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題名 應用神經網路建構師培校院選才模型之研究
The Research on Constructing Teacher Education Selection Model by Applying Neural Network
作者 王如哲
林欣怡
蔡明學
張敦程
呂昀潔
貢獻者 教育與心理研究
關鍵詞 師資培育 ; 校務研究 ; 跨平臺資料整合 ; 類神經網路
teacher education ; institutional research ; multi-platform integration ; neural network
日期 2021-09
上傳時間 6-Dec-2021 16:35:33 (UTC+8)
摘要 本研究主要進行跨平臺資料整合研究,連結教育部高級中等學校學籍資料與中部某教育大學學生學習檔案資料,以類神經網路法分析師培校院選才入學管道 ; 高中學業表現對大學學習成就之預測研究。研究結果顯示,高中學習表現與入學管道能有效預測師資生學習表現。進一步分析發現,有別於以往大學生選才較注重學科表現,本研究透過模型測試指出,入學管道 ; 性別與部分非考科對於預測師資生大學學習表現有其重要效果。最後,本研究結果建構師資生學習表現重要預測因子,提供師培大學作為選才參考。
This research focuses on multi-platform integration, which connects the data of student status in junior high school from the Ministry of Education and student’s learning profile from a university of education in central Taiwan. Neural network is used to analyze the teacher education selection, students’ performance in high school and its projection of students’ performance in university. The results show that students’ performance in high school can be used to predict their performance in university effectively. Moreover, by testing models, it is noticed that in the projection of teacher program students’ performance, some subjects are more important than exam subjects. At last, this research offers the dominant factors of projecting teacher program students’ performance for universities to take into consideration when selecting future teacher program students.
關聯 教育與心理研究, 44(3), 35-68
資料類型 article
DOI https://doi.org/10.3966/102498852021094403002
dc.contributor 教育與心理研究
dc.creator (作者) 王如哲
dc.creator (作者) 林欣怡
dc.creator (作者) 蔡明學
dc.creator (作者) 張敦程
dc.creator (作者) 呂昀潔
dc.date (日期) 2021-09
dc.date.accessioned 6-Dec-2021 16:35:33 (UTC+8)-
dc.date.available 6-Dec-2021 16:35:33 (UTC+8)-
dc.date.issued (上傳時間) 6-Dec-2021 16:35:33 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/138121-
dc.description.abstract (摘要) 本研究主要進行跨平臺資料整合研究,連結教育部高級中等學校學籍資料與中部某教育大學學生學習檔案資料,以類神經網路法分析師培校院選才入學管道 ; 高中學業表現對大學學習成就之預測研究。研究結果顯示,高中學習表現與入學管道能有效預測師資生學習表現。進一步分析發現,有別於以往大學生選才較注重學科表現,本研究透過模型測試指出,入學管道 ; 性別與部分非考科對於預測師資生大學學習表現有其重要效果。最後,本研究結果建構師資生學習表現重要預測因子,提供師培大學作為選才參考。
dc.description.abstract (摘要) This research focuses on multi-platform integration, which connects the data of student status in junior high school from the Ministry of Education and student’s learning profile from a university of education in central Taiwan. Neural network is used to analyze the teacher education selection, students’ performance in high school and its projection of students’ performance in university. The results show that students’ performance in high school can be used to predict their performance in university effectively. Moreover, by testing models, it is noticed that in the projection of teacher program students’ performance, some subjects are more important than exam subjects. At last, this research offers the dominant factors of projecting teacher program students’ performance for universities to take into consideration when selecting future teacher program students.
dc.format.extent 5390354 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) 教育與心理研究, 44(3), 35-68
dc.subject (關鍵詞) 師資培育 ; 校務研究 ; 跨平臺資料整合 ; 類神經網路
dc.subject (關鍵詞) teacher education ; institutional research ; multi-platform integration ; neural network
dc.title (題名) 應用神經網路建構師培校院選才模型之研究
dc.title (題名) The Research on Constructing Teacher Education Selection Model by Applying Neural Network
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.3966/102498852021094403002
dc.doi.uri (DOI) https://doi.org/10.3966/102498852021094403002