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Issue title: Special Section: Applied Machine Learning and Management of Volatility, Uncertainty, Complexity & Ambiguity (V.U.C.A)
Guest editors: Srikanta Patnaik
Article type: Research Article
Affiliations: Lingnan Normal University, Zhanjian, Guangdong, China
Correspondence: [*] Corresponding author. Yu Quan, Lingnan Normal University, Zhanjian, Guangdong 524048, China. E-mail: [email protected].
Abstract: With the continuous development of science and technology, computer-aided teaching has become a common mode of school teaching. From the current situation, it can be seen that the current computer-aided teaching mostly replaces the traditional teaching mode with multimedia, and does not play the role of functional teaching, and teachers cannot effectively grasp the students’ psychological thoughts in teaching. Based on this, this study combines machine learning prediction and artificial intelligence KNN algorithm to actual teaching. Moreover, this study collects video and instructional images for student feature behavior recognition, and distinguishes individual features from group feature recognition, and can detect student expression recognition in detail. In addition, this study designed a case study to analyze the performance of the algorithm. From the experimental results, it can be seen that the proposed algorithm has certain effects and can be used as an algorithm to assist the teaching process and can provide theoretical reference for subsequent related research.
Keywords: Machine learning prediction, artificial intelligence, KNN algorithm, auxiliary teaching, feature recognition
DOI: 10.3233/JIFS-179959
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 1879-1890, 2020
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