The analysis of artificial intelligence-based mobile learning in students' open teaching recommendation system based...

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Learning Analytics

Background:

• Mobile learning and open teaching modes are increasingly prevalent in student education.
• Optimizing student time-use efficiency and learning effects within these modes is crucial.
• Current challenges exist in effectively integrating technology to support diverse learning environments.

Purpose of the Study:

• To analyze the current state of mobile learning and open teaching.
• To propose and design an Artificial Intelligence (AI) Deep Learning (DL) recommendation system for mobile learning.
• To evaluate the effectiveness and student acceptance of the proposed AI DL recommendation system.

Main Methods:

• Analysis of mobile learning and open teaching modes.
• Development of an AI Deep Learning (DL) recommendation system model.
• Investigation and survey of student usage patterns and acceptance of AI mobile learning and the DL recommendation system.

Main Results:

• Students demonstrate high frequency of mobile learning, with approximately 85% of middle school students using AI mobile platforms weekly.
• The AI DL recommendation system showed high recommendation and acceptance rates during daily learning.
• Student satisfaction with the DL recommendation system was high (75-83%), significantly outperforming Decision Tree (DT) systems (50-70%).

Conclusions:

• The AI DL recommendation system effectively improves mobile learning engagement and student satisfaction.
• This technology offers a valuable tool for enhancing open teaching modes and mobile learning experiences.
• The findings support the integration of AI-driven recommendation systems in educational technology.