期刊目錄列表 - 65卷(2020) - 【教育科學研究期刊】65(3)九月刊(本期專題:東南亞國家相關之教育研究)
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學生認知歷程與背景變數對於學生評鑑教師的影響:潛在類別偏差校正與混合迴歸分析
作者:國立東華大學師資培育中心曾明基

卷期:65卷第3期
日期:2020年9月
頁碼:251-276
DOI:10.6209/JORIES.202009_65(3).0009

摘要:
在潛在類別模型中加入共變項時,如果沒有經過偏差校正,共變項與潛在類別之間的估計參數將產生偏誤。基於此,本研究在探討學生層次變項對學生評鑑教師教學影響時,除了加入與學生學習有關的認知歷程變項外,並進行潛在類別偏差校正與混合迴歸分析。研究對象為東部某大學大學部學生,總樣本數為6,111人。研究發現,學生學習的認知歷程改變可以分為五個潛在類別群組,當學生在該科目所保留或遷移的認知歷程最多時,給教師的分數最高。此外,當學生認知歷程存在潛在異質差異時,在學生層次不同背景變項上對學生評鑑教師教學的影響不同。針對上述結果,本研究對學生評鑑教師教學的議題發展及模型建構提出相關建議。

關鍵詞:學生評鑑教師教學、認知歷程、偏差校正三步驟混合迴歸

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參考文獻:
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中文APA引文格式曾明基(2020)。學生認知歷程與背景變數對於學生評鑑教師的影響:潛在類別偏差校正與混合迴歸分析。教育科學研究期刊,65(3),251-276。doi:10.6209/JORIES.202009_65(3).0009
APA FormatTseng, M.-C. (2020). Influences of Different Background Variables on Student Ratings of Instruction: Bias-Adjusted Three-Step Mixture Regression Analysis. Journal of Research in Education Sciences, 65(3), 251-276. doi:10.6209/JORIES.202009_65(3).0009

Journal directory listing - Volume 65(2020) - Journal of Research in Education Sciences【65(3)】September (Special Issue: Educational Research on Southeast Asian countries)
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Influences of Different Background Variables on Student Ratings of Instruction: Bias-Adjusted Three-Step Mixture Regression Analysis
Author: Ming-Chi Tseng (Center for Teacher Education, National Dong Hwa University)

Vol.&No.:Vol. 65, No.3
Date:September 2020
Pages:251-276
DOI:10.6209/JORIES.202009_65(3).0009

Abstract:
This study used a bias-adjusted three-step mixture regression model to evaluate the influences of students’ cognitive process on their ratings of instruction. Data were collected from 6,111 students enrolled at a university in Taiwan. The results indicated that students’ gender, year in the university, course, department, and learning interest had a significant impact on student ratings, and students’ cognitive process demonstrated a moderating effect. Furthermore, the implications of these findings for student ratings policies and theirs effects on university faculty and students are discussed.

Keywords:student ratings of instruction, cognitive process, bias-adjusted three-step mixture regression