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）
Directory
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
《詳全文》
參考文獻：
- 江滿堂、龔儀（2013）。2012年屏東教育大學課程與教學滿意度之研究。教育行政論壇，5（2），1-24。 【Chiang, M.-T., & Kung, Y. (2013). The study of 2012 curriculum and teaching satisfaction in National Pingtung University of Education. Forum of Educational Administration, 5(2), 1-24.】
- 林珊如（1999）。大學生評鑑教學量表：編製及效度考驗。教育與心理研究，22，295-322。【Lin, S.-R. (1999). Student evaluation of effective university teaching in Taiwan: Scale development and factor structure. Journal of Education & Psychology, 22, 295-322.】
- 張德勝（2002）。學生評鑑教師教學理論、實務與態度。臺北市：揚智。 【Chang, T.-S. (2002). Students’ assessment of teacher teaching: Theory, practice and attitude. Taipei, Taiwan: Yang Zhi.】
- 曹嘉秀、魏孟雪（2003）。影響學生評鑑教學之背景因素探討。測驗學刊，50（1），143-158。doi:10.7108/PT.200306.0143 【Tsao, C.-H., & Wai, M.-S. (2003). Non-teaching factors that affect student ratings of teacher performance. Psychological Testing, 50(1), 143-158. doi:10.7108/PT.200306.0143】
- 陳俊瑋、秦夢群（2015）。學生家庭作業時間和家庭作業頻率與學習成就的關係：以TIMSS 2007臺灣學生為例。教育與心理研究，38（4），1-33。doi:10.3966/102498852015123804001 【Chen, C.-W., & Chin, J. M.-C. (2015). The relationships of homework time and homework frequency with learning achievements: An example of Taiwanese students from TIMSS 2007. Journal of Education & Psychology, 38(4), 1-33. doi:10.3966/102498852015123804001】
» 展開更多
- 江滿堂、龔儀（2013）。2012年屏東教育大學課程與教學滿意度之研究。教育行政論壇，5（2），1-24。 【Chiang, M.-T., & Kung, Y. (2013). The study of 2012 curriculum and teaching satisfaction in National Pingtung University of Education. Forum of Educational Administration, 5(2), 1-24.】
- 林珊如（1999）。大學生評鑑教學量表：編製及效度考驗。教育與心理研究，22，295-322。【Lin, S.-R. (1999). Student evaluation of effective university teaching in Taiwan: Scale development and factor structure. Journal of Education & Psychology, 22, 295-322.】
- 張德勝（2002）。學生評鑑教師教學理論、實務與態度。臺北市：揚智。 【Chang, T.-S. (2002). Students’ assessment of teacher teaching: Theory, practice and attitude. Taipei, Taiwan: Yang Zhi.】
- 曹嘉秀、魏孟雪（2003）。影響學生評鑑教學之背景因素探討。測驗學刊，50（1），143-158。doi:10.7108/PT.200306.0143 【Tsao, C.-H., & Wai, M.-S. (2003). Non-teaching factors that affect student ratings of teacher performance. Psychological Testing, 50(1), 143-158. doi:10.7108/PT.200306.0143】
- 陳俊瑋、秦夢群（2015）。學生家庭作業時間和家庭作業頻率與學習成就的關係：以TIMSS 2007臺灣學生為例。教育與心理研究，38（4），1-33。doi:10.3966/102498852015123804001 【Chen, C.-W., & Chin, J. M.-C. (2015). The relationships of homework time and homework frequency with learning achievements: An example of Taiwanese students from TIMSS 2007. Journal of Education & Psychology, 38(4), 1-33. doi:10.3966/102498852015123804001】
- 曾明基（2019）。縱貫性網路使用行為對學業成就的影響：潛藏轉移模型分析取向。教育科學研究期刊，64（4），31-59。doi:10.6209/JORIES.201912_64(4).0002 【Tseng, M.-C. (2019). The effect of the internet use behavior on academic achievement: Latent transition model analysis. Journal of Research in Education Sciences, 64(4), 31-59. doi:10.6209/JORIES.201912_64(4).0002】
- 曾明基、邱于真、張德勝、羅寶鳳（2011）。學生認知歷程對學生評鑑教師教學的影響：階層線性模式分析。課程與教學，14（3），157-180。doi:10.6384/CIQ.201107.0158 【Tseng, M.-C., Qiu, Y.-Z., Chang, T.-S., & Lo, P.-F. (2011). HLM analysis of the effects of cognitive process on student ratings of instruction. Curriculum & Instruction Quarterly, 14(3), 157-180. doi:10.6384/CIQ.201107.0158】
- 曾明基、邱皓政、張德勝、羅寶鳳（2013）。以學生評鑑教師教學量表決定教師開課或去留可行嗎？混合IRT分析取向。教育科學研究期刊，58（1），91-116。doi:10.3966/2073753X 2013035801004 【Tseng, M.-C., Chiou, H.-J., Chang, T.-S., & Lo, P.-F. (2013). Is using a SRI to determine the fate of teachers or commencement of work suitable? A mixture IRT analysis. Journal of Research in Education Sciences, 58(1), 91-116. doi:10.3966/2073753X2013035801004】
- 黃毅志、巫有鎰（2003）。影響教學評鑑得分因素之探討─以台東師院為例。台東師院學報，14（上），347-370。 【Hwang, Y.-J., & Wu, Y.-I. (2003). The influence factors of teaching evaluation scores － Taking National Taitung Teachers College for an example. Journal of National Taitung Teachers College, 14(I), 347-370.】
- 黃瓊蓉（2004）。使用階層線性模式分析學生評量教學績效之資料。測驗學刊，51（2），163-184。doi:10.7108/PT.200412.0163 【Huang, C.-J. (2004). Student evaluation of teaching effectiveness: A hierarchical linear model. Psychological Testing, 51(2), 163-184. doi:10.7108/PT.200412.0163】
- 鄭蕙如、林世華（2004）。Bloom認知領域教育目標分類修訂版理論與實務之探討─以九年一貫課程數學領域分段能力指標為例。臺東大學教育學報，15（2），247-274。 【Cheng, H.-R., & Lin, S.-H. (2004). Theory and practice of the revision of Bloom’s taxonomy of educational objectives － Take competence indicators of nine-year integrated mathematical curriculum as an example. NTTU Educational Research Journal, 15(2), 247-274.】
- 蕭儒堂、曾建銘、謝佩蓉、黃馨瑩、吳慧珉、陳冠銘…謝進昌（2017）。大型教育調查研究實務：以TASA為例。新北市：國家教育研究院。 【Xiao, R.-T., Tseng, C.-M., Hsieh, P.-R., Huang, S.-Y., Wu, H.-M., Chen, G.-M.,... Hsieh, J.-C. (2017). Using large-scale assessment datasets for research: Taiwan Assessment of Student Achievement (TASA). New Taipei City, Taiwan: National Academy for Educational Research.】
- Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s educational objectives. New York, NY: Longman.
- Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling, 21, 329-341. doi:10.1080/10705511.2014.915181
- Asparouhov, T., & Muthén, B. (2018). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary second model. Web Note 21. Retrieved from http://www.statmodel.com
- Bakk, Z., Tekle, F. B., & Vermunt, J. K. (2013). Estimating the association between latent class membership and external variables using bias-adjusted three-step approaches. Sociological Methodology, 43(1), 272-311. doi:10.1177/0081175012470644
- Bakk, Z., & Vermunt, J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling, 23(1), 20-31. doi:10.1080/10705511.2014.955104
- Bloom, B. S., Engelahar, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objective, handbook 1: Cognitive domain. New York, NY: David McKay.
- Bolck, A., Croon, M. A., & Hagenaars, J. A. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 3-27. doi:10.1093/pan/mph001
- Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195-212. doi:10.1007/BF01246098
- Chang, T.-S. (2001). Student ratings: What are Hualien teacher college students telling us? Journal of Jinan University, 5(2), 169-193.
- Cho, S.-J., Cohen, A. S., Kim, S.-H., & Bottge, B. (2010). Latent transition analysis with a mixture item response theory measurement model. Applied Psychological Measurement, 34(7), 483-504. doi:10.1177/0146621610362978
- Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral and health sciences. Hoboken, NJ: John Wiley & Sons.
- Davidovitch, N., & Soen, D. (2006). Class attendance and students’ evaluation of their college instructors. College Student Journal, 40(3), 691-703.
- Heckert, T. M., Latier, A., Ringwald-Burton, A., & Drazen, A. (2006). Relation among student effort, perceived class difficulty appropriateness, and student evaluations of teaching: Is it possible to “buy” better evaluations through lenient grading? College Student Journal, 40(3), 588-596.
- Koushki, P. A., & Kuhn, H. A. J. (1982). How reliable are student evaluations of teachers? Engineering Education, 72, 362-367.
- Kozub, R. M. (2010). Relationship of course, instructor, and student characteristics to dimensions of student ratings of teaching effectiveness in business schools. American Journal of Business Education, 3(1), 33-40. doi:10.19030/ajbe.v3i1.9024
- Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling, 20(1), 1-26. doi:10.1080/10705511.2013.742377
- Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767-778. doi:10.1093/biomet/88.3.767
- McLachlan, G., & Peel, D. (2000). Finite mixture models. New York, NY: John Wiley & Sons.
- Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
- Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535-569. doi:10.1080/10705510701575396
- Slocombe, T., Miller, D., & Hite, N. (2011). A survey of student perspectives toward faculty evaluations. American Journal of Business Education, 4(7), 51-57. doi:10.19030/ajbe.v4i7.4675
- Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450-469. doi:10.1093/pan/mpq025
Journal directory listing - Volume 65 (2020) - Journal of Research in Education Sciences【65(3)】September (Special Issue: Educational Research on Southeast Asian countries)
Directory
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