The rapid expansion of e-learning has resulted in a surge in educational data volume, presenting challenges in manually uncovering valuable information. Concurrently, advancements in educational data mining offer robust technical support for forecasting student performance based on their engagement behaviors. In this study, we initially…

Student performance is affected by their knowledge which changes dynamically over time. Therefore, employing recurrent neural networks (RNN), which are known to be very good in dynamic time series prediction, can be a suitable approach for student performance prediction. We propose such a neural network architecture containing two modules: (i) a…

Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners' future performance. This is…

This study aimed to investigate the trajectories of students' self-regulated learning in an online course and the predictive role of students' satisfaction with basic psychological needs (autonomy, competence, relatedness) on these trajectories. Additionally, the potential variation in the relationship between basic psychological needs and…

In the context that many students have been fed up with rote learning or mechanical learning in history, this three-wave longitudinal study with one-year intervals examined the potential role of historical empathy in facilitating history learning. A sample of 821 middle school students participated (aged from 11 to 15 years, M = 13.47 years, SD =…

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…

Various studies have been done on shifting toward technology-based second language (L2) education. However, the influence of psycho-emotional factors on students' technology acceptance is overlooked. To fill this gap, the present quantitative study examined the role of students' achievement emotions and technological self-efficacy in predicting…

Researchers and practitioners from different disciplines (e.g., educational science, computer science, statistics) continuously enter the rapidly developing research field of learning analytics (LA) and bring along different perspectives and experiences in research design and methodology. Scientific communities share common problems, concepts,…

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…

A course-level early final study status prediction task is to predict as soon as possible the final success of each student after studying a course. It is significant because each successful course accomplishment is required for a degree. Further, early predictions provide enough time to make necessary changes for ultimate success. This article…

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…

The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aims to predict learners' performance in online learning settings. This systemic model integrates cognitive, social,…

This study aims to examine the construct validity of the English Language Resilience Scale (ELRS), explore how English learning resilience can be predicted through the six factors of resilience (i.e. planning, self-control, persistence and continuity in English learning, growth mindset, flexibility, and sociability), investigate how English…

How does the act of explaining influence learning? Prior work has studied effects of explaining through a predominantly proximal lens, measuring short-term outcomes or manipulations within lab settings. Here, we ask whether the benefits of explaining extend to academic performance over time. Specifically, does the quality and frequency of student…

Maintaining learning engagement throughout adolescence is critical for long-term academic success. This research sought to understand the role of metacognition and motivation in predicting adolescents' engagement in math learning over time. In two longitudinal studies with 2,325 and 207 adolescents (ages 11-15), metacognitive skills, interest, and…