The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an…

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…

Purpose: The challenges of language assessment in bilinguals include a lack of assessment tools and bilingual speech-language pathology services. Additionally, the weighting of subtests in standardized tests has not been empirically explored to maximize sensitivity and specificity. Language exposure might also inform the decision to diagnose…

Student's shift of attention away from a current learning task to task-unrelated thought, also called mind wandering, occurs about 30% of the time spent on education-related activities. Its frequent occurrence has a negative effect on learning outcomes across learning tasks. Automated detection of mind wandering might offer an opportunity to…

Predicting what we will remember and forget is crucial for daily functioning. We were interested in whether evaluating something as likely to be remembered or forgotten leads to enhanced memory for "both" forms of information relative to information that was not judged for memorability. We presented participants with lists of words to…

Developmental science has experienced a vivid debate on whether young children prioritize goals over means in their prediction of others' actions. Influential developmental theories highlight the role of goal objects for action understanding. Yet, recent infant studies report evidence for the opposite. The empirical evidence is therefore…

The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at…

This study aimed to investigate the predictive role of critical thinking dispositions and new media literacies on the ability to detect fake news on social media. The sample group of the study consisted of 157 university students. Sosu Critical Thinking Dispositions Scale, New Media Literacy Scale, and fake news detection task were employed to…

Purpose: To date, there are no automated tools for the identification and fine-grained classification of paraphasias within discourse, the production of which is the hallmark characteristic of most people with aphasia (PWA). In this work, we fine-tune a large language model (LLM) to automatically predict paraphasia targets in Cinderella story…

Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish…

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…

Background: Formative assessments build a positive learning environment and provide feedback to enhance learning. This study examined the impact of online formative and low-stake summative assessments on medical students' learning outcomes in the Clinically Oriented Anatomy course from 2016 to 2020. We aimed to demonstrate that formative…

Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…

Teaching Institutions could benefit from Early Warning Systems to identify at-risk students before learning difficulties affect the quality of their acquired knowledge. An Early Warning System can help preemptively identify learners at risk of dropping out by monitoring them and analyzing their traces to promptly react to them so they can continue…

A quick and effective learning assessment is needed to evaluate the learning process. Many tools currently offer automatic assessment for subjective and objective questions; however, there is no such free tool that provides plagiarism detection among students for subjective questions in a learning management system (LMS). This article aims to…