A Study of an IT-Assisted Higher Education Model Based on Distributed Hardware-Assisted Tracking Intervention

Area of Science:

• Information Technology
• Educational Technology
• Control Systems

Background:

• Higher education models are evolving with information technology integration.
• Existing video streaming and learning models face challenges with latency and user experience.
• Distributed hardware tracking and edge computing offer potential solutions.

Purpose of the Study:

• To propose and analyze a MEC-based dynamic adaptive video stream caching technology model.
• To validate a blended learning-based adaptive intervention model for improving student performance.
• To develop and test a novel policy iterative algorithm for optimal consistency control in multi-intelligent systems.

Main Methods:

• Developed a MEC-based dynamic adaptive video stream caching model adjusting bit rate based on broadband and cache data.
• Implemented an edge cloud collaborative architecture migrating rendering to edge servers.
• Conducted three rounds of teaching practice using a blended learning-based adaptive intervention model.
• Established a multi-intelligent system dynamic model with grouping and applied a policy iterative algorithm.

Main Results:

• The MEC model demonstrated fewer transcoding times and lower latency compared to traditional models, enhancing video viewing quality.
• The blended learning model showed significant positive impact on student motivation and learning adaptability.
• The policy iterative algorithm achieved optimal consistency control with lower latency and energy consumption than cloud rendering models.
• The proposed models are suitable for dual-teacher and safety education classroom scenarios.

Conclusions:

• The MEC-based caching model significantly improves video streaming experience in educational settings.
• Effective teaching methods positively influence student motivation, adaptability, and learning outcomes.
• Edge cloud collaboration and advanced algorithms offer efficient solutions for educational technology challenges.
• The study provides a robust framework for enhancing digital learning environments.