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Construction of Teacher Personal Knowledge Management Model Based on Data Mining Algorithm from TPACK Perspective

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DOI: 10.23977/infkm.2024.050107 | Downloads: 10 | Views: 213

Author(s)

Min Yang 1

Affiliation(s)

1 Department of Literature and Media, Xi'an Fanyi University, Xi'an, Shaanxi, 710105, China

Corresponding Author

Min Yang

ABSTRACT

With the renewal of teaching concept and the development of educational technology, educational practice has put forward higher requirements for teachers' teaching level. The amount of knowledge has become the core indicator to measure teachers' teaching level. The research on teachers' personal knowledge management is of great significance to promote teachers' teaching level. Therefore, this paper studied the teacher PKM, constructed the teacher PKM model, proposed the teacher PKM strategy based on the perspective of Technological Pedagogical Content Knowledge (TPACK) and educational cybernetics, and the teacher's personal knowledge acquisition method based on Expectation-Maximization algorithm (EM). At the same time, this paper conducted an experimental study on teachers' personal knowledge management, and obtained the following proof through the experimental study. The average personal knowledge management evaluation score of teachers was 6.525. The average feasibility evaluation score of experts on teachers' personal knowledge management strategy was 7.65, and the average effectiveness evaluation score of experts on teachers' personal knowledge management strategy was 8.575. At this stage, teachers' personal knowledge management ability still needed to be improved, and the strategies proposed in this paper had certain practical value.

KEYWORDS

Personal Knowledge Management Model, Data Mining Algorithms, Maximum Expectation Algorithm, Educational Cybernetics, Integrate Technical Subject Teaching Knowledge

CITE THIS PAPER

Min Yang, Construction of Teacher Personal Knowledge Management Model Based on Data Mining Algorithm from TPACK Perspective. Information and Knowledge Management (2024) Vol. 5: 44-53. DOI: http://dx.doi.org/10.23977/infkm.2024.050107.

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