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Research On The Status Quo And Improvement Strategy Of TPACK For Junior Middle School AI Teachers

Posted on:2021-02-20Degree:MasterType:Thesis
Country:ChinaCandidate:M ZhaoFull Text:PDF
GTID:2427330623473809Subject:Education Technology
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The "New Generation Artificial Intelligence Development Plan" advocates the implementation of the National Intelligence Education Project,sets up artificial intelligence-related courses at the primary and secondary levels,and gradually promotes programming education.The construction of artificial intelligence teachers has become a key step in artificial intelligence education.Information technology teachers are the builders and performers of artificial intelligence courses.The cognitive level of pedagogical knowledge,subject content knowledge,and technical knowledge(TPACK)of the subject of artificial intelligence is the key factor that affects the effectiveness of curriculum development.Through investigation and research,it is found that there is no clear training for artificial intelligence teachers in primary and secondary schools.The theoretical guidance framework does not analyze changes in teacher TPACK development through evaluation tools.Therefore,this research focuses on investigating the status of TPACK for junior high school AI teachers,and develops a training strategy based on "design learning" to improve the TPACK ability of AI teachers.The aim is to improve the TPACK level of junior high school AI teachers and enhance the ability of AI teachers to implement artificial intelligence courses.(1)Through the research of literature,sort out the development path and definition of artificial intelligence at home and abroad,the definition and connotation of artificial intelligence education,collate the research results of localization of artificial intelligence in domestic primary and secondary schools,and establish the curriculum content and curriculum goals of artificial intelligence universal education in junior high school.The literature collates the TPACK ability dimension of information technology teachers' AI courses from the perspective of artificial intelligence disciplines.(2)Investigate the TPACK ability of IT teachers.On the basis of related theories,establish the feasibility of the ability of teachers of artificial intelligence courses based on TPACK,integrate the subject teaching knowledge of integrated technology TPACK and the teaching practice of junior middle school artificial intelligence education,and draw on the TPACK questionnaires from teachers' experimental research.The questionnaire was revised and developed,the questionnaire was distributed and collected,and the research was conducted.(3)By analyzing the current status of TPACK dimensions of the questionnaire and teachers 'learning needs,based on the determined teaching materials and teaching aids,under the guidance of "design learning" and constructivist theory,construct a training strategy based on "design learning" to improve AI teachers' TPACK.Under the three teaching environments of online training,centralized training and follow-up training,junior high school information technology teachers in J district and X school information technology teachers are used as experimental objects to implement training strategies.(4)Analysis of the practice and effect of the "design learning" training strategy based on TPACK.Through the analysis of practice records in the analysis stage,preparation stage,design stage,and evaluation stage,combined with the analysis of the front and back of the questionnaire,and the interview method,the author summarizes and analyzes the training strategies for improving the TPACK ability of AI teachers based on "design learning".It is concluded that the training strategy in the PK and PCK dimensions of teachers has a significant improvement effect over other dimensions,and the training strategy that has been carried out needs further verification and improvement.
Keywords/Search Tags:Artificial intelligence education, AI teacher, TPACK, Design-based Learning, Training strategy
PDF Full Text Request
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