Emotions of learners are fundamental and significant in e-learning as they encourage learning. Machine learning models are presented in the literature to look at how emotions may affect e-learning results that are improved and optimized. Nevertheless, the models that have been suggested so far are appropriate for offline mode, whereby data for…

In e-learning environments, most adaptive systems do not consider the learner's emotional state when recommending activities for learning difficulties, blockages, or demotivation. In this paper, we propose a new approach of emotion-based adaptation in e-learning environments. The system will allow recommendation resources/activities to motivate…

This study based on the Hedonic Motivation System Adoption Model (HMSAM), the Expectation Confirmation Model (ECM), and Task Technology Fit (TTF), explores the factors influencing users' continuance intention in gamified learning within metaversity. A total of 286 valid questionnaires were collected using random sampling. The data were analyzed…

Learning interest affects the way of learning and its process, which is an important factor that affects the learning effect. At present, students' learning interest in a teaching environment is mainly based on a traditional questionnaire or case analysis, which is not conducive for teachers to promptly access students' interest in class to…

The starting reasoning and promoting switch from intuitive system 1 to deliberate system 2 for provoking creative thinking is lacking feasible model, especially during the global pandemic. We established a visible, trainable and learnable (VTL) model with digital technique to promote this dual switch for creative thinking. This study was…

The importance and dynamic development of technological pedagogical content knowledge (TPACK) has been well recognized. In order to keep up with the development of the ever-changing society and variety of teaching technologies, teachers need to continue to learn TPACK. Previous studies indicated the importance of student engagement in promoting…

In tutoring systems, a pedagogical policy, which decides the next action for the tutor to take, is important because it determines how well students will learn. An effective pedagogical policy must adapt its actions according to the student's features, such as knowledge, error patterns, and emotions. For adapting difficulty, it is common to…

Predicting the students' emotional and behavioral engagements using computer vision techniques is a challenging task. Though there are several state-of-the-art techniques for analyzing a student's affective states in an e-learning environment (single person's engagement detection in a single image frame), a very few works are available for…

The purpose of this study is to propose an e-learning system model for learning content personalisation based on students' emotions. The proposed system collects learners' brainwaves using a portable Electroencephalogram and processes them via a supervised machine learning algorithm, named K-nearest neighbours (KNN), to recognise real-time…