Understanding software modelers' difficulties and evaluating their performance is crucial to Model-Driven Engineering (MDE) education. The software modeling process contains fine-grained information about the modelers' analysis and thought processes. However, existing research primarily focuses on identifying obvious issues in the software…

"How do witnesses make identification decisions when viewing a lineup?" Understanding the witness decision-making process is essential for researchers to develop methods that can reduce mistaken identifications and improve lineup practices. Yet, the inclusion of fillers has posed a pivotal challenge to this task because the traditional…

The recommendation is an active area of scientific research; it is also a challenging and fundamental problem in online education. However, classical recommender systems usually suffer from item cold-start issues. Besides, unlike other fields like e-commerce or entertainment, e-learning recommendations must ensure that learners have the adequate…

When selecting fillers to include in a police lineup, one must consider the level of similarity between the suspect and potential fillers. In order to reduce misidentifications, an innocent suspect should not stand out. Therefore, it is important that the fillers share some degree of similarity. Importantly, increasing suspect-filler similarity…

Representations of diversity and otherness in cartoons offer metaphors for identity that can affect children's perceptions and attitudes towards the potential and challenges associated with various forms of disability. This contribution analyses a corpus of animations made up of feature films, series, and short films with a focus on how disability…

Emotions of learners are fundamental and significant in e-learning as they encourage learning. Machine learning models are presented in the literature to look at how emotions may affect e-learning results that are improved and optimized. Nevertheless, the models that have been suggested so far are appropriate for offline mode, whereby data for…

This study evaluates techniques to identify high-quality teachers. Since tenure restricts dismissals of experienced teachers, schools must predict productivity and dismiss those expected to perform ineffectively prior to tenure receipt. Many states rely on evaluation scores to guide these personnel decisions without considering other dimensions of…

The implementation of prereferral models was being discussed in educational literature as early as 1979. Over the past decade, schools in the United States have begun to adopt prereferral models to meet multiple goals, such as reducing inappropriate referrals to special education, supporting students who face academic and behavioral challenges,…

Abstraction in business processes (BP) modeling arises from the recognition of similarities to the detriment of its differences. However, teaching modeling to beginning students in the context of process management is a hard task to perform, given the high level of abstraction required for these students to develop. This paper uses BP fragments to…

Although using machine learning for predicting which students are at risk of failing a course is indeed valuable, how can we identify which characteristics of individual students contribute to their being At-Risk? By characterising individual At-Risk students we could potentially advise on specific interventions or ways to reduce their probability…

The Lognormal Response Time (LNRT) model measures the speed of test-takers relative to the normative time demands of items on a test. The resulting speed parameters and model residuals are often analyzed for evidence of anomalous test-taking behavior associated with fast and poorly fitting response time patterns. Extending this model, we…

Background: Peer assessment has played an important role in large-scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop…

The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep…

Academic plagiarism is a pressing concern in educational institutions. With the emergence of artificial intelligence (AI) chatbots, like ChatGPT, potential risks related to cheating and plagiarism have increased. This study aims to investigate the authenticity capabilities of ChatGPT models 3.5 and 4 in generating novel, coherent, and accurate…

Colleges are increasingly turning to predictive analytics to identify "at-risk" students in order to target additional supports. While recent research demonstrates that the types of prediction models in use are reasonably accurate at identifying students who will eventually succeed or not, there are several other considerations for the…