[HTML][HTML] Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models
Higher education institutions often struggle with increased dropout rates, academic
underachievement, and delayed graduations. One way in which these challenges can …
underachievement, and delayed graduations. One way in which these challenges can …
[HTML][HTML] On developing generic models for predicting student outcomes in educational data mining
G Ramaswami, T Susnjak, A Mathrani - Big Data and Cognitive …, 2022 - mdpi.com
Poor academic performance of students is a concern in the educational sector, especially if it
leads to students being unable to meet minimum course requirements. However, with timely …
leads to students being unable to meet minimum course requirements. However, with timely …
[HTML][HTML] A systematic literature review of student'performance prediction using machine learning techniques
Educational Data Mining plays a critical role in advancing the learning environment by
contributing state-of-the-art methods, techniques, and applications. The recent development …
contributing state-of-the-art methods, techniques, and applications. The recent development …
[HTML][HTML] Customized rule-based model to identify at-risk students and propose rational remedial actions
Detecting at-risk students provides advanced benefits for improving student retention rates,
effective enrollment management, alumni engagement, targeted marketing improvement …
effective enrollment management, alumni engagement, targeted marketing improvement …
Student performance prediction in higher education: A comprehensive review
E Tjandra, SS Kusumawardani… - AIP Conference …, 2022 - pubs.aip.org
Student dropout still becomes a critical problem in education. Educational Data Mining
(EDM) can bring potential impact to support academic institution's goals in making academic …
(EDM) can bring potential impact to support academic institution's goals in making academic …
Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science
Predicting academic performance for students majoring in computer science has long been
a significant field of research in computing education. Previous studies described that …
a significant field of research in computing education. Previous studies described that …
Current stance on predictive analytics in higher education: Opportunities, challenges and future directions
Predictive models on students' academic performance can be built by using historical data
for modelling students' learning behaviour. Such models can be employed in educational …
for modelling students' learning behaviour. Such models can be employed in educational …
Early identification of at-risk students using iterative logistic regression
L Zhang, H Rangwala - … in Education: 19th International Conference, AIED …, 2018 - Springer
Higher education institutions are faced with the challenge of low student retention rates and
high number of dropouts. 41% of college students in United States do not finish their …
high number of dropouts. 41% of college students in United States do not finish their …
Scale up predictive models for early detection of at-risk students: A feasibility study
Purpose This study aims to investigate the feasibility of developing general predictive
models for using the learning management system (LMS) data to predict student …
models for using the learning management system (LMS) data to predict student …
Interpretable models do not compromise accuracy or fairness in predicting college success
C Kung, R Yu - Proceedings of the seventh acm conference on …, 2020 - dl.acm.org
The presence of" big data" in higher education has led to the increasing popularity of
predictive analytics for guiding various stakeholders on appropriate actions to support …
predictive analytics for guiding various stakeholders on appropriate actions to support …