High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…

Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…

Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…

Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…

In the educational landscape, educational data mining has emerged as an indispensable tool for institutions seeking to deliver exceptional and high-quality education. However, education data revealed suboptimal academic performance among a significant portion of the student population, which consequently resulted in delayed graduation. This…

Predicting student performance in international large-scale assessments (ILSAs) is crucial for understanding educational outcomes on a global scale. ILSAs, such as the Program for International Student Assessment and the Trends in International Mathematics and Science Study, serve as vital tools for policymakers, educators, and researchers to…

This study empirically examines the predictive relationship between Institutional Autonomy (IA) and Academic Freedom (AF) whilst controlling for the mediating effect of corporate governance (CG) amongst selected higher education institutions in Ghana. It also looks at the difference between females and males and their perceptions of the predictive…

The fundamental goal of education is to teach students to become self-regulated learners who actively and efficiently manage their learning processes by deploying self-regulated learning strategies. Based on this stance, the present study investigated whether these strategies predicted the writing performance of English major students. Eleven…

Childhood adversity takes a toll on lifelong health. However, investigations of unpredictability as a form of adversity are lacking. Environmental unpredictability across multiple developmental periods and ecological levels was examined using a multiethnic, longitudinal birth cohort (1998-2000) oversampled for unmarried parents. Data were from the…

Non-cognitive factors, such as motivation, have shown to play a significant role in adolescences science learning. However, there is little longitudinal research investigating students' intrinsic motivation in science learning over school years. Based on self-determination theory, this study examines the change in, and associations between,…

Several recommender systems have been proposed to suggest courses to students based on their transcripts. In this paper, we evaluate whether these systems can be generalized to other academic activities, such as research projects and extracurricular activities. We follow previous grade-aware and content-based approaches, where course descriptions…

Prior work has developed a range of automated measures ("detectors") of student self-regulation and engagement from student log data. These measures have been successfully used to make discoveries about student learning. Here, we extend this line of research to an underexplored aspect of self-regulation: students' decisions about when to…

In this study, we examined how variables such as the age of faculty members, autism awareness, and readiness to work with individuals with autism predict their attitudes toward individuals with Autism Spectrum Disorder. This study included 240 faculty members from various faculties. Data were analyzed using stepwise multiple regression analysis.…

This study concludes that traditional logistic regression models, particularly those using ACT Composite scores, tend to demonstrate better fairness metrics across subgroups compared to a fairness-aware machine learning gradient-boosted machine model. The exclusion of race/ethnicity from predictive models does not introduce notable bias and may…

This study examined the effectiveness of online English learning among college students by integrating data from multiple learning behavior sources. The research revealed a bidirectional regulatory relationship between device environment and cognitive strategies, clarified the critical threshold for the regulation of metacognitive strategies, and…