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Ziqian Xu; Fei Gao; Anqi Fa; Wen Qu; Zhiyong Zhang – Grantee Submission, 2024
Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this…
Descriptors: Statistical Analysis, Sample Size, Mediation Theory, Monte Carlo Methods
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
Hadis Anahideh; Nazanin Nezami; Abolfazl Asudeh – Grantee Submission, 2025
It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing unfairness.…
Descriptors: Correlation, Measurement Techniques, Guidelines, Semantics
Zhongdi Wu; Eric Larson; Makoto Sano; Doris Baker; Nathan Gage; Akihito Kamata – Grantee Submission, 2023
In this investigation we propose new machine learning methods for automated scoring models that predict the vocabulary acquisition in science and social studies of second grade English language learners, based upon free-form spoken responses. We evaluate performance on an existing dataset and use transfer learning from a large pre-trained language…
Descriptors: Prediction, Vocabulary Development, English (Second Language), Second Language Learning
Du, Han; Enders, Craig; Keller, Brian; Bradbury, Thomas N.; Karney, Benjamin R. – Grantee Submission, 2022
Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as…
Descriptors: Bayesian Statistics, Data Analysis, Computer Simulation, Sample Size
Bogdan Nicula; Mihai Dascalu; Tracy Arner; Renu Balyan; Danielle S. McNamara – Grantee Submission, 2023
Text comprehension is an essential skill in today's information-rich world, and self-explanation practice helps students improve their understanding of complex texts. This study was centered on leveraging open-source Large Language Models (LLMs), specifically FLAN-T5, to automatically assess the comprehension strategies employed by readers while…
Descriptors: Reading Comprehension, Language Processing, Models, STEM Education

Devika Venugopalan; Ziwen Yan; Conrad Borchers; Jionghao Lin; Vincent Aleven – Grantee Submission, 2025
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…
Descriptors: Homework, Computational Linguistics, Teaching Methods, Learning Analytics

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
Merkle, Edgar C.; Fitzsimmons, Ellen; Uanhoro, James; Goodrich, Ben – Grantee Submission, 2021
Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or…
Descriptors: Bayesian Statistics, Structural Equation Models, Psychometrics, Factor Analysis
Candace Walkington; Mitchell J. Nathan; Jonathan Hunnicutt; Julianna Washington; Kasi Holcomb-Webb – Grantee Submission, 2022
Novel forms of technology, like shared Augmented Reality (AR) holograms, can spur the discovery of new hypotheses about cognition and how it is embodied and distributed. These holograms have affordances for exploration, collaboration, and learning that have never been seen before. In the present study, we examine the multimodal ways that high…
Descriptors: Geometry, Mathematics Instruction, Schemata (Cognition), Teaching Methods

Julianna Washington; Taylor Darwin; Theodora Beauchamp; Candace Walkington – Grantee Submission, 2024
Prisms VR, a secondary math learning application, allows for users to see, manipulate, and engage with mathematical concepts in an embodied way in Virtual Reality (VR) environment. We examine cases in which mathematics teachers and middle school students worked through Prisms and reflected upon their experiences. Findings indicate that VR…
Descriptors: Mathematics Instruction, Teacher Attitudes, Computer Simulation, Algebra
Xu Qin; Lijuan Wang – Grantee Submission, 2023
Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued in substantive research. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and…
Descriptors: Causal Models, Mediation Theory, Computer Software, Statistical Analysis
Ethan Prihar; Morgan Lee; Mia Hopman; Adam Tauman Kalai; Sofia Vempala; Allison Wang; Gabriel Wickline; Aly Murray; Neil Heffernan – Grantee Submission, 2023
Large language models have recently been able to perform well in a wide variety of circumstances. In this work, we explore the possibility of large language models, specifically GPT-3, to write explanations for middle-school mathematics problems, with the goal of eventually using this process to rapidly generate explanations for the mathematics…
Descriptors: Mathematics Instruction, Teaching Methods, Artificial Intelligence, Middle School Students
Yizhu Gao; Xiaoming Zhai; Min Li; Gyeonggeon Lee; Xiaoxiao Liu – Grantee Submission, 2025
The rapid evolution of generative artificial intelligence (GenAI) is transforming science education by facilitating innovative pedagogical paradigms while raising substantial concerns about scholarly integrity. One particularly pressing issue is the growing risk of student use of GenAI tools to outsource assessment tasks, potentially compromising…
Descriptors: Artificial Intelligence, Computer Software, Science Education, Integrity

Kelsey E. Schenck; Doy Kim; Fangli Xia; Michael I. Swart; Candace Walkington; Mitchell J. Nathan – Grantee Submission, 2024
Access to body-based resources has been shown to augment cognitive processes, but not all movements equally aid reasoning. Interactive technologies, like dynamic geometry systems (DGS), potentially amplify the link between movement and geometric representation, thereby deepening students' understanding of geometric properties. This study…
Descriptors: Geometric Concepts, Task Analysis, Thinking Skills, Validity