A systematic meta-review and analysis of learning analytics research

X Du, J Yang, BE Shelton, JL Hung… - Behaviour & information …, 2021‏ - Taylor & Francis
As an emerging field of research, learning analytics (LA) offers practitioners and researchers
information about educational data that is helpful for supporting decisions in management of …

Educational data mining: a systematic review of research and emerging trends

X Du, J Yang, JL Hung, B Shelton - Information Discovery and …, 2020‏ - emerald.com
Purpose Educational data mining (EDM) and learning analytics, which are highly related
subjects but have different definitions and focuses, have enabled instructors to obtain a …

Predicting at-risk students at different percentages of course length for early intervention using machine learning models

M Adnan, A Habib, J Ashraf, S Mussadiq… - Ieee …, 2021‏ - ieeexplore.ieee.org
Online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning
Environments (VLEs), and Learning Management Systems (LMS) facilitate thousands or …

[HTML][HTML] Enhancing active learning through collaboration between human teachers and generative AI

K Pahi, S Hawlader, E Hicks, A Zaman… - Computers and Education …, 2024‏ - Elsevier
To address the increasing demand for AI literacy, we introduced a novel active learning
approach that leverages both teaching assistants (TAs) and generative AI to provide …

An early feedback prediction system for learners at-risk within a first-year higher education course

D Baneres, ME Rodríguez-Gonzalez… - IEEE Transactions on …, 2019‏ - ieeexplore.ieee.org
Identifying at-risk students as soon as possible is a challenge in educational institutions.
Decreasing the time lag between identification and real at-risk state may significantly reduce …

[HTML][HTML] An early warning system to detect at-risk students in online higher education

D Bañeres, ME Rodríguez, AE Guerrero-Roldán… - Applied Sciences, 2020‏ - mdpi.com
Artificial intelligence has impacted education in recent years. Datafication of education has
allowed develo** automated methods to detect patterns in extensive collections of …

[HTML][HTML] Learning behaviours data in programming education: Community analysis and outcome prediction with cleaned data

TT Mai, M Bezbradica, M Crane - Future Generation Computer Systems, 2022‏ - Elsevier
Due to the COVID19 pandemic, more higher-level education programmes have moved to
online channels, raising issues in monitoring students' learning progress. Thanks to …

The role of machine learning in identifying students at-risk and minimizing failure

RZ Pek, ST Özyer, T Elhage, T Özyer, R Alhajj - IEEE Access, 2022‏ - ieeexplore.ieee.org
Education is very important for students' future success. The performance of students can be
supported by the extra assignments and projects given by the instructors for students with …

MOOC student dropout prediction model based on learning behavior features and parameter optimization

C ** - Interactive Learning Environments, 2023‏ - Taylor & Francis
Since the advent of massive open online courses (MOOC), it has been the focus of
educators and learners around the world, however the high dropout rate of MOOC has had a …

Predicting student dropout in self-paced MOOC course using random forest model

S Dass, K Gary, J Cunningham - Information, 2021‏ - mdpi.com
A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student
dropout in these courses. An effective student dropout prediction model of MOOC courses …