As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…

The recommendation is an active area of scientific research; it is also a challenging and fundamental problem in online education. However, classical recommender systems usually suffer from item cold-start issues. Besides, unlike other fields like e-commerce or entertainment, e-learning recommendations must ensure that learners have the adequate…

This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores…

Complex Machine Learning (ML) models used to support decision-making in peer-to-peer (P2P) lending often lack clear, accurate, and interpretable explanations. While the game-theoretic concept of Shapley values and its computationally efficient variant Kernel SHAP may be employed for this aim, similarly to other existing methods, the latter makes…

A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of…

Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an…

Knowledge tracing is a well-known problem in AI for education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their…

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7 yrs; 16 girls) performed six activities whilst…

The multimedia simulation teaching mode introduces students into virtual scenes for learning. Whether it is enhancing students' interest in learning or enhancing their physical fitness, it is a new teaching mode. This article discusses the establishment of a BP neural network model to study the prediction of students' physical fitness and conducts…

Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…

Designing and constructing pedagogical tests that contain items (i.e. questions) which measure various types of skills for different levels of students equitably is a challenging task. Teachers and item writers alike need to ensure that the quality of assessment materials is consistent, if student evaluations are to be objective and effective.…

Colleges are increasingly turning to predictive analytics to identify "at-risk" students in order to target additional supports. While recent research demonstrates that the types of prediction models in use are reasonably accurate at identifying students who will eventually succeed or not, there are several other considerations for the…

Whether machine learning algorithms effectively predict college students' course outcomes using learning management system data is unknown. Identifying students who will have a poor outcome can help institutions plan future budgets and allocate resources to create interventions for underachieving students. Therefore, knowing the effectiveness of…

Given the rapid adoption of machine learning methods by education researchers, and the growing acknowledgment of their inherent risks, there is an urgent need for tailored methodological guidance on how to improve and evaluate the validity of inferences drawn from these methods. Drawing on an integrative literature review and extending a…