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…

This study explores how students' initial two years at college influence degree GPA and duration of completion. Results highlight the importance of the first two-year cumulative GPA and credits in forecasting degree outcomes. Early college credits and high school GPA impact success, guiding effective course planning. Implications encompass…

As an introduction to quadrupolar effects in NMR spectroscopy, students use low field ([superscript 1]H, 60 MHz), benchtop [superscript 13]C NMR spectroscopy to contrast the spin-spin coupling behavior of [superscript 13]C to the dipolar [superscript 1]H and quadrupolar [superscript 2]D in familiar solvents C(H/D)Cl[subscript 3], C(H/D)[subscript…

This study examined the perceptions of students related to determinants of service quality experienced within both academic and non-academic areas of their respective institutions. As competition increases among higher education institutions so does the need to implement organizational strategies comparable to that of industries outside academia.…

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…