Based on the self-determination theory (SDT), this evaluates the effects of generative artificial intelligence (Gen-AI) on learning performance within China's education sector, emphasizing the roles of social interaction, utilitarian benefit, knowledge acquisition, and epistemic curiosity. The study employs a dual method, using PLS-SEM and fsQCA…

Students' reading ability affects their outcomes in learning software even outside of reading education, such as in math education, which can result in unexpected and inequitable outcomes. We analyze an adaptive learning software using Bayesian Knowledge Tracing (BKT) to understand how the fairness of the software is impacted when reading ability…

A hallmark of effective teaching is that it grants learners not just a collection of facts about the world, but also a toolkit of abstractions that can be applied to solve new problems. How do humans teach abstractions from examples? Here, we applied Bayesian models of pedagogy to a necklace-building task where teachers create necklaces to teach a…

Ineffective use of resources and suboptimal management at the school level can detrimentally affect human resource development and labour market outcomes. Therefore, efficiently managing human and physical resources is crucial to transforming school inputs into improved educational outcomes. This study assesses the effectiveness of educational…

To this date, few standardized tests measuring students' performance with regards to statistics exist. Only four tests have been proposed for college or university students. The goal of the present study is to investigate these tests. University professors or instructors experienced in teaching statistics were asked to list the concepts they think…

This study concludes that traditional logistic regression models, particularly those using ACT Composite scores, tend to demonstrate better fairness metrics across subgroups compared to a fairness-aware machine learning gradient-boosted machine model. The exclusion of race/ethnicity from predictive models does not introduce notable bias and may…

Many scholars have traditionally examined features linked to students' science performance by using classical statistical methods. However, there is a dearth of research on predicting student achievements by using machine learning (ML) models in science education. We aim to address this gap using the 2006 and 2015 Program for International Student…

Previous psychophysical studies reported a positive aftereffect in tactile temporal order judgments, which can be explained by the Bayesian estimation model ('Bayesian calibration'). We investigated the relationship between Bayesian calibration and autistic traits in participants with typical development (TD) and autism spectrum disorder (ASD).…

The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test…

Effect modification (EM) may cause bias in network meta-analysis (NMA). Existing population adjustment NMA methods use individual patient data to adjust for EM but disregard available subgroup information from aggregated data in the evidence network. Additionally, these methods often rely on the shared effect modification (SEM) assumption. In this…

Purpose: The purpose of this study was twofold: (1) to determine whether there are speech rhythm differences between preschool-age children who stutter that were eventually diagnosed as persisting (CWS-Per) or recovered (CWS-Rec) and children who do not stutter (CWNS), using empirical spectral analysis and empirical mode decomposition of the…

Students typically find research methods and statistics classes intimidating. In particular, learning different types of data measurement and operationalization can take significant time and practice to understand. Utilizing a pedagogical approach that turns a party game into a fun exercise, students collaboratively learn to operationalize…

Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were…

This study analyses academics' commitment and job satisfaction according to full-time non-tenure-track (FTNT) and full-time tenure-track (FTT) in South Korea's changing academic environment. Data were collected from the 2018 Academic Profession in the Knowledge-Based Society Survey. One-way analyses of variance show no statistically significant…

To evaluate multidimensional factor structure, a popular method that combines features of confirmatory and exploratory factor analysis is Bayesian structural equation modeling with small-variance normal priors (BSEM-N). This simulation study evaluated BSEM-N as a variable selection and parameter estimation tool in factor analysis with sparse…