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Xiaoxiao Liu; Okan Bulut; Ying Cui; Yizhu Gao – Journal of Computer Assisted Learning, 2025
Background: Process data captured by computer-based assessments provide valuable insight into respondents' cognitive processes during problem-solving tasks. Although previous studies have utilized process data to analyse behavioural patterns or strategies in problem-solving tasks, the connection between latent cognitive states and their…
Descriptors: Adults, Problem Solving, Markov Processes, Network Analysis
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Yu, Yuhua; Oh, Yongtaek; Kounios, John; Beeman, Mark – Creativity Research Journal, 2023
To solve a new problem, people spontaneously engage multiple cognitive processes. Previous work has identified a diverse set of oscillatory components critical at different stages of creative problem solving. In this project, we use hidden state modeling to untangle the roles of oscillation processes over time as people solve puzzles. Building on…
Descriptors: Creativity, Creative Thinking, Problem Solving, Cognitive Processes
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Xiao, Yue; He, Qiwei; Veldkamp, Bernard; Liu, Hongyun – Journal of Computer Assisted Learning, 2021
The response process of problem-solving items contains rich information about respondents' behaviours and cognitive process in the digital tasks, while the information extraction is a big challenge. The aim of the study is to use a data-driven approach to explore the latent states and state transitions underlying problem-solving process to reflect…
Descriptors: Problem Solving, Competence, Markov Processes, Test Wiseness
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Lee, Hee Seung; Betts, Shawn; Anderson, John R. – Cognitive Science, 2016
Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem…
Descriptors: Problem Solving, Hypothesis Testing, Experiments, Cognitive Processes
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Anderson, John R.; Fincham, Jon M. – Cognitive Science, 2014
Multi-voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. We apply this method to a task where participants solve novel…
Descriptors: Cognitive Structures, Pattern Recognition, Markov Processes, Cognitive Processes
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Mercier, Julien – World Journal of Education, 2012
A cognitive model of how teachers plan instruction was validated in laboratory settings but remained to be tested empirically in authentic situations. The objective of this work is to describe and compare pedagogical reasoning in laboratory and authentic contexts and across expertise levels. The "state-driven hypothesis" and the…
Descriptors: Planning, Lesson Plans, Laboratories, Expertise