Enhancing prediction of student success: Automated machine learning approach
Authors:
- Hassan ZeineddineHZHassan Zeineddine
- Udo BraendleUBUdo Braendle
- Assaad FarahAFAssaad Farah
Top Cited Papers
- 1 January 2021
- journal article
- research article
- Published by Elsevier in Computers and Electrical Engineering
- Vol. 89
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- Early segmentation of students according to their academic performance: A predictive modelling approachDecision Support Systems, 2018
- Early detection of university students with potential difficultiesDecision Support Systems, 2017
- Predicting process behaviour using deep learningDecision Support Systems, 2017
- A data mining based system for credit-card fraud detection in e-tailDecision Support Systems, 2017
- Models for early prediction of at-risk students in a course using standards-based gradingComputers & Education, 2016
- Early dropout prediction using data mining: a case study with high school studentsExpert Systems, 2015
- Educational data mining: A survey and a data mining-based analysis of recent worksExpert Systems with Applications, 2014
- Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical modelsComputers & Education, 2013
- A comparative analysis of machine learning techniques for student retention managementDecision Support Systems, 2010
- Predicting Academic Performance by Data Mining MethodsEducation Economics, 2007