Intelligent Teaching Methods Integrating Virtual Reality and Big Data Algorithms to Enhance Traditional Cultural Education Platforms

Summary

This study details a reproducible workflow for extracting behavioral features and deploying dynamically adjusted educational paths within virtual environments. The outlined procedures enable educators to develop adaptive virtual reality curricula and implement real-time behavioral tracking.

Abstract

Current conventional teaching environments feature limited interactive elements, resulting in limited engagement metrics among undergraduate populations studying cultural heritage. To address these issues, an intelligent teaching platform was developed, integrating a high-fidelity spatial rendering engine into virtual reality environments with a multi-model algorithmic architecture. Within this platform, Transformer and Neural Collaborative Filtering (NCF) models process behavioral data to generate personalized learning paths, while a Deep Q-Network (DQN) dynamically adjusts content difficulty based on real-time feedback. Such adaptive scaffolding reflects constructivist learning principles, wherein knowledge is actively constructed through interaction with contextualized, responsive environments rather than passively received through static instruction. The novelty of this research lies in integrating a spatial rendering engine with a multi-model algorithmic architecture to establish a real-time behavioral adaptation mechanism. The experimental results show that the experimental group utilizing the proposed intelligent teaching platform has higher learning scores (low/medium/high groups are 69/79/93 points, respectively) and a higher learning task completion rate (93%/96%/98%) than the control group using the traditional teaching model. The design of the intelligent teaching platform offers strong interactivity, provides personalized learning paths for students with diverse individual differences, and dynamically adjusts during the learning process. Response time analysis indicates an average latency of 0.496 s, ensuring rapid feedback on interactions. Ultimately, the proposed platform demonstrates that combining immersive VR with behavior-adaptive algorithms effectively enhances both academic achievement and task engagement for undergraduate students in traditional cultural education.

Introduction

Traditional cultural education plays a vital role in the inheritance and development of culture. However, the current traditional cultural education model mainly relies on static forms such as textbooks, pictures, and videos, lacking interactivity and immersion, making it difficult for students to truly understand and experience the charm of culture. In addition, traditional teaching methods often adopt a unified curriculum, which fails to fully consider students' interests, cognitive abilities, and learning styles, resulting in low learning efficiency and difficulty in stimulating students' enthusiasm for learning. This limitation aligns with Vygotsky’s....

Protocol

The institutional ethics committee of Shaanxi University of International Trade & Commerce validated the research protocol under reference 2025-04A. Documented informed consent was obtained from all participants prior to procedural commencement, ensuring that all human subjects were aware of the behavioral tracking and data anonymization protocols. (Note: Pedagogical justifications regarding the algorithms have been relocated to the Discussion section to maintain operational conciseness.)

Step 1: Virtual reality scene construction

Asset Integration
Digital modeling of cultura....

Results

Data splits, validation, and reproducibility settings
For the algorithmic training phase, the behavioral interaction dataset, containing 5,000 logs, was split into 80:20 training and validation sets. Validation procedures utilized a 5-fold cross-validation scheme to evaluate feature extraction consistency. To ensure absolute experimental reproducibility, all network weight initializations, dataset shuffling sequences, and reinforcement learning exploration patterns were locked using a fixed random se.......

Discussion

This study contributes to educational technology by translating core pedagogical theories—namely constructivism and the Zone of Proximal Development—into a computationally implemented intelligent teaching system. The integration of VR immersion with behavior-driven personalization ensures that technological innovation serves demonstrable learning objectives rather than functioning as an end in itself. As highlighted in the methodology, the generation of personalized learning paths is central to ensuring the e.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
High-fidelity spatial rendering engine	Unreal Engine 5		Used for scene integration, dynamic ray tracing, and physics simulation.
Ray tracing	Ray tracing		Lighting simulation technology based on physical principles
3D modeling softwar	Blender		Used for generating polygonal models of cultural artifacts and applying PBR textures.
Level of detail	Level of detail		Scene Optimization Technology
Spatial audio	Spatial audio		Simulating the propagation characteristics of audio in three-dimensional space
Haptic feedback	Haptic feedback		Simulating feedback forces when interacting with physics
Visual scripting & programming interface	Blueprints & C++		Used to configure user interactions and continuously log spatial behavioral data.
Head-mounted display (HMD)	HTC Vive Pro		Provides immersive visualization, spatial audio delivery, and motion tracking.