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modeling Chunking Effects on learning and Performance using the Computational-unified learning model (C-ULM): A Multiagent Cognitive Process model 15

Modeling Chunking Effects on Learning and Performance using ...

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15th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Shell, Duane F. Soh, Leen-Kiat Chiriacescu, Vlad Univ Nebraska Dept Educ Psychol Lincoln NE 68588 USA Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA Simudyne London England

ISBN: (纸本)9781509038466

Chunking has emerged as a basic property of human cognition. Computationally, chunking has been proposed as a process for compressing information also has been identified in neural processes in the brain and used in models of these processes. Our purpose in this paper is to expand understanding of how chunking impacts both learning and performance using the Computational-unified learning model (C-ULM) a multi-agent computational model. Chunks in C-ULM long-term memory result from the updating of concept connection weights via statistical learning. Concept connection weight values move toward the accurate weight value needed for a task and a confusion interval reflecting certainty in the weight value is shortened each time a concept is attended in working memory and each time a task is solved, and the confusion interval is lengthened when a chunk is not retrieved over a number of cycles and each time a task solution attempt fails. The dynamic tension between these updating mechanisms allows chunks to come to represent the history of relative frequency of co-occurrence for the concept connections present in the environment;thereby encoding the statistical regularities in the environment in the long-term memory chunk network. In this paper, the computational formulation of chunking in the C-ULM is described, followed by results of simulation studies examining impacts of chunking versus no chunking on agent learning and agent effectiveness. Then, conclusions and implications of the work both for understanding human learning and for applications within cognitive informatics, artificial intelligence, and cognitive computing are discussed.

关键词： Chunking Statistical learning Cognitive process models Cognitive machine learning unified learning model Cognitive processes of the brain Cognitive Computing Multiagent Systems

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modeling Self-Efficacy as a Dynamic Cognitive Process with the Computational-unified learning model (C-ULM): Implications for Cognitive Informatics and Cognitive Computing

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2015年第3期9卷 1-24页

作者： Shell, Duane F. Soh, Leen-Kiat Chiriacescu, Vlad Univ Nebraska Dept Educ Psychol Lincoln NE 68588 USA Univ Nebraska Lincoln Dept Comp Sci & Engn Lincoln NE 68588 USA I&I Internet Dev Bucharest Romania

Self-efficacy is a person's subjective confidence in their capability of effectively executing behaviors and actions including problem solving. Research has shown it to be one of the most powerful motivators of human action and strongest predictors of performance across a variety of domains. This paper reports on the computational modeling of self-efficacy based on principles derived from the unified learning model (ULM) as instantiated in the multi-agent Computational ULM (C-ULM). The C-ULM simulation is unique in tying self-efficacy directly to the evolution of knowledge itself and in dynamically updating self-efficacy at each step during learning and task attempts. Self-efficacy beliefs have been associated with neural and brain level cognitive processes. Because C-ULM models statistical learning consistent with neural plasticity, the C-ULM simulation provides a model of self-efficacy that is more compatible with neural and brain level instantiation. Results from simulations of self-efficacy evolution due to teaching and learning, task feedback, and knowledge decay are presented. Implications for research into human motivation and learning, cognitive informatics, and cognitive computing are discussed.

关键词： Cognitive Computing Cognitive Informatics Cognitive modeling Computational Psychology Motivation Multiagent Systems Self-Efficacy unified learning model

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modeling Self-Efficacy using the Computational-unified learning model (C-ULM): Implications for Computational Psychology and Cognitive Computing 13

Modeling Self-Efficacy using the Computational-Unified Learn...

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13th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Shell, Duane F. Soh, Leen-Kiat Chiriacescu, Vlad Univ Nebraska Dept Educ Psychol Lincoln NE 68588 USA Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

ISBN: (纸本)9781479960811

Self-efficacy is defined as a person's subjective confidence in their capability of executing an action and has been shown to be one of the most powerful motivators of human action predicting performance across a variety of domains. Self-efficacy has been associated with brain level neural processes and efficacy-like confidence mechanisms are incorporated into decision making in many cognitive informatics and cognitive computing models. Current computational implementations, however, do not directly model self-efficacy at either the theoretical or neural level. This paper reports on the computational modeling of self-efficacy based on principles derived from the unified learning model (ULM) as instantiated in the multi-agent Computational ULM (C-ULM). Description of the modeling of self-efficacy within the C-ULM is provided. Results from simulations of self-efficacy evolution due to teaching and learning, task feedback, and knowledge decay are presented. The C-ULM simulation is unique in tying self-efficacy directly to the evolution of knowledge itself, consistent with recent neurological findings, and in dynamically updating self-efficacy at each step during learning and task attempts. Implications for research into human motivation and learning and for cognitive computing are discussed.

关键词： Cognitive modeling unified learning model Computational psychology Cognitive computing Motivation Self-Efficacy Multi-agent systems

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e-Homework and Motivation for Students' Success in First-Semester General Chemistry

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JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY 2023年第3期32卷 433-443页

作者： Ford, Robyn Broadway, Susan Mason, Diana Univ North Texas Dept Chem Denton TX 76203 USA

Contributed data reflective of students' compliance in doing electronic homework (e-HW) exercises when asked to choose when required e-HW assignments are due are evaluated using a quasi-experimental format with non-equivalent groups to determine if first-semester general chemistry (Chem I) students (N = 273) will academically benefit from improved e-HW completion rates. The three groups studied are categorized under their VOICES (Voluntary Options in Chemical Education Schedules). Evaluated for each VOICES group are (1) results of students' procedural arithmetic ability without the use of a calculating device as determined by their scores on the MUST (Math-Up Skills Test) and (2) outcomes of e-HW due-date compliance and achievement compared to final course averages. Students who entered with below average MUST scores were the least successful course completers (increased percentage of D/F grades) as compared to those in the average or above average MUST score ranges. Students who followed the instructor's predetermined schedule (VOICES 1) outperformed the other groups of students who chose due dates based on personal preferences. A higher percentage of VOICES 1 achieved e-HW mastery level of 89.5% than VOICES 2 (individual choice of e-HW due dates) and VOICES 3 (e-HW due at end of semester). Students who selected their own e-HW due dates entered with less automaticity ability and completed the course with lower averages, but without statistical difference. Students regardless of the VOICES group who completed at least 69.5% of their e-HW showed greater course success (i.e., grades of A, B, or C) than those who did not.

关键词： Electronic homework Chemistry Higher education Assessment Automaticity Choice theory Motivation unified learning model

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UNDERSTANDING HUMAN learning USING A MULTI-AGENT SIMULATION OF THE unified learning model

UNDERSTANDING HUMAN LEARNING USING A MULTI-AGENT SIMULATION ...

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12th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI CC)

作者： Chiriacescu, Vlad Soh, Leen-Kiat Shell, Duane F. Univ Nebraska Lincoln Dept Comp Sci & Engn Lincoln NE 68588 USA Univ Nebraska Lincoln Dept Educ Psychol Lincoln NE 68588 USA

ISBN: (纸本)9781479907816

Within cognitive science. computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the unified learning model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning and intelligent agents are presented.

关键词： Cognitive modeling unified learning model Human learning Computational simulation

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