Version 1
: Received: 19 August 2024 / Approved: 19 August 2024 / Online: 19 August 2024 (10:42:50 CEST)
How to cite:
Tapia, S.; Araya, R. From Play to Understanding: LLMs in Logic and Spatial Reasoning Coloring Activities for Children. Preprints2024, 2024081321. https://doi.org/10.20944/preprints202408.1321.v1
Tapia, S.; Araya, R. From Play to Understanding: LLMs in Logic and Spatial Reasoning Coloring Activities for Children. Preprints 2024, 2024081321. https://doi.org/10.20944/preprints202408.1321.v1
Tapia, S.; Araya, R. From Play to Understanding: LLMs in Logic and Spatial Reasoning Coloring Activities for Children. Preprints2024, 2024081321. https://doi.org/10.20944/preprints202408.1321.v1
APA Style
Tapia, S., & Araya, R. (2024). From Play to Understanding: LLMs in Logic and Spatial Reasoning Coloring Activities for Children. Preprints. https://doi.org/10.20944/preprints202408.1321.v1
Chicago/Turabian Style
Tapia, S. and Roberto Araya. 2024 "From Play to Understanding: LLMs in Logic and Spatial Reasoning Coloring Activities for Children" Preprints. https://doi.org/10.20944/preprints202408.1321.v1
Abstract
Visual thinking leverages spatial mechanisms in animals for navigation and reasoning. Therefore, given the challenge of abstract mathematics and logic, spatial reasoning-based teaching strategies can be highly effective. Our previous research verified that innovative box and ball coloring activities help teach elementary school students complex notions like quantifiers, logical connectors, and dynamic systems. However, given the richness of the activities, correction is slow, error-prone, and demands high attention and cognitive load from the teacher. Moreover, feedback to the teacher should be immediate. Thus, we propose to provide the teacher with real-time help with LLMs. We explored various prompting techniques with and without context Zero shot, Few shot, Chain of Thought, Visualization of Thought, Self Consistency, logicLM and Emotional to test GPT-4o’s visual, logical, and correction capabilities. We obtained that Visualization of Thought and Self Consistency techniques enabled GPT-4o to correctly evaluate 90% of logical-spatial problems that we tested. Additionally, we propose a novel prompt combining some of these techniques that achieved 100% accuracy on a testing sample, excelling in spatial problems and enhancing logical reasoning.
Keywords
LLM; GPT; prompting; quantifiers; visual thinking; coloring activities; support for teachers; correction; feedback
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
