Massive open online courses (MOOCs) offer rich opportunities to comprehend learners' learning experiences by examining their self-generated course evaluation content. This study investigated the effectiveness of fine-tuned BERT models for the automated classification of topics in online course reviews and explored the variations of these topics…

This study aims to explore the frequently used words in 41 research articles and 12 related books from 2000 to 2024 in the context of social media marketing and e-commerce, focusing on general words, academic words, and specialized words. The investigation also leads to an in-depth examination of collocation words using Antconc software's N-gram…

This paper offers an invitation to higher music education communities to think differently about the significance of the connections between music and play. We highlight the many texts that articulate these connections and draw together a speculative ontological claim that all musicking is an enactment of play. In other words, we ask how…

Addressing the Sustainable Development Goals (SDGs) has become of paramount importance for higher education institutions; however, a lack of a transparent, transferrable approach to identifying and quantifying the extent of SDG contributions hinders the ability to benchmark against past performance or other institutions. In this study, a…

Reputable scholars are developing typologies of ignorance for different reasons including philosophizing about ignorance as well as understanding ignorance within professions, the scientific endeavour, and women's health (discussed herein). These four agnotological typologies (i.e., the study of ignorance) proved useful for revealing and…

Cybersecurity content is typically taught and assessed using Bloom's Taxonomy to ensure that students acquire foundational and higher-order knowledge. In this study we show that when students are given the objectives written in the form of a competency-based statements, students have a more clearly defined outcome and are be able to exhibit their…

Item compromise has long posed challenges in educational measurement, jeopardizing both test validity and test security of continuous tests. Detecting compromised items is therefore crucial to address this concern. The present literature on compromised item detection reveals two notable gaps: First, the majority of existing methods are based upon…

This research study holds practical significance for educators, school administrators, and mental health professionals. The study utilized structural equation modeling on two samples of elementary school students in grades three to five to create and validate condensed versions of the Social-Emotional Health Survey-Primary and the Me and My School…

This open access book maps the role of challenge-based learning (CBL) in the transformation of higher education pedagogy, towards being sector-informed as well as student-driven. CBL democratises the process of learning by repositioning students as drivers, who are empowered to make decisions on course content, assess needs in the real world and…

Most English learners (ELs) eventually gain sufficient English proficiency to be reclassified and receive instruction without linguistic supports. Though well-identified, prior regression discontinuity estimates for the effect of reclassification are estimated too imprecisely to detect policy-relevant effects. Applying a student fixed-effect…

Research on engineering practice and its relationship with engineering education is getting increased attention in the engineering education literature recently. In this article, we present a study of engineering practice in an electrical power utility company that focused specifically on the learning practices of professional engineers. Through…

Advances in machine learning (ML) offer new possibilities for science education research. We report on early progress in the design of an ML-based tool to analyze students' mechanistic sensemaking, working from a coding scheme that is aligned with previous work in physics education research (PER) and that is amenable to recently developed ML…

We propose an innovative, effective, and data-agnostic method to train a deep-neural network model with an extremely small training dataset, called VELR (Voting-based Ensemble Learning with Rejection). In educational research and practice, providing valid labels for a sufficient amount of data to be used for supervised learning can be very costly…

Using cognitive diagnostic modeling (CDM), this study identified subtypes of mathematics learning disability (MLD) based on children's numerical skills and examined the language and spatial precursors of these subtypes. Participants were 99 MLD children and 420 low achievers identified from 1839 Finnish children (966 boys) who were followed from…

Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3-6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy,…