Welcome to Francis Academic Press

Frontiers in Sport Research, 2023, 5(8); doi: 10.25236/FSR.2023.050807.

Construction of Intelligent Teaching Decision System for College Physical Education Based on Big Data Analysis


Wanli Deng1,2, Maria Luvimi Casihan1

Corresponding Author:
Wanli Deng

1Graduate School, Adamson University, Manila, Philippines

2Minzu Normal University of Xingyi, Xingyi, Guizhou, China


It is difficult for the normal PE (physical Education) to arouse students' interest in PE, resulting in low participation in sports activities and inability to exercise. It is essential to enhance the effect of PE in universities. To arouse students' interest in physical education, school have organized physical education classes. By using the ID3 decision tree algorithm to compare students' comprehensive abilities in traditional teaching and intelligent teaching, it can be concluded from relevant sample tests that logic, music, and interpersonal communication had the highest T-test values, which were 4.625, 4.827, and 4.708, respectively. The data results of intelligent teaching had significantly improved compared to traditional teaching. The comparison between traditional teaching and intelligent teaching showed that through intelligent teaching, students have significantly improved their intelligence in language, music, and interpersonal communication. However, there has been little progress in the three dimensions of logical intelligence, spatial intelligence, and motor intelligence.


Big Data Analysis, College Physical Education Teaching, Intelligent Teaching, Systems

Cite This Paper

Wanli Deng, Maria Luvimi Casihan. Construction of Intelligent Teaching Decision System for College Physical Education Based on Big Data Analysis. Frontiers in Sport Research (2023) Vol. 5, Issue 8: 35-40. https://doi.org/10.25236/FSR.2023.050807.


[1] Alam A. Possibilities and challenges of compounding artificial intelligence in India’s educational landscape. International Journal of Advanced Science and Technology, 2020, 29(5): 5077-5094.

[2] Guan C, Mou J, Jiang Z. Artificial intelligence innovation in education: a twenty-year data-driven historical analysis. International Journal of Innovation Studies, 2020, 4(4): 134-147.

[3] Quennerstedt M. Physical education and the art of teaching: Transformative learning and teaching in physical education and sports pedagogy. Sport, Education and Society, 2019, 24(6): 611-623.

[4] Lopez-Fernandez D, Gordillo A, Alarcon P P. Comparing traditional teaching and game-based learning using teacher-authored games on computer science education. IEEE Transactions on Education, 2021, 64(4): 367-373.

[5] Hurlbut A R.  Online vs. traditional learning in teacher education: a comparison of student progress. American Journal of Distance Education, 2018, 32(4): 248-266.

[6] Mousavinasab E, Zarifsanaiey N, R. Niakan Kalhori S. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 2021, 29(1): 142-163. 

[7] Ouyang F, Zheng L, Jiao P. Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 2022, 27(6): 7893-7925.

[8] Pranoto B E, Suprayogi S. A Need Analysis of ESP for Physical Education Students in Indonesia. Premise: Journal of English Education, 2020, 9(1): 94-110.

[9] Maksymchuk I, Sitovskyi A, Savchuk I. Developing pedagogical mastery of future physical education teachers in higher education institutions. Journal of Physical Education and Sport, 2018, 18(2): 810-815.

[10] Chng L S, Lund J. Assessment for learning in physical education: The what, why and how. Journal of Physical Education, Recreation & Dance, 2018, 89(8): 29-34.

[11] Sener S, Çokçaliskan A. An investigation between multiple intelligences and learning styles. Journal of Education and Training Studies, 2018, 6(2): 125-132.

[12] Prez M D M, Duque A G, Garca L F. Game-based learning: Increasing the logical-mathematical, naturalistic, and linguistic learning levels of primary school students. Journal of New Approaches in Educational Research (NAER Journal), 2018, 7(1): 31-39.

[13] Li B, Xu X. Application of artificial intelligence in basketball sport. Journal of Education, Health and Sport, 2021, 11(7): 54-67.

[14] Aartun I, Walseth K, Standal O F. Pedagogies of embodiment in physical education–a literature review. Sport, Education and Society, 2022, 27(1): 1-13.

[15] Williamson B, Bayne S, Shay S. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education, 2020, 25(4): 351-365.

[16] Sha L, Lu P, Yue Y. Interactive sports analytics: An intelligent interface for utilizing trajectories for interactive sports play retrieval and analytics. ACM Transactions on Computer-Human Interaction (TOCHI), 2018, 25(2): 1-32.

[17] Patel H H, Prajapati P. Study and analysis of decision tree based classification algorithms. International Journal of Computer Sciences and Engineering, 2018, 6(10): 74-78.

[18] Rana A, Pandey R. A review of popular decision tree algorithms in data mining. Asian Journal of Multidimensional Research, 2021, 10(10): 230-237.

[19] Mekhrinigor Y. Statistical Programs in the Teaching of Physical Education Classes. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(7): 354-356.

[20] Aggerholm K, Standal O, Barker D M. On practising in physical education: Outline for a pedagogical model. Physical Education and Sport Pedagogy, 2018, 23(2): 197-208.

[21] Sujanta Kazemanzadeh. Distributed System Integrating Virtual Reality Technology in English Teaching. Distributed Processing System (2022), Vol. 3, Issue 1: 62-70.