How does learning analytics contribute to prevent students' dropout in higher education: a systematic literature review
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 …
On the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010–2018
The aim of this paper is to survey recent research publications that use Soft Computing
methods to answer education-related problems based on the analysis of educational data …
methods to answer education-related problems based on the analysis of educational data …
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 …
Survey of state-of-the-art mixed data clustering algorithms
Mixed data comprises both numeric and categorical features, and mixed datasets occur
frequently in many domains, such as health, finance, and marketing. Clustering is often …
frequently in many domains, such as health, finance, and marketing. Clustering is often …
Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in Chile
Data mining is employed to extract useful information and to detect patterns from often large
data sets, closely related to knowledge discovery in databases and data science. In this …
data sets, closely related to knowledge discovery in databases and data science. In this …
A comparative study on student performance prediction using machine learning
Y Chen, L Zhai - Education and Information Technologies, 2023 - Springer
Accompanied with the development of storage and processing capacity of modern
technology, educational data increases sharply. It is difficult for educational researchers to …
technology, educational data increases sharply. It is difficult for educational researchers to …
[HTML][HTML] Uplift Modeling for preventing student dropout in higher education
Uplift modeling is an approach for estimating the incremental effect of an action or treatment
at the individual level. It has gained attention in the marketing and analytics communities …
at the individual level. It has gained attention in the marketing and analytics communities …
Educational big data: Predictions, applications and challenges
Educational big data is becoming a strategic educational asset, exceptionally significant in
advancing educational reform. The term educational big data stems from the rapidly growing …
advancing educational reform. The term educational big data stems from the rapidly growing …
Trajectory of university dropout: Investigating the cumulative effect of academic vulnerability and proximity to family support
EM Sosu, P Pheunpha - Frontiers in Education, 2019 - frontiersin.org
University dropout is a major policy concern around the world because of its consequences
for the individual, institutions and society. In this study, we offer new evidence by examining …
for the individual, institutions and society. In this study, we offer new evidence by examining …
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 …