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…

This dissertation uses sociocultural linguistics to investigate how Black undergraduates at a Historically White Institution (HWI) understand Blackness, and how their racial socialization experiences in their pre-college years inform that perspective. This project reveals that many Black undergraduates in the HWI context may not have the benefit…

Behavior is a form of communication. For many young children, they may engage in certain behaviors to consciously or subconsciously communicate a need to access something, such as a desired adult or peer, sensory stimulation, or a tangible item. Other times, children may engage in a behavior to escape or avoid something, such as a particular…

Teachers' conceptions of gifted and talented students significantly influence their nomination skills and teaching practices. However, research indicates that these conceptions are often incorrect or inconsistent, even among teachers who have completed coursework in gifted and talented education. This study aimed to explore pre-service teachers'…

Language educators employ visual depictions of people for reasons including use as writing or conversation prompts, use as illustrations of vocabulary, or simply use as decoration. Despite research documenting so-called visual agism across mass media, there has been little attention to the issue in such language education materials. This project…

This paper explores the growing complexity of detecting and differentiating generative AI from other AI interventions. Initially prompted by noticing how tools like Grammarly were being flagged by AI detection software, it examines how these popular tools such as Grammarly, EditPad, Writefull, and AI models such as ChatGPT and Microsoft Bing…

The field of gifted education in the United States faces an existential crisis due to well-founded charges that many school programs fail to identify students who reflect the diversity of the overall student population. In this study, we used a mixed methods light approach to explore gifted education coordinators' perceptions of equity and…

The current work introduces the concept of AI-trap questions as a tool for maintaining academic integrity in online courses. AI-trap questions are assessment tools designed to detect cheating by exploiting generative AI's tendency to provide answers that are common rather than context-specific. This paper explores theoretical perspectives of…

The on-going use of the Coolabah Dynamic Assessment (CDA; Chaffey, 2002) protocol to identify students with high learning potential is explored, with a particular emphasis on students from communities that are often under- represented in opportunities for high potential and gifted learners (i.e., students from culturally, linguistically and…

This study combined data from the National Survey of Children's Health (NSCH; 2016--2019) to develop state-level percentages of Hispanic, non-Hispanic Black, non-Hispanic other race, and non-Hispanic White children receiving developmental screening and/or monitoring and diagnosed with an autism spectrum disorder or developmental delay (ASD/DD).…

Sensory processing abilities are highly variable within and across people diagnosed with autism and attention-deficit/hyperactivity disorder (ADHD). This study examined the transdiagnostic nature of sensory processing abilities, and their association with features of autism and ADHD, in a large sample of autistic people (n = 495) and people with…

Background: In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have…

Measures of academic engagement and disruptive behavior in students are strong predictors of social, academic, and lifelong career outcomes. Educators need a measurement tool that is feasible, accurate, and cost-effective in order to identify students who may be in need of additional behavioral support. Direct Behavior Ratings (DBRs) offer a…

Early warning systems (EWS) using analytical tools that have been trained against prior years' data, can reliably predict dropout risk in individual students so that educators may intervene early to help avert this from happening. Risk profiles for dropouts aren't always useful since students often do not conform to the profiles. Researchers with…

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…