Multi-site randomized controlled trials (RCTs) provide unbiased estimates of the average impact in the study sample. However, their ability to accurately predict the impact for individual sites outside the study sample, to inform local policy decisions, is largely unknown. To extend prior research on this question, we analyzed six multi-site RCTs…

Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students (N = 101) in comparison to students with other health conditions (N = 2465) and students with no health conditions (N = 25,077). We…

A neurological disorder, along with several behavioral issues, may be to blame for a child's subpar performance in the academic journey (such as anxiety, depression, learning disorders, and irritability). These symptoms can be used to diagnose children with ASD, and supervised machine learning models can help differentiate between ASD traits and…

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two-player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether…

Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…

The present meta-analysis aimed to improve on Involvement Load Hypothesis (ILH) by incorporating it into a broader framework that predicts incidental vocabulary learning. Studies testing the ILH were systematically collected and 42 studies meeting our inclusion criteria were analyzed. The model-selection approach was used to determine the optimal…

Purpose: Bayesian-based models for diagnosis are common in medicine but have not been incorporated into identification models for dyslexia. The purpose of the present study was to evaluate Bayesian identification models that included a broader set of predictors and that capitalized on recent developments in modeling the prevalence of dyslexia.…

Purpose: Vocal roughness is often present in many voice disorders but the assessment of roughness mainly depends on the subjective auditory-perceptual evaluation and lacks acoustic correlates. This study aimed to apply the concept of roughness in general sound quality perception to vocal roughness assessment and to characterize the relationship…

Several factors seem important to understand the nature of mathematical learning. Byrnes and Miller combined these factors into the Opportunity-Propensity model. In this study the model was used to predict the number-processing factor and the arithmetic fluency in grade 4 (n = 195) and grade 5 (n = 213). Gender, intelligence and affect (positive…

Purpose: We aim to identify the most accurate model for predicting adolescent (Grade 9) reading difficulties (RD) in reading fluency and reading comprehension using 17 kindergarten-age variables. Three models (neural networks, linear, and mixture) were compared based on their accuracy in predicting RD. We also examined whether the same or a…

Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…

Despite the overwhelming evidence that higher education data are nested at various levels, single-level techniques such as regression and analysis of variance are commonly used to investigate student outcomes. This is problematic as a mismatch in methodology and research questions can lead to biased parameter estimates. The purpose of this study…

For outsiders to higher education institutions (HEIs) in South Korea, predicting the outcomes of the International Education Quality Assurance System (IEQAS)--a Korean institutional accreditation system for HEIs--is challenging. The annual IEQAS accreditation has been conducted behind closed doors; the assessment process is confidential, and there…

Much research in autism spectrum disorders (ASD) has focused on the development of efficacious interventions to address the core deficits of ASD. However, the heterogeneous nature of ASD complicates the development of such interventions. With great heterogeneity in the expression of ASD's core deficits, it is unlikely that there is a one size fits…

Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…