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Austin Wyman; Zhiyong Zhang – Grantee Submission, 2025
Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…
Descriptors: Artificial Intelligence, Algorithms, Computer Software, Identification
Xiao Liu; Zhiyong Zhang; Lijuan Wang – Grantee Submission, 2024
In psychology, researchers are often interested in testing hypotheses about mediation, such as testing the presence of a mediation effect of a treatment (e.g., intervention assignment) on an outcome via a mediator. An increasingly popular approach to testing hypotheses is the Bayesian testing approach with Bayes factors (BFs). Despite the growing…
Descriptors: Sample Size, Bayesian Statistics, Programming Languages, Simulation
Daniel McNeish – Grantee Submission, 2023
Scale validation is vital to psychological research because it ensures that scores from measurement scales represent the intended construct. Factor analysis fit indices are commonly used to provide quantitative evidence that a proposed factor structure is plausible. However, there is mismatch between guidelines for evaluating fit of factor models…
Descriptors: Factor Analysis, Goodness of Fit, Validity, Likert Scales
Chenchen Ma; Jing Ouyang; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational…
Descriptors: Algorithms, Item Response Theory, Scoring, Accuracy

Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages
W. Jake Thompson – Grantee Submission, 2023
In educational and psychological research, we are often interested in discrete latent states of individuals responding to an assessment (e.g., proficiency or non-proficiency on educational standards, the presence or absence of a psychological disorder). Diagnostic classification models (DCMs; also called cognitive diagnostic models [CDMs]) are a…
Descriptors: Bayesian Statistics, Measurement, Psychometrics, Educational Research

Arun-Balajiee Lekshmi-Narayanan; Priti Oli; Jeevan Chapagain; Mohammad Hassany; Rabin Banjade; Vasile Rus – Grantee Submission, 2024
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide…
Descriptors: Coding, Computer Science Education, Computational Linguistics, Artificial Intelligence
Lijin Zhang; Xueyang Li; Zhiyong Zhang – Grantee Submission, 2023
The thriving developer community has a significant impact on the widespread use of R software. To better understand this community, we conducted a study analyzing all R packages available on CRAN. We identified the most popular topics of R packages by text mining the package descriptions. Additionally, using network centrality measures, we…
Descriptors: Computer Software, Programming Languages, Data Analysis, Visual Aids
Xue Zhang; Chun Wang – Grantee Submission, 2021
Among current state-of-art estimation methods for multilevel IRT models, the two-stage divide-and-conquer strategy has practical advantages, such as clearer definition of factors, convenience for secondary data analysis, convenience for model calibration and fit evaluation, and avoidance of improper solutions. However, various studies have shown…
Descriptors: Error of Measurement, Error Correction, Item Response Theory, Comparative Analysis
Zhanxia Yang; Patricia Moore Shaffer; Courtney Hagan; Parastu Dubash; Marina Bers – Grantee Submission, 2023
The aim of this study was to explore how the Coding as Another Language (CAL) curriculum, developed by Boston College's DevTech Research Group and utilizing the ScratchJr app, impacted students' computational thinking, coding skills, and reading comprehension. To accomplish this, the research team randomly assigned thirteen schools in a…
Descriptors: Coding, Second Language Learning, Curriculum Development, Programming Languages
Xu Qin; Fan Yang – Grantee Submission, 2022
Causal inference regarding a hypothesized mediation mechanism relies on the assumptions that there are no omitted pretreatment confounders (i.e., confounders preceding the treatment) of the treatment-mediator, treatment-outcome, and mediator-outcome relationships, and there are no posttreatment confounders (i.e., confounders affected by the…
Descriptors: Simulation, Correlation, Inferences, Attribution Theory
Kaitlyn G. Fitzgerald; Elizabeth Tipton – Grantee Submission, 2022
As the body of scientific evidence about effective policies and practices grows, so does the need to effectively communicate that evidence to policy-makers and practitioners. Clearinghouses have emerged to facilitate the evidence-based decision-making process for education practitioners. While the results and methods for developing and analyzing…
Descriptors: Meta Analysis, Scientific Research, Evidence Based Practice, Decision Making
Zhanxia Yang; Patricia Moore Shaffer; Courtney Hagan; Parastu Dubash; Marina Bers – Grantee Submission, 2023
The aim of this study was to explore how the Coding as Another Language using ScratchJr (CAL-ScratchJr) curriculum, developed by Boston College's DevTech Research Group utilizing the ScratchJr app, impacted second grade students' computational thinking, coding skills, and reading comprehension. To accomplish this, the research team randomly…
Descriptors: Coding, Programming Languages, Computer Science Education, School Districts
Rich, Peter Jacob; Hu, Helen; Christensen, James; Ellsworth, Jordan – Grantee Submission, 2019
This report provides a comprehensive look at computer science (CS) education across Utah elementary, middle/jr. high, and high schools as of 2018. The Utah Expanding Computing Education Pathways (ECEP) team sent out a survey to all public schools in the state of Utah. The survey presented targeted questions depending on whether it was completed…
Descriptors: Computer Science Education, Public Schools, Teacher Attitudes, Enrollment Trends