Predicting academic performance of students from VLE big data using deep learning models

H Waheed, SU Hassan, NR Aljohani, J Hardman… - Computers in Human …, 2020 - Elsevier
The abundance of accessible educational data, supported by the technology-enhanced
learning platforms, provides opportunities to mine learning behavior of students, addressing …

Educational data mining to predict students' academic performance: A survey study

S Batool, J Rashid, MW Nisar, J Kim, HY Kwon… - Education and …, 2023 - Springer
Educational data mining is an emerging interdisciplinary research area involving both
education and informatics. It has become an imperative research area due to many …

The datafication of education

J Jarke, A Breiter - Learning, Media and Technology, 2019 - Taylor & Francis
The increasing datafication, in particular the availability of data and corresponding
algorithms introduces new means to measure, capture, describe and represent social life in …

Prediction of students' early dropout based on their interaction logs in online learning environment

AA Mubarak, H Cao, W Zhang - Interactive Learning Environments, 2022 - Taylor & Francis
Online learning has become more popular in higher education since it adds convenience
and flexibility to students' schedule. But, it has faced difficulties in the retention of the …

Prediction of student academic performance using a hybrid 2D CNN model

S Poudyal, MJ Mohammadi-Aragh, JE Ball - Electronics, 2022 - mdpi.com
Opportunities to apply data mining techniques to analyze educational data and improve
learning are increasing. A multitude of data are being produced by institutional technology, e …

Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models

M Adnan, AAS Alarood, MI Uddin… - PeerJ Computer Science, 2022 - peerj.com
Abstract Corona Virus Disease 2019 (COVID-19) pandemic has increased the importance of
Virtual Learning Environments (VLEs) instigating students to study from their homes. Every …

Predicting at-risk university students based on their e-book reading behaviours by using machine learning classifiers

CH Chen, SJH Yang, JX Weng, H Ogata… - Australasian Journal of …, 2021 - ajet.org.au
Providing early predictions of academic performance is necessary for identifying at-risk
students and subsequently providing them with timely intervention for critical factors affecting …

Open learning analytics: a systematic review of benchmark studies using open university learning analytics dataset (OULAD)

HA Alhakbani, FM Alnassar - Proceedings of the 2022 7th International …, 2022 - dl.acm.org
Virtual learning has gained increased importance because of the recent pandemic situation.
A mass shift to virtual means of education delivery has been observed over the past couple …

The potential for student performance prediction in small cohorts with minimal available attributes

E Wakelam, A Jefferies, N Davey… - British Journal of …, 2020 - Wiley Online Library
The measurement of student performance during their progress through university study
provides academic leadership with critical information on each student's likelihood of …

Predictive model using a machine learning approach for enhancing the retention rate of students at-risk

HS Brdesee, W Alsaggaf, N Aljohani… - International Journal on …, 2022 - igi-global.com
Student retention is a widely recognized challenge in the educational community to assist
the institutes in the formation of appropriate and effective pedagogical interventions. This …