Investigation of Decision-Making Computational cortex-like mechanism models ‎in Neuroeducation

Mousavi Harris, A. M.; Ebrahimpour, R.

doi:10.22061/tej.2023.8786.2728

Document Type : Original Research Paper

Authors

¹ Department of Artificial Intelligence, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, ‎Iran ‎

² Institute for Convergent Science and Technology, Sharif University of Technology, Tehran, Iran‎

https://doi.org/10.22061/tej.2023.8786.2728

Abstract

Background and Objectives: Methods of pedagogy, for a long time, have been a social practice based on direct experiences from the past, and many of teaching methods have been traditionally formed. These methods were not supported by a scientific theory-based system and failed to keep up with the rapidly changing social needs. Neuroeducation is an experimental science and an interdisciplinary field that applies the latest theoretical advances in the human brain and psychology to education. By studying the theories in neuroeducation, it enables us to improve optimal presentation of contents for a course, teaching strategy and teaching methods of new subjects, and simultaneously improve students' computational thinking ability. One way that these theories can be tested is to study how decision-making is formed in the human brain. Goal-based decisions and behaviors depend on both sensory evidence mechanisms that collect perceptual information from the outside and mechanisms that select appropriate behaviors based on that sensory information which is decision-making mechanisms. Behaviorism is one of the basic foundations of theories of learning and behavior. One way to study behavior in detail is to use computational models based on brain biology that have been developed by neuroscientists in recent years. In this paper we try to explore the relationship between neuroeducation and pedagogy by studying theoretical achievements in computational neuroscience, cognitive neuroscience and psychology.
Methods: To investigate this issue, a neural-computational model of brain-based for decision making was used. This model consists of two recurrent dynamic neurons that can explore how perceptual decisions are formed in complex behavioral spaces and show the key parameters of decision-making process. In this study, we designed three different experiments in the model that included the accuracy-speed trade-off when responding, the effect of attention on decision making, and the impact of the confidence of decision, and then analyzed how the parameters and model's behavior change then we map the parameters to the classroom and changes in student’s brain. Finally, we used linear regression model to study the relationships and correlations between the parameters of the model’s behavior.
Findings: The results showed that using this decision-making computational model opened a way to study the speed-accuracy trade-off of students while answering exam questions and then, by using the model, an optimal trade-off could be found to answer the questions. Also, the analysis of model parameters showed that the level of students' attention in the classroom could be measured by the model and it had an important effect on decision making and answering the questions. Finally, the model could show the effect of students’ confidence on their performance and based on the fitted data of the model to students' behavioral data, we could make optimal suggestions from the perspective of educational psychology.
Conclusion: In this study, we show that by using decision-making neural-computational models, students' behavior in the classroom can be modeled. Educational science experts and psychologists in the field of pedagogy can use the findings to provide the best and most optimal teaching methods for teaching easily and the flourishing of students' creativity.

Keywords

20.1001.1.20080441.1402.17.2.1.2

Main Subjects

Educational Technology

COPYRIGHTS

©2023 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

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