This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…

Purpose: Anomia, or word-finding difficulty, is a prevalent and persistent feature of aphasia, a neurogenic language disorder affecting millions of people in the United States. Anomia assessments are essential for measuring performance and monitoring outcomes in clinical settings. This study aims to evaluate the reliability of response time (RT)…

Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world…

Today's college ACCUPLACER System's Computerized Placement Test (CPT) plays a major role for determining whether post-secondary students are ready for placement into Arithmetic, Elementary Algebra, Intermediate Algebra, or College Mathematics courses. It is widely used in most college systems in the U.S. This paper discusses some of the issues to…

The predictive ability of a mathematics test organized into a branching test for computer-interactive administration was investigated. Twenty-five blocks of five items were used in the branching. Each testee took 25 items, with each subsequent block being determined by prior performance. Results supported the branching technique. (JKS)

A tailored testing model employing the beta distribution, whose mean equals the difficulty of an item and whose variance is approximately equal to the sampling variance of the item difficulty, and employing conditional item difficulties, is proposed. (Author)

This paper reports the results of using a three-layer backpropagation artificial neural network to predict item difficulty in a reading comprehension test. Two network structures were developed, one with and one without a sigmoid function in the output processing unit. The data set, which consisted of a table of coded test items and corresponding…