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Pengchong Zhang; Shi Zhang – Language Learning & Technology, 2025
Multimodal input can significantly support second language (L2) vocabulary learning and comprehension. However, very little research has examined how L2 learners, especially young learners, allocate attention when exposed to such input and whether learning from multimodal input can be explained by attention allocation. This study therefore…
Descriptors: Attention, Eye Movements, Vocabulary Development, Reading Comprehension
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Owen Henkel; Hannah Horne-Robinson; Maria Dyshel; Greg Thompson; Ralph Abboud; Nabil Al Nahin Ch; Baptiste Moreau-Pernet; Kirk Vanacore – Journal of Learning Analytics, 2025
This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori, a mathematics learning platform used by middle and high school students in several African countries. Using this dataset, we conducted two experiments to evaluate the use of large language models (LLM) for grading particularly challenging…
Descriptors: Learning Analytics, Learning Management Systems, Mathematics Instruction, Middle School Students
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Peng Peng; Wei Wang; Marissa J. Filderman; Wenxiu Zhang; Lifeng Lin – Review of Educational Research, 2024
Based on 52 studies with samples mostly from English-speaking countries, the current study used Bayesian network meta-analysis to investigate the intervention effectiveness of different reading comprehension strategy combinations on reading comprehension among students with reading difficulties in 3rd through 12th grade. We focused on commonly…
Descriptors: Reading Comprehension, Reading Strategies, Reading Difficulties, Reading Instruction
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Alex C. Garn; Andreas Stenling – Educational Psychology, 2024
This study investigated daily motivation regulation as a multilevel mediator of undergraduate students' intrinsic and extrinsic motivation and academic functioning. Undergraduate students (N = 124) completed measures on motivation, motivation regulation, and study time for 10 consecutive days leading up to a statistics exam. Bayesian multilevel…
Descriptors: Student Motivation, Prediction, Academic Achievement, Undergraduate Students
Lauren Kennedy; Daniel Simpson; Andrew Gelman – Grantee Submission, 2019
Cognitive modelling shares many features with statistical modelling, making it seem trivial to borrow from the practices of robust Bayesian statistics to protect the practice of robust cognitive modelling. We take one aspect of statistical workflow--prior predictive checks--and explore how they might be applied to a cognitive modelling task. We…
Descriptors: Models, Cognitive Measurement, Experiments, Statistical Analysis
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Uwimpuhwe, Germaine; Singh, Akansha; Higgins, Steve; Kasim, Adetayo – International Journal of Research & Method in Education, 2021
Educational researchers advocate the use of an effect size and its confidence interval to assess the effectiveness of interventions instead of relying on a p-value, which has been blamed for lack of reproducibility of research findings and the misuse of statistics. The aim of this study is to provide a framework, which can provide direct evidence…
Descriptors: Educational Research, Randomized Controlled Trials, Bayesian Statistics, Effect Size
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Kaplan, David; Huang, Mingya – Large-scale Assessments in Education, 2021
Of critical importance to education policy is monitoring trends in education outcomes over time. In the United States, the National Assessment of Educational Progress (NAEP) has provided long-term trend data since 1970; at the state/jurisdiction level, NAEP has provided long-term trend data since 1996. In addition to the national NAEP, all 50…
Descriptors: Educational Policy, Educational Trends, National Competency Tests, Bayesian Statistics
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Rehrig, Gwendolyn L.; Cheng, Michelle; McMahan, Brian C.; Shome, Rahul – Cognitive Research: Principles and Implications, 2021
A major problem in human cognition is to understand how newly acquired information and long-standing beliefs about the environment combine to make decisions and plan behaviors. Over-dependence on long-standing beliefs may be a significant source of suboptimal decision-making in unusual circumstances. While the contribution of long-standing beliefs…
Descriptors: Cognitive Processes, Decision Making, Semantics, Bayesian Statistics
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Binder, Karin; Krauss, Stefan; Schmidmaier, Ralf; Braun, Leah T. – Advances in Health Sciences Education, 2021
When physicians are asked to determine the positive predictive value from the a priori probability of a disease and the sensitivity and false positive rate of a medical test (Bayesian reasoning), it often comes to misjudgments with serious consequences. In daily clinical practice, however, it is not only important that doctors receive a tool with…
Descriptors: Clinical Diagnosis, Efficiency, Probability, Bayesian Statistics
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Kang, Jina; Baker, Ryan; Feng, Zhang; Na, Chungsoo; Granville, Peter; Feldon, David F. – Instructional Science: An International Journal of the Learning Sciences, 2022
Threshold concepts are transformative elements of domain knowledge that enable those who attain them to engage domain tasks in a more sophisticated way. Existing research tends to focus on the identification of threshold concepts within undergraduate curricula as challenging concepts that prevent attainment of subsequent content until mastered.…
Descriptors: Fundamental Concepts, Bayesian Statistics, Learning Processes, Research Skills
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Shi, Dingjing; Tong, Xin – Sociological Methods & Research, 2022
This study proposes a two-stage causal modeling with instrumental variables to mitigate selection bias, provide correct standard error estimates, and address nonnormal and missing data issues simultaneously. Bayesian methods are used for model estimation. Robust methods with Student's "t" distributions are used to account for nonnormal…
Descriptors: Bayesian Statistics, Monte Carlo Methods, Computer Software, Causal Models
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Mead, Alan D.; Zhou, Chenxuan – Journal of Applied Testing Technology, 2022
This study fit a Naïve Bayesian classifier to the words of exam items to predict the Bloom's taxonomy level of the items. We addressed five research questions, showing that reasonably good prediction of Bloom's level was possible, but accuracy varies across levels. In our study, performance for Level 2 was poor (Level 2 items were misclassified…
Descriptors: Artificial Intelligence, Prediction, Taxonomy, Natural Language Processing
Ross, Linette P. – ProQuest LLC, 2022
One of the most serious forms of cheating occurs when examinees have item preknowledge and prior access to secure test material before taking an exam for the purpose of obtaining an inflated test score. Examinees that cheat and have prior knowledge of test content before testing may have an unfair advantage over examinees that do not cheat. Item…
Descriptors: Testing, Deception, Cheating, Identification
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Na Shan; Ping-Feng Xu – Journal of Educational and Behavioral Statistics, 2025
The detection of differential item functioning (DIF) is important in psychological and behavioral sciences. Standard DIF detection methods perform an item-by-item test iteratively, often assuming that all items except the one under investigation are DIF-free. This article proposes a Bayesian adaptive Lasso method to detect DIF in graded response…
Descriptors: Bayesian Statistics, Item Response Theory, Adolescents, Longitudinal Studies
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Demir, Ibrahim; Sener, Ersin; Karaboga, Hasan Aykut; Basal, Ahmet – Participatory Educational Research, 2023
Classroom rules are a fundamental aspect of classroom management and ensuring compliance with established rules is crucial. Previous research has shown that students often pay little attention to the development of classroom rules. This quantitative study aims to investigate the expectations that students have concerning classroom rules. To this…
Descriptors: Classroom Techniques, Bayesian Statistics, Compliance (Psychology), Secondary School Students
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