MOOC dropout prediction using machine learning techniques: Review and research challenges

F Dalipi, AS Imran, Z Kastrati - 2018 IEEE global engineering …, 2018 - ieeexplore.ieee.org
MOOC represents an ultimate way to deliver educational content in higher education
settings by providing high-quality educational material to the students throughout the world …

Learning analytics and digital badges: Potential impact on student retention in higher education

DK Mah - Technology, Knowledge and Learning, 2016 - Springer
Learning analytics and digital badges are emerging research fields in educational science.
They both show promise for enhancing student retention in higher education, where …

Student engagement predictions in an e‐learning System and their impact on student course assessment scores

M Hussain, W Zhu, W Zhang… - Computational …, 2018 - Wiley Online Library
Several challenges are associated with e‐learning systems, the most significant of which is
the lack of student motivation in various course activities and for various course materials. In …

The role of demographics in online learning; A decision tree based approach

S Rizvi, B Rienties, SA Khoja - Computers & Education, 2019 - Elsevier
Research has shown online learners' performance to have a strong association with their
demographic characteristics, such as regional belonging, socio-economic standing …

A large-scale implementation of predictive learning analytics in higher education: The teachers' role and perspective

C Herodotou, B Rienties, A Boroowa, Z Zdrahal… - … technology research and …, 2019 - Springer
By collecting longitudinal learner and learning data from a range of resources, predictive
learning analytics (PLA) are used to identify learners who may not complete a course …

Process mining for self-regulated learning assessment in e-learning

R Cerezo, A Bogarín, M Esteban, C Romero - Journal of Computing in …, 2020 - Springer
Content assessment has broadly improved in e-learning scenarios in recent decades.
However, the e-Learning process can give rise to a spatial and temporal gap that poses …

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

SB Dias, SJ Hadjileontiadou, J Diniz… - Scientific reports, 2020 - nature.com
Abstract Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-
face teaching to universities and schools, forcing a crash course for online learning plans …

How learning analytics can early predict under-achieving students in a blended medical education course

M Saqr, U Fors, M Tedre - Medical teacher, 2017 - Taylor & Francis
Aim: Learning analytics (LA) is an emerging discipline that aims at analyzing students'
online data in order to improve the learning process and optimize learning environments. It …

The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study

C Herodotou, B Rienties, M Hlosta, A Boroowa… - The Internet and Higher …, 2020 - Elsevier
A vast number of studies reported exciting innovations and practices in the field of Learning
Analytics (LA). Whilst they provided substantial insights, most of these studies have been …

[HTML][HTML] Virtual learning environment engagement and learning outcomes at a 'bricks-and-mortar'university

CA Boulton, C Kent, HTP Williams - Computers & Education, 2018 - Elsevier
In this study, we analyse the relationship between engagement in a virtual learning
environment (VLE) and module grades at a 'bricks-and-mortar'university in the United …