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Yume Menghe Xu; Brian E. Gravel – Journal of Engineering Education, 2025
Background: Many students bring rich experiences and identities related to making activities into undergraduate engineering programs. However, the dominant technocentric narratives in engineering dictate which forms of making are legitimized in engineering practices, marginalizing students whose identities do not align with the canonical norms of…
Descriptors: Engineering Education, Self Concept, Undergraduate Students, Prior Learning
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Luke K. Fryer; Alex Shum – British Educational Research Journal, 2025
How do task experiences support proximal and longer-term desires to reengage? Central to this question is the interplay between perceptions of task difficulty and the situational interest. This interplay is increasingly researched but not yet well understood. Micro-analytic, latent studies are important tools for replicating past findings and…
Descriptors: Prior Learning, Interests, Mathematics Education, Learning Readiness
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Elizabeth Boling; Victoria Abramenka-Lachheb; Dilnoza Kadirova; Merve Basdogan; Rajagopal Sankaranarayanan; Vanessa Johnson; Alyse Harris; Ahmed Lachheb – TechTrends: Linking Research and Practice to Improve Learning, 2025
As part of a larger study (Boling et al., 2024a, b), this paper reports on how instructional designers define and use precedent knowledge, including its sources, forms, and role in decision-making. Using a qualitative approach, we conducted semi-structured interviews with instructional designers from higher education and non-profit professional…
Descriptors: Prior Learning, Decision Making, Instructional Design, Program Validation
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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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Alexis Gable; Cristina Alvarez; Susan Bush-Mecenas; Jonathan Schweig – RAND Corporation, 2025
The authors of this report provide findings and recommendations of a mixed-methods evaluation of a multiyear action research project by Learning Creates Australia (LCAust). The authors aimed to answer five research questions organised around the three focal areas of LCAust's work: convening supporters, curating evidence and demonstrating…
Descriptors: Foreign Countries, Program Evaluation, Educational Change, Stakeholders
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Abdulaziz Altamimi; Kathy Conklin – TESOL Quarterly: A Journal for Teachers of English to Speakers of Other Languages and of Standard English as a Second Dialect, 2024
Little is known about the effect of pre-reading exposure on collocational learning. This study used eye-tracking and offline measures (form recall and recognition) to explore the effectiveness of pre-reading study and reading exposure on the processing and learning of novel collocations. Three learning conditions were evaluated: reading-only…
Descriptors: Prior Learning, Reading, Learning Processes, Study Habits
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Moore, Kara N.; Lampinen, James Michael; Adams, Eryn J.; Nesmith, Blake L.; Burch, Presley – Cognitive Research: Principles and Implications, 2022
We examined how prior experience encountering targets affected attention allocation and event-based prospective memory. Participants performed four color match task blocks with a difficult, but specified prospective memory task (Experiment 1) or an easier, but unspecified prospective memory task (Experiment 2). Participants were instructed to…
Descriptors: Attention Control, Memory, Prior Learning, Experience
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Jia Yang; Fang-Fang Yan; Tingting Wang; Zile Wang; Qingshang Ma; Jinmei Xiao; Xianyuan Yang; Zhong-Lin Lu; Chang-Bing Huang – npj Science of Learning, 2025
Learning to perform multiple tasks robustly is a crucial facet of human intelligence, yet its mechanisms remain elusive. Here, we formulated four hypotheses concerning task interactions and investigated them by analyzing training sequence effects through a continual learning framework. Forty-nine subjects learned seven tasks sequentially, each of…
Descriptors: Sequential Learning, Interference (Learning), Prior Learning, Perceptual Motor Learning
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Cheng-Wen He; Logan Fiorella; Paula P. Lemons – Educational Psychology Review, 2025
This study tested competing theories about the effectiveness of different instructional sequences for learners with different levels of prior knowledge. Across two classroom experiments, undergraduates learned about noncovalent interactions in biochemistry by either receiving explicit instruction before problem-solving (I-PS group) or engaging in…
Descriptors: Instructional Effectiveness, Problem Solving, Prior Learning, Learning Processes
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Xiao, Yiqi; Jiang, Chenhan – International Journal of Technology and Design Education, 2023
Social design produces strategic, systematic solutions or new cultures as a response to the complexity of social changes and, in consequence, designers who are used to the traditional way of ideation may not be well prepared for the requirements of social design. This paper identified the characteristics of the concept generation of industrial…
Descriptors: Industrial Arts, Design, Concept Formation, Prior Learning
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Tina Seufert; Verena Hamm; Andrea Vogt; Valentin Riemer – Educational Psychology Review, 2024
Self-regulated learning depends on task difficulty and on learners' resources and cognitive load, as described by an inverted U-shaped relationship in Seufert's (2018) model: for easy tasks, resources are high and load is low, so there is no need to regulate, whereas for difficult tasks, load is too high and resources are too low to regulate. Only…
Descriptors: Cognitive Processes, Difficulty Level, Resources, Self Management
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Yasuhiro Yamamoto; Yasuo Miyazaki – Journal of Experimental Education, 2025
Bayesian methods have been said to solve small sample problems in frequentist methods by reflecting prior knowledge in the prior distribution. However, there are dangers in strongly reflecting prior knowledge or situations where much prior knowledge cannot be used. In order to address the issue, in this article, we considered to apply two Bayesian…
Descriptors: Sample Size, Hierarchical Linear Modeling, Bayesian Statistics, Prior Learning
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Joyce B. Main; Christina Pantoja; Susan M. Lord; Catherine Mobley; Catherine Brawner – Journal of Engineering Education, 2025
Background: In the United States, there is increased demand to expand the engineering workforce. Many military veterans have received technical training that can be leveraged in engineering. Their pursuit of engineering degrees has great potential to expand and diversify the engineering workforce. Purpose: This study examines the pathways of US…
Descriptors: Engineering Education, Veterans, Prior Learning, High School Graduates
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Adrienne Mueller; Johannes Konert; René Röpke; Ömer Genc; Henrik Bellhäuser – Journal of Computing in Higher Education, 2025
The study investigates how the 2×2 configuration of homogeneous and heterogeneous distributions of extraversion and prior knowledge influences group outcomes, including satisfaction, performance, and stability. Based on the standard deviation of extraversion and prior knowledge, groups were established to test experimentally, what form of grouping…
Descriptors: Extraversion Introversion, Prior Learning, Online Courses, Heterogeneous Grouping
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Tulay Senel Coruhlu; Muammer Calik; Sibel Er Nas; Salih Cepni; Cevriye Ergul; Seyma Kurt Almali – Asia Pacific Education Review, 2025
This study aimed to investigate the effects of the prediction-observation-explanation (POE) strategy using worksheets designed for the "living things and life" topic on conceptual understanding and science process skills (SPS) of students with mild intellectual disabilities (SMIDs). The sample consisted of 12 SMIDs (eight students for…
Descriptors: Students with Disabilities, Intellectual Disability, Scientific Concepts, Science Education
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