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No Child Left Behind Act 20011
Showing 1 to 15 of 253 results Save | Export
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Alicia M. Chen; Andrew Palacci; Natalia Vélez; Robert D. Hawkins; Samuel J. Gershman – Cognitive Science, 2024
How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N…
Descriptors: Bayesian Statistics, Models, Teaching Methods, Evaluation
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de Jong, Bastian; Jansen in de Wal, Joost; Cornelissen, Frank; van der Lans, Rikkert; Peetsma, Thea – International Journal of Training and Development, 2023
Transfer motivation is an important factor influencing transfer of training. However, earlier research often did not investigate transfer motivation as a multidimensional construct. The unified model of task-specific motivation (UMTM) takes into account that (transfer) motivation is multidimensional by including both affective and cognitive…
Descriptors: Informed Consent, Transfer of Training, Prediction, Models
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Gleiman, Ashley – Journal of Continuing Higher Education, 2023
Prior Learning Assessment (PLA) programs are unique to the institution and the students they serve. While a variety of best practices persist, the need for academically rigorous and credible programs is ever-present as institutions evolve to keep up with the growing needs of adult learners today. This article provides a case study overview of…
Descriptors: Prior Learning, Portfolio Assessment, Best Practices, Adult Learning
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Roee Peretz; Natali Levi-Soskin; Dov Dori; Yehudit Judy Dori – IEEE Transactions on Education, 2024
Contribution: Model-based learning improves systems thinking (ST) based on students' prior knowledge and gender. Relations were found between textual, visual, and mixed question types and student achievements. Background: ST is essential to judicious decision-making and problem-solving. Undergraduate students can be taught to apply better ST, and…
Descriptors: Models, Engineering Education, Thinking Skills, Systems Approach
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Mao, Shun; Zhan, Jieyu; Wang, Yizhao; Jiang, Yuncheng – IEEE Transactions on Learning Technologies, 2023
For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Prediction, Prior Learning
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Durak, Benzegul; Topçu, Mustafa Sami – Science Activities: Projects and Curriculum Ideas in STEM Classrooms, 2023
Recent research suggests that integrating model-based learning and socioscientific issue based instruction helps students construct meaningful learning in science classrooms. Thus, this paper presents a unit plan that integrates model-based learning and socioscientific issues. The focus of the unit is the white butterfly which is a local pest. A…
Descriptors: Science and Society, Models, Science Instruction, Middle School Students
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Ghudkam, Supachai; Chatwattana, Pinanta; Piriyasurawong, Pallop – Higher Education Studies, 2023
An imagineering learning model using advance organizers with the internet of things was developed to promote creative innovation for learners in the 21st century. It is an innovation initiated by integrating classroom learning and technology that connects with the internet of things. The objectives of this research were (1) to study and synthesize…
Descriptors: Advance Organizers, Models, Imagination, Problem Solving
Dragos Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2023
Reading comprehension is essential for both knowledge acquisition and memory reinforcement. Automated modeling of the comprehension process provides insights into the efficacy of specific texts as learning tools. This paper introduces an improved version of the Automated Model of Comprehension, version 3.0 (AMoC v3.0). AMoC v3.0 is based on two…
Descriptors: Reading Comprehension, Models, Concept Mapping, Graphs
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D. R. E. Cotton; S. Bloxham; S. Cooper; J. Downey; M. Fornasiero – Journal of Further and Higher Education, 2024
Knowledge exchange (KE) is increasingly important in higher education internationally, yet relatively little attention has been paid to it as a pedagogic opportunity for students. This paper draws on 26 interviews with stakeholders within and outside HE to develop a model of student-led knowledge exchange as a guide for learning through KE. The…
Descriptors: Higher Education, Stakeholders, Attitudes, Models
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Butterfuss, Reese; Kendeou, Panayiota – Educational Psychology Review, 2021
The aim of this paper is two-fold. The first aim is to review the core representational and processing aspects of influential accounts of single-document and multiple-document comprehension with a particular emphasis on how readers negotiate conflicting information during reading. This review provides the groundwork for the second aim--to expand…
Descriptors: Reading Comprehension, Cognitive Processes, Conflict, Misconceptions
Butterfuss, Reese; Kendeou, Panayiota – Grantee Submission, 2021
The aim of this paper is two-fold. The first aim is to review the core representational and processing aspects of influential accounts of single-document and multiple-document comprehension with a particular emphasis on how readers negotiate conflicting information during reading. This review provides the groundwork for the second aim--to expand…
Descriptors: Reading Comprehension, Cognitive Processes, Conflict, Misconceptions
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Televantou, Ioulia; Marsh, Herbert W.; Xu, Kate M.; Guo, Jiesi; Dicke, Theresa – Educational Psychology Review, 2023
The present study uses doubly latent models to estimate the effect of average mathematics achievement at the class level on students' subsequent mathematics achievement (the "Peer Spillover Effect") and mathematics self-concept (the "Big-Fish-Little-Pond-Effect; BFLPE"), controlling for individual differences in prior…
Descriptors: Error of Measurement, Mathematics Achievement, Self Concept, Individual Differences
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Julius Moritz Meier; Peter Hesse; Stephan Abele; Alexander Renkl; Inga Glogger-Frey – Journal of Computer Assisted Learning, 2024
Background: In example-based learning, examples are often combined with generative activities, such as comparative self-explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self-explanations. While static text-based examples can…
Descriptors: Video Technology, Models, Cues, Problem Solving
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Bichler, Sarah; Stadler, Matthias; Bühner, Markus; Greiff, Samuel; Fischer, Frank – Instructional Science: An International Journal of the Learning Sciences, 2022
Extensive research has established that successful learning from an example is conditional on an important learning activity: self-explanation. Moreover, a model for learning from examples suggests that self-explanation quality mediates effects of examples on learning outcomes (Atkinson et al. in Rev Educ Res 70:181-214, 2000). We investigated…
Descriptors: Statistics, Statistics Education, Problem Solving, Executive Function
Pooja G. Sidney; Julie F. Shirah; Lauren Zahrn; Clarissa A. Thompson – Grantee Submission, 2022
In mathematics, learners often spontaneously draw on prior knowledge when learning new ideas. In this study, we examined whether the specific diagrams used to represent more familiar (i.e., whole number division) and less familiar ideas (i.e., fraction division) shape successful transfer. Undergraduates (N = 177) were randomly assigned to…
Descriptors: Mathematics Education, Prior Learning, Transfer of Training, Visual Aids
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