Title: Application of MOOC mixed teaching model in college physical education teaching - taking track and field teaching as an example

Authors: Junqi Zhu

Addresses: Police Basic Skills Teaching Department, Sichuan Police College, Luzhou, 646000, China

Abstract: In order to provide students with more learning opportunities and predict their learning situation more accurately, the article applies massive open online course (MOOC) to track and field teaching using support vector machine (SVM) algorithm to predict whether MOOC students can obtain certificates, using improved PSO algorithm to optimise prediction results. The binary particle swarm optimisation (PSO)-SVM algorithm has higher classification accuracy (CA) and faster convergence speed. Its CA in the Spect Heart dataset is 86.15%, 0.97% higher than the GA-SVM algorithm. After the weighted improvement, the accuracy of the method for categorising track and field courses was 82.37%. Unlike a library for support vector machines (LIBSVM), when the training set size is 50,000 rows, its CA of track and field course (TFC) is 87.80%. The research design method can better predict the acquisition of certificates by MOOC learners, which is conducive to providing more personalised learning support.

Keywords: massive open online course; MOOC; track and field course; TFC; parallel SVM; binary PSO; Hadoop.

DOI: 10.1504/IJES.2024.143769

International Journal of Embedded Systems, 2024 Vol.17 No.1/2, pp.126 - 137

Received: 11 Oct 2023
Accepted: 02 Jun 2024
Published online: 06 Jan 2025 *

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