Predicting academic performance: a systematic literature review

A Hellas, P Ihantola, A Petersen, VV Ajanovski… - … companion of the 23rd …, 2018 - dl.acm.org
The ability to predict student performance in a course or program creates opportunities to
improve educational outcomes. With effective performance prediction approaches …

Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

F Ouyang, M Wu, L Zheng, L Zhang, P Jiao - International Journal of …, 2023 - Springer
As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced
computing technologies, AI performance prediction model is widely used to identify at-risk …

Artificial intelligence-enabled prediction model of student academic performance in online engineering education

P Jiao, F Ouyang, Q Zhang, AH Alavi - Artificial Intelligence Review, 2022 - Springer
Online education has been facing difficulty in predicting the academic performance of
students due to the lack of usage of learning process, summative data and a precise …

Predicting student performance using advanced learning analytics

A Daud, NR Aljohani, RA Abbasi, MD Lytras… - Proceedings of the 26th …, 2017 - dl.acm.org
Educational Data Mining (EDM) and Learning Analytics (LA) research have emerged as
interesting areas of research, which are unfolding useful knowledge from educational …

Insights into LSTM fully convolutional networks for time series classification

F Karim, S Majumdar, H Darabi - Ieee Access, 2019 - ieeexplore.ieee.org
Long short-term memory fully convolutional neural networks (LSTM-FCNs) and Attention
LSTM-FCN (ALSTM-FCN) have shown to achieve the state-of-the-art performance on the …

Impacts on students' academic performance due to emergency transition to remote teaching during the COVID-19 pandemic: A financial engineering course case …

R Nazempour, H Darabi, PC Nelson - Education Sciences, 2022 - mdpi.com
The COVID-19 pandemic has enforced higher education institutions to adopt emergency
remote teaching (ERT) as the substitution for traditional face-to-face (F2F) classes. A lot of …

Learning Analytics to identify dropout factors of Computer Science studies through Bayesian networks

C Lacave, AI Molina, JA Cruz-Lemus - Behaviour & Information …, 2018 - Taylor & Francis
ABSTRACT Student dropout in Engineering Education is an important problem which has
been studied from different perspectives, as well as using different techniques. This …

Education analytics: Challenges and approaches

L Zhang, KF Li - 2018 32nd international conference on …, 2018 - ieeexplore.ieee.org
We present a survey on the current status of education analytics. Over 80 state-of-the-art
research projects and reports, published from 2013 to 2017 and archived in IEEE Xplore …

Student success prediction using student exam behaviour

J Kuzilek, Z Zdrahal, V Fuglik - Future Generation Computer Systems, 2021 - Elsevier
Abstract The Faculty of Mechanical Engineering, Czech Technical University in Prague
(FME) faces a significant student drop-out in the first-year bachelor programme, which is an …

Automatic assessment of students' engineering design performance using a Bayesian network model

W Xing, C Li, G Chen, X Huang… - Journal of …, 2021 - journals.sagepub.com
Integrating engineering design into K-12 curricula is increasingly important as engineering
has been incorporated into many STEM education standards. However, the ill-structured …