An Evaluation Model of English Normal Students’ Informatization Teaching Ability Based on Technical Pedagogical Content Knowledge and Few-Shot Learning

Information-based instruction is the most important and recently altered sort of instruction. Numerous changes in educational techniques have emerged from the ongoing growth of science and technology. Teachers must also grasp and enhance their capacity to educate with information on a regular basis....

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Bibliographic Details
Published inComputational intelligence and neuroscience Vol. 2022; pp. 1 - 13
Main Authors Zeng, Honghong, Tan, Ronghua
Format Journal Article
LanguageEnglish
Published New York Hindawi 05.07.2022
John Wiley & Sons, Inc
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ISSN1687-5265
1687-5273
1687-5273
DOI10.1155/2022/8591303

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Summary:Information-based instruction is the most important and recently altered sort of instruction. Numerous changes in educational techniques have emerged from the ongoing growth of science and technology. Teachers must also grasp and enhance their capacity to educate with information on a regular basis. The goal of this research is to look at how to use the TPACK model and computational intelligence to build and explain a model for evaluating English normal students’ informatization teaching skills. To measure information-based teaching competence, this research recommends using the Technical Pedagogical Content Knowledge model and Few-Shot Learning technology. As a result, this paper covers the principles and related algorithms of both, as well as constructing and assessing the case design and analysis of the information-based teaching ability evaluation model. According to the experimental data, the average CK of normal students’ subject material knowledge is 3.522, which is the highest among the seven dimensions. The subject content is well-understood by ordinary students. When compared with teaching abilities, the competence level is low, and the performance of the integration of technology and teaching content needs to be improved.
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Academic Editor: Xin Ning
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/8591303