Successful prediction of student performance has significant impact to many stakeholders,
including students, teachers and educational institutes. In this domain, it is equally important …

Educational big data significantly impacts education, and Massive Open Online Courses
(MOOCs), a crucial learning approach, have evolved to be more intelligent with these …

Predicting student academic performance is a critical area of education research. Machine
learning (ML) algorithms have gained significant popularity in recent years. The capability to …

Discovering valuable learning path patterns from learner online learning data can provide
follow‐up learners with effective learning path reference and improve their learning …

Purpose Critical thinking is considered important in psychological science because it
enables students to make effective decisions and optimizes their performance. Aiming at the …

Purpose For studies in educational data mining or learning Analytics, the prediction of
student's performance or early warning is one of the most popular research topics. However …

Logistic regression models have traditionally been used to identify the factors contributing to
students' conceptual understanding. With the advancement of the machine learning-based …

While the use of deep neural networks is popular for predicting students' learning outcomes,
convolutional neural network (CNN)-based methods are used more often. Such methods …

Learning data analytics improves the learning field in higher education using educational
data for extracting useful patterns and making better decision. Identifying potential at-risk …

Abstract Machine learning and data mining techniques hold promise in predicting at-risk
students in online learning. This meta-analysis aimed to provide quantitative evidence to …