The increasing rate of permanent school closures in U.S. public school districts presents unprecedented challenges for administrators and communities alike. This study develops an early-warning indicator model to predict mass closure events -- defined as a district closing at least 10% of its schools -- five years in advance. Leveraging…

The purpose of this study was to validate a novel reading comprehension assessment for college students named MOCCA-College. A random sample of college students (N = 63, average age of 22.5) were recruited from various education programs (e.g., first-year courses, TRIO, SONA) and completed MOCCA-College Online and were later recruited to complete…

Background: Analyzing student short-answer written justifications to conceptually challenging questions has proven helpful to understand student thinking and improve conceptual understanding. However, qualitative analyses are limited by the burden of analyzing large amounts of text. Purpose: We apply dense and sparse Large Language Models (LLMs)…

This study evaluates the performance of three leading AI chatbots--OpenAI's ChatGPT, Google's Gemini, and Microsoft Bing Copilot--in answering multiple choice questions (MCQs) from the UGC-NET Education paper. Using 150 randomly selected questions from examination cycles between June 2019 and December 2023, the chatbots' accuracy was assessed…

This study examines the effectiveness of Lesson Study (LS) in enhancing the writing accuracy of EFL learners. A group of 63 intermediate male EFL learners from Gofteman Language Institute in Ardebil, Iran, divided into two experimental groups and one control group, participated in the research. After taking a grammar pre-test on the target…

We implemented tact matrix training to teach tacts of spatial locations to four children (male, 4-7 years of age) on the autism spectrum in China. The experimental design involved a multiple-probe design across participants with pre- and postinstruction probes on untaught tacts and listener responses. Learning outcomes included taught tacts of…

Machine learning-based automatic scoring faces challenges with imbalanced student responses across scoring categories. To address this, we introduce a novel text data augmentation framework that leverages GPT-4, a generative large language model specifically tailored for imbalanced datasets in automatic scoring. Our experimental dataset consisted…

Online calibration estimates new item parameters alongside previously calibrated items, supporting efficient item replenishment. However, most existing online calibration procedures for Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) lack mechanisms to ensure content balance during live testing. This limitation can lead to uneven…

The "Test Adaptation Reporting Standards" (TARES), or "TARES statement" was developed to alleviate the problems arising from inadequate reporting of test adaptation procedures. The TARES contains a short preamble and a checklist, that comprises an evidence-based minimum set of information for reporting in test adaptations. The…

The higher the level of development of higher education, the larger its contribution to socioeconomic development. In order to predict the trend of higher education development in a country more accurately, a new methodology is employed in this study. A weakening buffer operator-based GM (1, 1) model is constructed using Kazakhstan's gross…

Purpose: Differences in social cognition between autistic and non-autistic people may put autistic people at greater risk of being deceived. To inform communication interventions related to deception, the purposes of this mixed-methods study were to examine the deception detection strategies used by young adults with varying levels of autistic…

To explore the dynamics of processing in manuscript production, it is necessary to possess a system for recording the writer's graphic activity. This work describes the new version of the Eye and Pen program (version 3.01). In addition to the fact that it is now freely available (https://www.eyeandpen.net), the improvements described focus on its…

A random-effects model is often applied in meta-analysis when considerable heterogeneity among studies is observed due to the differences in patient characteristics, timeframe, treatment regimens, and other study characteristics. Since 2014, the journals "Research Synthesis Methods" and the "Annals of Internal Medicine" have…

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

Carefully chosen picturebooks offer a developmentally appropriate medium for helping children understand even the most complex challenges. However, it may be difficult for teachers to find high-quality children's books for explaining multifaceted topics such as climate change. This study explores the contents of a set of picturebooks about climate…