Understanding attrition risks specific to online student populations is crucial for the long-term success of online programs. Online programs allow place-based working professionals access to education needed for professional development and career advancement. This study was conducted to determine if educational preparation, student…

This study explored how caregiver-reported executive functioning domains, assessed by the BRIEF2 at baseline, predicted behavioral challenges reported by caregivers using the CBCL six months later. The sample included 94 youth with Down syndrome, aged 6 to 18 years. Results of hierarchical regression analyses revealed that, after controlling for…

Background: The study focuses on the need to optimise assessment scales for support needs in individuals with intellectual and developmental disabilities. Current scales are often lengthy and redundant, leading to exhaustion and response burden. The goal is to use machine learning techniques, specifically item-reduction methods and selection…

Existing toolkits for analyzing movement dynamics in animal ecology primarily focus on individual or group behavior in habitats without predefined boundaries, while methods for studying human activity often cater to bounded environments, such as team sports played on defined fields. This leaves a gap in tools for modeling and analyzing human group…

The study intended to describe profiles of three achievement emotions (enjoyment, boredom, anxiety), their associations with each other and with control-value appraisals within the framework of the control-value theory. A total of 2002 Chinese university EFL students from 11 universities in China participated in the questionnaire survey.…

Background: Perceptual evaluation of speech disorders produces scores that poorly predict the consequences of speech impairment on the communication abilities of patients treated for oral/oropharyngeal cancer. This may be mitigated by automatic speech analysis. Aim: To measure communication and speech impairment using automatic analyses of…

Feature engineering plays a critical role in the development of machine learning systems for educational contexts, yet its impact on student trust remains understudied. Traditional approaches have focused primarily on optimizing model performance through expert-crafted features, while the emergence of AutoML offers automated alternatives for…

Knowledge Tracing (KT) models predicting student performance in intelligent tutoring systems have been successfully deployed in several educational domains. However, their usage in open-ended programming problems poses multiple challenges due to the complexity of the programming code and a complex interplay between syntax and logic requirements…

Despite the spread of artificial intelligence (AI) tools and applications, the Apple Vision Pro (AVP) stands out for its innovative features compared to other types of wearable technology. Moreover, traditional glasses have been deficient in incorporating many AI innovations that could enhance user experiences and pose new challenges. In response…

This chapter uses critical perspectives on whiteness to critique higher education's institutional research practice. After briefly describing institutional research, we summarize scholarship about autonomy, ethics, and predictive analytics to illustrate how existing guidance and beliefs about institutional research often dehumanize students by…

For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by trained human raters. This process, the current standard, is both time-consuming and limiting: even the largest human coding efforts are typically constrained to…

We fine-tuned and compared several encoder-based Transformer large language models (LLM) to predict differential item functioning (DIF) from the item text. We then applied explainable artificial intelligence (XAI) methods to identify specific words associated with the DIF prediction. The data included 42,180 items designed for English language…

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…

Choosing a field of study (study major) is challenging for prospective students. However, little research has examined factors measured prior to enrollment to predict motivation and well-being in a specific study major. Based on literature on affective forecasting and person-environment fit, prospective students' well-being forecast could be such…

Since the introduction by Kermack and McKendrick in 1927, the Susceptible-Infected-Recovered (SIR) epidemic model has been a foundational model to comprehend and predict the dynamics of infectious diseases. Almost for a century, the SIR model has been modified and extended to meet the needs of different characteristics of various infectious…