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Psychological Mobilization of Innovative Teaching Methods for Students' Basic Educational Curriculum Reform Under
Dingzhou Zhao1, Hongming Li2, Annan Xu3
1Physical Education College of Zhengzhou University, Zhengzhou, China.
Frontiers in Psychology
|June 27, 2022
Summary
This study introduces an innovative Science, Technology, Engineering, and Mathematics (STEM) teaching program using Distributed Deep Neural Networks (DDNN) and edge computing. Results show high accuracy and identify challenging tasks and motivation as key drivers for deep learning effectiveness.
Area of Science:
- Educational Technology
- Computer Science
Background:
- Educational innovation reform necessitates new teaching programs.
- Leverages Distributed Deep Neural Network (DDNN) and deep learning under edge computing.
Purpose of the Study:
- Propose an innovative Science, Technology, Engineering, and Mathematics (STEM) teaching program.
- Investigate the psychological factors influencing students' deep learning in STEM.
- Provide theoretical references for educational curriculum reform in China.
Main Methods:
- Utilized Distributed Deep Neural Network (DDNN) with average training for model verification.
- Employed questionnaires to assess psychological mobilization factors in STEM education.
- Analyzed the impact of challenging learning tasks and motivation on deep learning.
Main Results:
- Achieved over 95% accuracy and sample ratio with a communication volume of 309 bytes.
- Identified challenging learning tasks and learning motivation as having the greatest positive impact on deep learning.
- Demonstrated the potential for STEM innovative teaching programs within educational reform.
Conclusions:
- STEM innovative teaching programs are widely applicable and beneficial.
- Challenging tasks and motivation are crucial for enhancing deep learning in STEM.
- The proposed framework offers a valuable reference for improving teaching innovation.
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