving the quality, developing and implementing systems that can provide advantages to
students, and predicting students' success during the term, at the end of the term, or in the …
students, and predicting students' success during the term, at the end of the term, or in the …
Practical early prediction of students' performance using machine learning and eXplainable AI
Predicting students' performance in advance could help assist the learning process; if “at-
risk” students can be identified early on, educators can provide them with the necessary …
risk” students can be identified early on, educators can provide them with the necessary …
Data mining and machine learning retention models in higher education
This study presents a systematic review of the literature on the predicting student retention in
higher education through machine learning algorithms based on measures such as dropout …
higher education through machine learning algorithms based on measures such as dropout …