Digital technology is profoundly transforming various aspects of life, thus highlighting the need to enhance digital literacy on a national scale. In primary and secondary schools, artificial intelligence (AI) education plays a pivotal role in fostering digital literacy. To comprehensively investigate the variables influencing AI education in…

The prediction of student performance and the analysis of students' learning behaviour play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behaviour, educators can gain valuable insights into the factors that influence students' academic outcomes and identify areas of…

School dropout is a significant issue in distance learning, and early detection is crucial for addressing the problem. Our study aims to create a binary classification model that anticipates students' activity levels based on their current achievements and engagement on a Canadian Distance learning Platform. Predicting student dropout, a common…

The objectives of this research were 1) to study the problems of stress and depression among Grade 12 students; 2) to investigate the machine learning technique in analyzing and predicting stress, depression, and academic performance among Grade 12 students; and 3) to evaluate the stress and depression prediction platform. Students from schools in…

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different…

Linguistic phenomena (e.g., words and syntactic structure) co-occur with a wide variety of meanings. These systematic correlations can help readers to interpret a text and create predictions about upcoming material. However, to what extent these correlations influence discourse processing is still unknown. We address this question by examining…

Past studies have explored student self-perception within chemistry courses. Various factors have been explored including course level, student academic background, and gender. However, it appears that there are few (if any) studies that have looked at whether students are aware of how they have performed previously in the course. Through a study…

In recent years, e-learning engagement has attracted much attention because the COVID-19 pandemic has forced schools to shift to online teaching without preparation. Therefore, based on the ecological system theory, this study investigates the relationship between subjective socioeconomic status and e-learning engagement among college students and…

This study aimed to understand the relationship between course activities and learning progress among students enrolled in the MicroMasters certificate program offered in an affordable MOOC-based learning platform. In order to capture the relationship, the differences between the engagement patterns of learners in the MicroMasters program compared…

As instruction shifts away from traditional approaches, online learning has grown in popularity in K-12 and higher education. Artificial intelligence (AI) and learning analytics methods such as machine learning have been used by educational scholars to support online learners on a large scale. However, the fairness of AI prediction in educational…

This study investigated whether transcranial direct current stimulation (tDCS) targeting the inferior frontal gyrus (IFG) can alter the thinking process and neural basis of creativity. Participants' performance on the compound remote associates (CRA) task was analyzed considering the semantic features of each trial after receiving different tDCS…

Random encouragement designs are randomized controlled trials (RCTs) that test interventions aimed at increasing participation in a program or activity whose take up is not universal. In these RCTs, instead of randomizing individuals or clusters directly into treatment and control groups to participate in a program or activity, the randomization…

Empirical research on number sense to date has been conducted with preschoolers and elementary school students. Despite their contributions to the literature, these studies have used variable-centered analytic approaches that may prevent distinct number sense profiles for preschoolers and first graders from being identified. This study aimed to…

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…

Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed…