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Slavit, David; Lesseig, Kristin; Simpson, Amber – Journal of Pedagogical Research, 2022
The goal of this paper is to share an analytic framework for understanding Students? Ways of Thinking (SWoT) in STEM-rich learning environments. Before revealing our refined coding framework, we detail the nature of our collaborations and the various analytic decisions that led to its formation. These collaborations supported our collective…
Descriptors: Thinking Skills, STEM Education, Cognitive Processes, Models
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Kwaku Adu-Gyamfi; Kayla Chandler; Anthony Thompson – School Science and Mathematics, 2025
The challenge posed by algebra story problems creates a significant hurdle for many students, transcending both the mathematical content of the problem and the specific instructional background received. This study offers a distinctive contribution to the existing literature by focusing on the cognitive conditions essential for comprehension in…
Descriptors: Algebra, Mathematics Instruction, Barriers, Cognitive Processes
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Kaldaras, Leonora; Wieman, Carl – International Journal of STEM Education, 2023
Background: Blended mathematical sensemaking in science ("Math-Sci sensemaking") involves deep conceptual understanding of quantitative relationships describing scientific phenomena and has been studied in various disciplines. However, no unified characterization of blended Math-Sci sensemaking exists. Results: We developed a theoretical…
Descriptors: Cognitive Processes, Models, Equations (Mathematics), Science Instruction
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Luo, Zhenzhen; Zheng, Chaoyu; Gong, Jun; Chen, Shaolong; Luo, Yong; Yi, Yugen – Education and Information Technologies, 2023
Learning interest affects the way of learning and its process, which is an important factor that affects the learning effect. At present, students' learning interest in a teaching environment is mainly based on a traditional questionnaire or case analysis, which is not conducive for teachers to promptly access students' interest in class to…
Descriptors: Student Interests, Artificial Intelligence, Attention, Psychological Patterns
Chen Tian – ProQuest LLC, 2023
The Q-diffusion model is a cognitive process model that considers decision making as an unobservable information accumulation process. Both item and person parameters decide the trace line of the cognitive process, which further decides observed response and response time. Because the likelihood function for the Q-diffusion model is intractable,…
Descriptors: Cognitive Processes, Item Response Theory, Reaction Time, Test Wiseness
Gregory Scott Garner – ProQuest LLC, 2023
There is growing consensus that data-informed decision-making through human-centered inquiry and design process results in improved outcomes for designed artifacts. Among the latest trends is a group of tools and processes loosely assimilated under the umbrella term, "design thinking." These "designerly ways of knowing" are…
Descriptors: Feedback (Response), Models, Design, Cognitive Processes
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Duncan Gillard; Sarah Cassidy; Ben Anderson – Educational Psychology in Practice, 2025
B. F. Skinner's work in the field of verbal behaviour represented a movement of global significance. However, in today's age, even those who appreciate its profound importance in the archives of psychology accept that it did not sufficiently account for complex human language. Recent advances in psychological science have led to the emergence of a…
Descriptors: Educational Psychology, Behavior Theories, Mental Health, Models
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Westera, Matthijs; Gupta, Abhijeet; Boleda, Gemma; Padó, Sebastian – Cognitive Science, 2021
Cognitive scientists have long used distributional semantic representations of categories. The predominant approach uses distributional representations of category-denoting nouns, such as "city" for the category city. We propose a novel scheme that represents categories as prototypes over representations of names of its members, such as…
Descriptors: Classification, Models, Nouns, Cognitive Processes
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Rott, Benjamin; Specht, Birte; Knipping, Christine – ZDM: Mathematics Education, 2021
Complementary to existing "normative" models, in this paper we suggest a descriptive phase model of problem solving. Real, not ideal, problem-solving processes contain errors, detours, and cycles, and they do not follow a predetermined sequence, as is presumed in normative models. To represent and emphasize the non-linearity of empirical…
Descriptors: Mathematics Skills, Problem Solving, Models, Cognitive Processes
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Yuang Wei; Bo Jiang – IEEE Transactions on Learning Technologies, 2024
Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying…
Descriptors: Cognitive Mapping, Models, Prediction, Short Term Memory
Nika Jurov – ProQuest LLC, 2024
Speech is a complex, redundant and variable signal happening in a noisy and ever changing world. How do listeners navigate these complex auditory scenes and continuously and effortlessly understand most of the speakers around them? Studies show that listeners can quickly adapt to new situations, accents and even to distorted speech. Although prior…
Descriptors: Models, Auditory Perception, Speech Communication, Cognitive Processes
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Julius Meier; Peter Hesse; Stephan Abele; Alexander Renkl; Inga Glogger-Frey – Instructional Science: An International Journal of the Learning Sciences, 2024
Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented ("retrospective" prompts). However, it might also help to self-explain upcoming steps ("anticipatory" prompts). The effects of the prompt type may differ for learners with…
Descriptors: Problem Based Learning, Problem Solving, Prompting, Models
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Cong Xie; Shuangfei Zhang; Xinuo Qiao; Ning Hao – npj Science of Learning, 2024
This study investigated whether transcranial direct current stimulation (tDCS) targeting the inferior frontal gyrus (IFG) can alter the thinking process and neural basis of creativity. Participants' performance on the compound remote associates (CRA) task was analyzed considering the semantic features of each trial after receiving different tDCS…
Descriptors: Stimulation, Brain Hemisphere Functions, Semantics, Comparative Analysis
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Xiaodong Wei; Lei Wang; Lap-Kei Lee; Ruixue Liu – Journal of Educational Computing Research, 2025
Notwithstanding the growing advantages of incorporating Augmented Reality (AR) in science education, the pedagogical use of AR combined with Pedagogical Agents (PAs) remains underexplored. Additionally, few studies have examined the integration of Generative Artificial Intelligence (GAI) into science education to create GAI-enhanced PAs (GPAs)…
Descriptors: Artificial Intelligence, Technology Uses in Education, Models, Science Education
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Alexander Eitel; Marie-Christin Krebs; Claudia Schöne – Educational Psychology Review, 2025
Given the many opportunities for technology use in education nowadays (e.g., Large language models, explainer videos, digital quizzing), teachers should know and rely on evidence-based answers to questions about when, how, and why technology-augmented instruction helps or hinders learning. To date, finding these answers requires integrating…
Descriptors: Predictor Variables, Technology Uses in Education, Educational Technology, Computer Assisted Instruction
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