[HTML][HTML] The Power of Deep Learning Techniques for Predicting Student Performance in Virtual Learning Environments: A Systematic Literature Review
With the advances in Artificial Intelligence (AI) and the increasing volume of online
educational data, Deep Learning techniques have played a critical role in predicting student …
educational data, Deep Learning techniques have played a critical role in predicting student …
Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach
Z Shou, M Xie, J Mo, H Zhang - Applied Sciences, 2024 - mdpi.com
As an emerging teaching method, online learning is becoming increasingly popular among
learners. However, one of the major drawbacks of this learning style is the lack of effective …
learners. However, one of the major drawbacks of this learning style is the lack of effective …
[PDF][PDF] Exploring the Impact of Students Demographic Attributes on Performance Prediction through Binary Classification in the KDP Model.
Learner assessment is central to determining students' progress in every educational
establishment. Evaluating students' performance, however, has become a daunting task as …
establishment. Evaluating students' performance, however, has become a daunting task as …
On conceptualisation and an overview of learning path recommender systems in e-learning
A Fuster-López, JM Cruz, P Guerrero-García… - arXiv preprint arXiv …, 2024 - arxiv.org
The use of e-learning systems has a long tradition, where students can study online helped
by a system. In this context, the use of recommender systems is relatively new. In our …
by a system. In this context, the use of recommender systems is relatively new. In our …
Modeling Learners to Early Predict Their Performance in Educational Computer Games
Data mining approaches have proven to be successful in improving learners' interaction with
educational computer games. Despite the potential of predictive modelling in providing …
educational computer games. Despite the potential of predictive modelling in providing …
Using big data to assess an affective domain for distance education
Distance education is popular worldwide, especially after the COVID-19 pandemic.
Assessing the affective domain of distance education becomes challenging because the …
Assessing the affective domain of distance education becomes challenging because the …
[HTML][HTML] Predicting student dropouts with machine learning: An empirical study in Finnish higher education
M Vaarma, H Li - Technology in Society, 2024 - Elsevier
This study uses three machine learning models to predict student dropouts based on
students' transcript, demographic, and learning management system (LMS) data from a …
students' transcript, demographic, and learning management system (LMS) data from a …
Utilizing decision tree machine learning model to map dental students' preferred learning styles with suitable instructional strategies
Background Growing demand for student-centered learning (SCL) has been observed in
higher education settings including dentistry. However, application of SCL in dental …
higher education settings including dentistry. However, application of SCL in dental …
Attention-Based Artificial Neural Network for Student Performance Prediction Based on Learning Activities
Student performance prediction was deployed to predict learning performance to identify at-
risk students and provide interventions for them. However, prediction models should also …
risk students and provide interventions for them. However, prediction models should also …
ClickTree: A Tree-based Method for Predicting Math Students' Performance Based on Clickstream Data
The prediction of student performance and the analysis of students' learning behavior play
an important role in enhancing online courses. By analysing a massive amount of …
an important role in enhancing online courses. By analysing a massive amount of …