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Application of Challenging Learning Based on Human-Computer Interaction under Machine Vision in Vocational
Bin Hu1,2, Xueqiong Hong2,3
1Nanchang Business College of Jiangxi Agricultural University, Gongqingcheng 332020, Jiangxi, China.
Computational Intelligence and Neuroscience
|October 21, 2022
Summary
This study integrated challenging learning with Human-Computer Interaction (HCI) in vocational colleges. The Gaussian mixture model algorithm recognized student actions, showing improved learning outcomes and positive user experience.
Area of Science:
- Educational Technology
- Human-Computer Interaction
- Artificial Intelligence in Education
Background:
- Advancements in science and technology necessitate innovative educational approaches.
- Human-Computer Interaction (HCI) offers new paradigms for learning.
- Challenging learning methods can enhance student engagement and autonomous learning.
Purpose of the Study:
- To apply challenging learning combined with HCI in vocational undergraduate colleges.
- To utilize the Gaussian mixture model (GMM) algorithm for recognizing student actions.
- To analyze the effectiveness of this integrated approach across different student demographics.
Main Methods:
- Implemented a 15-week challenging learning program for 200 vocational students.
- Employed the Gaussian mixture model (GMM) algorithm for face and gesture recognition.
- Collected data on task completion rates, scores, GMM accuracy, and HCI usage/experience.
Main Results:
- Higher completion rates and scores observed in senior students and liberal arts majors.
- GMM algorithm achieved high accuracy (90% for face, 87% for gestures).
- Students reported frequent HCI use (320 times/day) and a positive experience (80/100 score).
Conclusions:
- The HCI-integrated challenging learning strategy is effective in vocational colleges.
- The approach yielded satisfactory learning results and positive student engagement.
- GMM algorithm shows promise for action recognition in educational HCI applications.
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