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…

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…

The latest technologies in natural language processing provide creative, knowledge retrieval, and question-answering technologies in the design of intelligent education, which can provide learners with personalized feedback and expert guidance. Entrepreneurship education aims to cultivate and develop the innovative thinking and entrepreneurial…

Knowledge tracing (KT) for evaluating students' knowledge is an essential task in personalized education. More and more researchers have devoted themselves to solving KT tasks, e.g., deep knowledge tracing (DKT), which can capture more sophisticated representations of student knowledge. Nonetheless, these techniques ignore the reconstruction of…

Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…

In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate…

Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying…

With the popularity of online one-to-one tutoring, there are emerging concerns about the quality and effectiveness of this kind of tutoring. Although there are some evaluation methods available, they are heavily relied on manual coding by experts, which is too costly. Therefore, using machine learning to predict instruction quality automatically…

This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and…

Properly predicting students' academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to…

Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…

Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…

To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…

Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…

Reading comprehension is a widely adopted method for learning English, involving reading articles and answering related questions. However, the reading comprehension training typically focuses on the skill level required for a standardized learning stage, without considering the impact of individual differences in linguistic competence. This…