THE EFFECTIVENESS OF USING NEURAL NETWORKS AND MACHINE LEARNING IN TEACHING PHYSICS - Article Overview

Aug 4, 2026

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Neural networks and machine learning (ML) technologies are applied to physics education to strengthen teaching methods and improve student learning outcomes. The article examines how adaptive learning environments, automated assessment and feedback, and ML-driven simulations support personalized learning pathways. It analyzes how data-driven insights can raise student engagement, optimize instructional strategies, and streamline grading by delivering real-time guidance based on student interactions and performance patterns.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Introduction

Challenges in physics education

Aim of the study

Applications of Neural Networks and Machine Learning in Physics Education

Personalized learning environments

Interactive simulations and visualizations

Automated assessments and feedback systems

УДК531/534:37.016; 53:37.016 THE EFFECTIVENESS OF USING NEURAL NETWORKS AND MACHINE LEARNING IN TEACHING PHYSICS Ali Askar, a graduate student, MukhambetzhanA.M., candidate of physical and mathematical sciences KorkytAta Kyzylorda University, Kazakhstan Neural networks and machine learning (ML) technologies have seen rapid advancement across various fields, including education. In physics education, these technologies enhance both teaching methods and student learning outcomes through personalized learning, automated feedback, simulations, and data-driven insights. This article explores the applications and benefits of neural networks and ML in teaching physics, addressing their ability to create adaptive learning environments, improve student engagement, and optimize teaching strategies through real-time data analysis. Keywords: Neural networks, Machine learning, Physics education, Student engagement, Teaching strategies. 1. Introduction Physics education presents distinct challenges due to the subject's abstract conceptual nature and its reliance on mathematical rigor. Conventional teaching methodologies, while foundational, often fall short in addressing the diverse learning needs and cognitive engagement required by students. The integration of neural networks and machine learning (ML) technologies offers promising avenues to address these educational challenges by providing tools that can personalize learning, enhance assessment accuracy, and deliver real-time feedback. Neural networks and ML systems, which emulate human intelligence through pattern recognition and data analysis, can dynamically adapt to individual student needs, making physics content more accessible and engaging. This article aims to explore the impact of neural networks and ML in advancing both teaching methodologies and learning outcomes within the context of physics education. Through adaptive learning, these technologies not only enhance student comprehension of complex topics but also streamline grading processes and reduce instructional burdens. The paper investigates the extent to which neural networks and ML systems can revolutionize instructional practices and elevate student performance, highlighting the transformative potential of these tools in fostering more effective, individualized, and data-driven educational experiences. 2. Applications of Neural Networks and Machine Learning in Physics Education 2.1 Personalized Learning Environments The deployment of neural networks in physics education is revolutionizing the ways educators deliver personalized learning experiences. Through advanced algorithms that analyze student interactions, performance metrics, and response patterns, neural networks can craft customized educational pathways for each learner. This process typically involves supervised learning models like decision trees and clustering algorithms that segment students based on factors such as prior knowledge, learning speed, and cognitive strengths or weaknesses [2, 29] . These models allow the learning platform to assign individualized recommendations and additional resources for topics where the student exhibits lower proficiency. Adaptive learning platforms, powered by neural networks, can thus create a more student-centered educational experience by adjusting the complexity of content in real-time. For instance, these platforms dynamically generate physics problem sets tailored to each student’s current understanding and pace, ensuring optimal challenge and minimizing frustration. By continually analyzing learner performance, neural networks refine the learning trajectory, making education more efficient and engaging. 2.2 Interactive Simulations and Visualizations Machine learning has significantly enhanced the development of physics simulationsand visualizations, creating immersive learning tools that bridge theoretical concepts and practical understanding. Real-time simulations of complex phys

FAQs

How do neural networks support personalized learning in physics education?

Neural networks analyze student interactions and performance to create customized learning pathways. They can recommend resources for topics where students show lower proficiency and adjust content complexity in real time.

What role does machine learning play in physics simulations and visualizations?

ML enables real-time simulations of complex physical phenomena, allowing learners to interact with and vary parameters. It also supports predictive behavior in simulation design to guide productive exploration and reduce misunderstandings.

How can machine learning automate assessments and provide feedback for physics students?

Natural language processing and neural models can assess open-ended responses and conceptual explanations. They generate immediate, constructive feedback by highlighting errors and offering hints, while also identifying common misconceptions for targeted curriculum improvements.

Aug 4, 2026

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