Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about…

Notwithstanding the neuromyth controversy, the malleability of learning style preferences impacts the validity of the measurement instrument and the effectiveness of the associated model of learning. This study investigates the test-retest reliability and underlying dynamics of Kolb's Learning Style Inventory (KLSI). It surveys 245 college-level…

A slowdown or a speedup in response times across experimental conditions can be taken as evidence of online deployment of knowledge. However, response-time difference measures are rarely evaluated on their reliability, and there is no standard practice to estimate it. In this article, we used three open data sets to explore an approach to…

Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and usability. With the release of large language models (LLMs), new possibilities have emerged to…

In the context of virtual learning communities (VLCs), where the participants may not know each other, it is necessary to have a mechanism to help when deciding who to work with and what reliable contents and information sources are. This study aims to design a generic trust model, named T-VLC, applicable to VLCs, which can be adapted to different…

Background: Peer assessment has played an important role in large-scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop…

This article explores how well bibliometrics and altmetrics reflect research impact in relation to Boyer's Model of the Scholarship. Indices used for both types of metrics are explored and discussed while including an analysis on primary methodological works performed on each in the literature to date. As confirmatory in nature, we chose as our…

This study investigates inter- and intracoder reliability, proposing a new approach based on social network analysis (SNA) and exponential random graph models (ERGM). During a recent exit poll, the responses of voters to two open-ended questions were recorded. A coding experiment was conducted where a group of coders coded a sample of text…

Recent developments have enabled the modeling of longitudinal assessment data in a diagnostic classification model (DCM) framework. These longitudinal DCMs were developed to provide measures of student growth on a discrete scale in the form of attribute mastery transitions, thereby supporting categorical and criterion-referenced interpretations of…

Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a factor-analysis model; as such, the accuracy of omega estimates is likely to depend on correct…

Internalizing and externalizing problems are common targets for school mental health screening. Prior research supports the interpretation of scores from the Youth Internalizing Problems Screener (YIPS) and the Youth Externalizing Problems Screener (YEPS), which were developed separately yet intended as companion measures. We extended previous…

Diagnostic classification models (DCMs) are psychometric models that yield probabilistic classifications of respondents according to a set of discrete latent variables. The current study examines the recently introduced one-parameter log-linear cognitive diagnosis model (1-PLCDM), which has increased interpretability compared with general DCMs due…

Diagnostic classification models (DCMs) have grown in popularity as stakeholders increasingly desire actionable information related to students' skill competencies. Longitudinal DCMs offer a psychometric framework for providing estimates of students' proficiency status transitions over time. For both cross-sectional and longitudinal DCMs, it is…

Purpose: This study aims to develop and validate a scale to measure Teacher Entrepreneurial Behavior (TEB), which encapsulates the behaviors teachers employ to identify and amplify innovation in schools. TEB are catalysts for innovation, navigating their peers through risks and building trust, which empowers the collective to transcend structural…

Predictive research on the enrollment proportion of general education and vocational education is crucial to optimizing the regional talent structure and industrial structure adjustment. The reasonable enrollment proportion of general education and vocational education also plays an important role in the adjustment of the overall employment…