Badminton Teaching Mode in Network Teaching Platform Under Multimedia Environment

• Published in: International journal of web-based learning and teaching technologies Vol. 18; no. 2; pp. 1 - 18
• Main Author: Dou, Yanli
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 2023
• Subjects: Algorithms; Athletics; Aural Learning; Badminton (Game); Candidates; College Students; Colleges & universities; Credibility; Education; Education Courses; Educational Change; Educational Facilities Improvement; Educational Quality; Educational Technology; Educational Trends; Electronic Learning; Exercise; Familiarity; Feedback; Foreign Countries; Friendship; Golf; Guidance Programs; Handheld Devices; Higher Education; Influence of Technology; Instructional Improvement; Methods; Multimedia; Networks; Obesity; Online Courses; Periodicals; Physical Education; Physical Fitness; Physical Health; Racquet Sports; Social behavior; Social Networks; Student Behavior; Student Interests; Student Participation; Study and teaching; Teacher Effectiveness; Teaching; Technology; Telecommunications; Trust (Psychology); China
• ISSN: 1548-1093; 1548-1107; 1548-1107
• DOI: 10.4018/IJWLTT.319967

This study analyzes the necessity of introducing multimedia technology into college badminton teaching and puts forward some countermeasures and suggestions for introducing multimedia technology into college badminton teaching. Taking badminton as an example, this paper studies the design and implementation of mobile teaching platform under O2O physical education teaching mode. Firstly, a learner trust model is established by using the idea of graph model, which considers three aspects: familiarity trust, social credibility, and deep circle of friends similarity. The trust source generates a candidate set of golfers based on the trust of learners. Then, based on SVD++, a learner interest model with implicit information feedback is constructed, and learners' learning behavior and social behavior are selected as implicit feedback to generate interest candidates for learners. Finally, the trust and interest models are sorted comprehensively to generate golf recommendations for learners.