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Tao Huang; Jing Geng; Yuxia Chen; Han Wang; Huali Yang; Shengze Hu – Education and Information Technologies, 2024
Digital technology is profoundly transforming various aspects of life, thus highlighting the need to enhance digital literacy on a national scale. In primary and secondary schools, artificial intelligence (AI) education plays a pivotal role in fostering digital literacy. To comprehensively investigate the variables influencing AI education in…
Descriptors: Artificial Intelligence, Elementary Schools, Secondary Schools, Prediction
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Hyunyi Jung; Corey Brady – Mathematical Thinking and Learning: An International Journal, 2025
In this article, we investigated how a class engaged in whole-class discussions of modeling solutions, across two different types of modeling tasks: a Model Eliciting Activity (MEA) and a Three-Act Task. We analyzed whole-class discourse as small groups presented and responded to each other's solutions to a MEA and a Three-Act Task. We have also…
Descriptors: Mathematical Models, Discussion (Teaching Technique), Class Activities, Simulation
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Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Jorge Salas – Journal of Educational Measurement, 2024
Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore,…
Descriptors: Item Response Theory, Reaction Time, Models, Accuracy
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Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Jorge Salas – Grantee Submission, 2024
Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore,…
Descriptors: Item Response Theory, Reaction Time, Models, Accuracy
Gao, Ruiqin – ProQuest LLC, 2023
This multiple-manuscript dissertation explored the measurement invariance (MI) testing with multiple-group confirmatory factor analysis (MG-CFA) approach from different perspectives. Study 1 explored MI from a theoretical perspective by conducting a systematic review study on MI practices in education. The findings of this study indicated…
Descriptors: Error of Measurement, Factor Analysis, Simulation, Elementary School Students
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Jones, Ryan Seth; Jia, Zhigang; Bezaire, Joel – Mathematics Teacher: Learning and Teaching PK-12, 2020
Too often, statistical inference and probability are treated in schools like they are unrelated. In this paper, we describe how we supported students to learn about the role of probability in making inferences with variable data by building models of real world events and using them to simulate repeated samples.
Descriptors: Statistical Inference, Probability, Mathematics Instruction, Mathematical Models
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Emily K. Toutkoushian; Kihyun Ryoo – Measurement: Interdisciplinary Research and Perspectives, 2024
The Next Generation Science Standards (NGSS) delineate three interrelated dimensions that describe what students should know and how they should engage in science learning. These present significant challenges for assessment because traditional assessments may not be able to capture the ways in which students engage with content. Science…
Descriptors: Middle School Students, Academic Standards, Science Education, Learner Engagement
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Dong, Yixiao; Dumas, Denis; Clements, Douglas H.; Sarama, Julie – Journal of Experimental Education, 2023
Dynamic Measurement Modeling (DMM) is a recently-developed measurement framework for gauging developing constructs (e.g., learning capacity) that conventional single-timepoint tests cannot assess. The current project developed a person-specific DMM Trajectory Deviance Index (TDI) that captures the aberrance of an individual's growth from the…
Descriptors: Measurement Techniques, Simulation, Student Development, Educational Research
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Provost, Amanda; Lim, Su San; York, Toni; Panorkou, Nicole – North American Chapter of the International Group for the Psychology of Mathematics Education, 2022
The frequentist and classical models of probability provide students with different lenses through which they can view probability. Prior research showed that students may bridge these two lenses through instructional designs that begin with a clear connection between the two, such as coin tossing. Considering that this connection is not always…
Descriptors: Probability, Models, Mathematics Instruction, Teaching Methods
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Biehler, Rolf; Frischemeier, Daniel; Podworny, Susanne – ZDM: The International Journal on Mathematics Education, 2018
Elements of statistical modeling can be implemented already in primary school. A prerequisite for this approach is that teachers are well-educated in this domain. Content knowledge, pedagogical content knowledge and (pedagogical) content related technological knowledge are core components of teacher education. We designed a course for elementary…
Descriptors: Preservice Teachers, Elementary School Teachers, Pedagogical Content Knowledge, Civics
Edward J. Kim – Annenberg Institute for School Reform at Brown University, 2022
This study introduces the signal weighted teacher value-added model (SW VAM), a value-added model that weights student-level observations based on each student's capacity to signal their assigned teacher's quality. Specifically, the model leverages the repeated appearance of a given student to estimate student reliability and sensitivity…
Descriptors: Value Added Models, Student Evaluation, Reliability, Simulation
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Brigas, Carlos Jorge – Research in Social Sciences and Technology, 2019
In science education, there is a need to evaluate the behavior of dynamic systems. Representing and explaining processes through educational models or simulations enables students to perform activities where it is easier to understand these processes and discover the essential properties of a system. Performing modeling or simulation activities…
Descriptors: Science Education, Models, Simulation, Teaching Methods
James Soland; Megan Kuhfeld – Annenberg Institute for School Reform at Brown University, 2020
Survey respondents use different response styles when they use the categories of the Likert scale differently despite having the same true score on the construct of interest. For example, respondents may be more likely to use the extremes of the response scale independent of their true score. Research already shows that differing response styles…
Descriptors: Social Emotional Learning, Scores, Likert Scales, Surveys
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Oh, Phil Seok – Science & Education, 2019
The purpose of this study was to investigate the features of modeling-based abductive reasoning as a disciplinary practice of inquiry in the domain of earth science. The study was based on an undergraduate course of a university of education, Korea, offered for preservice elementary teachers majoring in science as their specialty. The course…
Descriptors: Foreign Countries, Logical Thinking, Inquiry, Science Process Skills
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Cheng, Ying; Shao, Can; Lathrop, Quinn N. – Educational and Psychological Measurement, 2016
Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable…
Descriptors: Test Bias, Models, Simulation, Sample Size
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