Mining big data in education: Affordances and challenges

C Fischer, ZA Pardos, RS Baker… - Review of Research …, 2020 - journals.sagepub.com
The emergence of big data in educational contexts has led to new data-driven approaches
to support informed decision making and efforts to improve educational effectiveness. Digital …

Educational big data: Predictions, applications and challenges

X Bai, F Zhang, J Li, T Guo, A Aziz, A Jin, F Xia - Big Data Research, 2021 - Elsevier
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 …

Goal-based course recommendation

W Jiang, ZA Pardos, Q Wei - … of the 9th international conference on …, 2019 - dl.acm.org
With cross-disciplinary academic interests increasing and academic advising resources over
capacity, the importance of exploring data-assisted methods to support student decision …

University dropout prediction through educational data mining techniques: A systematic review

F Agrusti, G Bonavolontà, M Mezzini - Journal of e-learning and knowledge …, 2019 - je-lks.org
The dropout rates in the European countries is one of the major issues to be faced in a near
future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people …

Negative link prediction to reduce dropout in Massive Open Online Courses

F Khoushehgir, S Sulaimany - Education and Information Technologies, 2023 - Springer
In recent years, the rapid growth of Massive Open Online Courses (MOOCs) has attracted
much attention for related research. Besides, one of the main challenges in MOOCs is the …

Early detection of students at risk of poor performance in Rwanda higher education using machine learning techniques

E Masabo, J Nzabanita, I Ngaruye, C Ruranga… - International Journal of …, 2023 - Springer
The prediction of student performance is one of the major issues in many higher education
institutions throughout the world. This issue is lately detected due to the lack of a proper …

Predicting and mitigating freshmen student attrition: A local-explainable machine learning framework

D Delen, B Davazdahemami… - Information Systems …, 2024 - Springer
With the emergence of novel methods for improving machine learning (ML) transparency,
traditional decision-support-focused information systems seem to need an upgrade in their …

A comparative study of machine learning algorithms for virtual learning environment performance prediction

E Ismanto, HA Ghani… - IAES International Journal …, 2023 - myscholar.umk.edu.my
Virtual learning environment is becoming an increasingly popular study option for students
from diverse cultural and socioeconomic backgrounds around the world. Although this …

Identifying at-risk K-12 students in multimodal online environments: a machine learning approach

H Li, W Ding, Z Liu - arXiv preprint arXiv:2003.09670, 2020 - arxiv.org
With the rapid emergence of K-12 online learning platforms, a new era of education has
been opened up. It is crucial to have a dropout warning framework to preemptively identify K …

[PDF][PDF] Predictive models for higher education dropout: A systematic literature review

M Tete, M Sousa, T Santana, S Fellipe - Education Policy Analysis …, 2022 - epaa.asu.edu
School dropout is considered a complex problem and one that cuts across several levels of
analysis. The development of predictive models has been a more dynamic and proactive …