Learning affective state is determinate to online learning. Different affective states are associated with different online learning behaviors. Given the behavioral indicators of different affective states are still to be explored, this study constructed a data-driven online learning affective state detector by analyzing the learning log data of…

On e-learning platforms, most e-learners didn't complete the course successfully. It means that reducing dropout is a critical problem for the sustainability of e-learning. This paper aims to establish a predictive model to describe e-learners' dropout behavior, which can help the commercial e-learning platforms to make appropriate interventions…

Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of…

Prediction of student performance is one of the most important subjects of educational data mining. Artificial neural networks are seen to be an effective tool in predicting student performance in e-learning environments. In the studies carried out with artificial neural networks, performance predictions based on student scores are generally made,…

Knowledgebase and information management systems have existed for many years within academics and professional organizations. These technologies have greater importance within academic's programs due to the need and responsibility of creating and transferring knowledge and reliable information to their communities. In today's global, digital, and…

Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to…

This study aimed to investigate the effect of academic procrastination on e-learning course achievement. Because all of the interactions among students, instructors, and contents in an e-learning environment were automatically recorded in a learning management system (LMS), procrastination such as the delays in weekly scheduled learning and late…

Individualization of learning through ICT [Information and Communication Technology] allows to students not only the possibility choose the time and place to study, but especially pace adoption of new knowledge on the basis of preferred learning styles. Analysis of learning processes should give the answer to difficult questions from pedagogical…

This paper examines factors that predict students' continual usage intention of web-based learning content management systems in Tanzania, with a specific focus at Muhimbili University of Health and Allied Science (MUHAS). This study sent a questionnaire surveys to 408 first year undergraduate students, with a rate of return of 66.7. This study…

The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…