Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review-Reference-Cited by-同舟云学术

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Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Published:2022 Issue: Volume:10 Page:72480-72503
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shafiq Dalia Abdulkareem¹^ORCID,Marjani Mohsen¹^ORCID,Habeeb Riyaz Ahamed Ariyaluran¹^ORCID,Asirvatham David¹

Affiliation:

1. School of Computer Science (CS), Taylor’s University, Subang Jaya, Malaysia

Funder

Taylor’s University through the Taylor’s Research Excellence Scholarship 2021

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09815588.pdf?arnumber=9815588

Reference142 articles.

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

2. Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

3. Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

4. Systematic Literature Review of Predictive Analysis Tools in Higher Education

5. Differentiating between educational data mining and learning analytics: A bibliometric approach;dormezil;Proc CEUR Workshop,2020

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1. Idiographic artificial intelligence to explain students' self-regulation: Toward precision education;Learning and Individual Differences;2024-08

2. Modelling Student Retention in Tutorial Classes with Uncertainty—A Bayesian Approach to Predicting Attendance-Based Retention;Education Sciences;2024-07-30

3. Student Clustering Through KMeans to Enhance Teaching;2024 XVI Congreso de Tecnología, Aprendizaje y Enseñanza de la Electrónica (TAEE);2024-06-26

4. A critical review of data mining in education on the levels and aspects of education;Quality Education for All;2024-06-24

5. Long-term student performance prediction using learning ability self-adaptive algorithm;Complex & Intelligent Systems;2024-06-10

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