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Başarımla İlgili Duygular Anketinin Öğretmen Adayları için Geçerleme Çalışması ve Kısa Formu

Year 2021, Issue: 52, 412 - 438, 31.12.2021
https://doi.org/10.53444/deubefd.919467

Abstract

Bu çalışmanın iki amacı bulunmaktadır. Birincisi, Pekrun, Goetz ve Perry (2005) tarafından geliştirilen ve Can, Emmioğlu Sarıkaya ve Bardakçı (2020) tarafından lise öğrencileri için Türkçeye uyarlaması yapılan Başarımla İlgili Duygular Anketinin (BDA), dersle ilgili duygular bölümünün öğretmen adayları için geçerleme çalışmasını yapmaktır. İkincisi, yapılan geçerleme çalışmasının bulgularından hareketle BDA’nın kısa formunun oluşturularak geçerlik ve güvenirliğinin irdelenmesidir. Çalışma grubu, Türkiye’de altı devlet üniversitesinde öğrenim gören 308 öğretmen adayından oluşmaktadır. Verilerin analizinde, birinci ve ikinci düzey faktör analizi yapılmış, Cronbach’s alpha ve yapısal güvenirlik katsayıları hesaplanmıştır. Verilerin analizi sonucunda uyum iyiliği indisleri ve yakınsama geçerliği ölçütlerine uymayan maddeler çıkarılmış; umutsuzluk duygusunun tek faktörlü yapıda, diğer duyguların üç faktörlü ve ikinci düzey faktör yapısında doğrulandığı bulgusuna ulaşılmıştır. Doğrulanan üç faktörlü duygu modelleri için Cronbach’s alpha iç tutarlılık katsayıları 0.60 ve 0.83 arasında değişirken tek faktörlü umutsuzluk duygusu için 0.79 olarak bulunmuştur. Yapısal güvenirlik katsayısı üç faktörlü duygu modelleri için 0.63 ve 0.87 arasında, tek faktörlü umutsuzluk duygusunda 0.79 olarak hesaplanmıştır. Bu bulgulardan hareketle ölçme aracının sekiz faktör 46 maddeden oluştuğu sonucuna ulaşılmıştır. Geçerleme çalışması sonucunda 46 maddeden oluşan BDA’nın kısa versiyonu, kapsam geçerliği gözetilerek yüksek faktör yükü veren maddelerin seçilmesi yoluyla oluşturulmuştur. Doğrulayıcı faktör analizi sonucunda, uyum indisleri iyi ve mükemmel aralığında olan 24 maddelik kısa formun Cronbach Alpha değeri 0.75 olarak; yapısal güvenirlik katsayıları 0.73 ve 0.86 olarak bulunmuştur.

Thanks

Değerli görüşleri ve katkıları için Prof. Dr. Halil Yurdugül’e teşekkür ederiz.

References

  • Can, Y , Sarıkaya, E. E., Bardakçı, S . (2020). Başarı Duyguları Anketinin Türk Kültürüne Uyarlanması. Kastamonu Eğitim Dergisi , 28 (2) , 673-693 . DOI: 10.24106/kefdergi.697110.
  • D'Errico, F., Paciello, M., & Cerniglia, L. (2016). When emotions enhance students’ engagement in e-learning processes. Journal of e-Learning and Knowledge Society, 12(4).
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  • Frenzel, A.C., Pekrun, R., Goetz, T., Daniels, L.M., Durksen, T.L., Becker-Kurz, B. & Robert M. Klassen, R.M. (2016). Measuring Teachers’ enjoyment, anger, and anxiety: The Teacher Emotions Scales (TES). Contemporary Educational Psychology, 46, 148–163. doi: 10.1016/j.cedpsych.2016.05.003
  • Fried, L. (2011). Teaching teachers about emotion regulation in the classroom. Australian Journal of Teacher Education (Online), 36(3), 1.
  • Gefen, D., Straub, D. W., & Boudreau, M.-C. (2000). Structural equation modeling and regressing: guidelines for research practice. Communications of the Association of In formation Systems, 4(7), 1–70.
  • Hacıömeroğlu, G. (2020). Öğretmen Adayları için Öğretmen Duygu Ölçeği-Matematik Türkçe Formu: Geçerlik ve Güvenilirlik Çalışması. Sakarya Üniversitesi Eğitim Fakültesi Dergisi, 20(2), 133-147.
  • Hacıömeroğlu, G., Bilgen, S., ve Tabuk, M. (2013). Başarı duygusu ölçeği-ilkokul’un Türkçe’ye uyarlama çalışması. Eğitim Bilimleri Dergisi, 38, 85-96. doi:10.15285/EBD.2013385568.
  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA
  • Harley, J. M., Lajoie, S. P., Tressel, T., & Jarrell, A. (2018). Fostering positive emotions and history knowledge with location-based augmented reality and tour-guide prompts. Learning and Instruction.
  • Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academicself-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: a structural equation model. BMC MedicalEducation, 20(1), 1-11.
  • Hilliard, J., Kear, K., Donelan, H., & Heaney, C. (2020). Students’ experiences of anxiety in an assessed, online, collaborative project. Computers & Education, 143, 103675.
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
  • Immordino-Yang, M. H., & Damasio, A. (2007). We Feel, Therefore We Learn. The Jossey-Bass reader on the brain and learning, 183.
  • Hair, J. F. , Ringle, C M. & Sarstedt, M. (2011) PLS-SEM: Indeed a Silver Bullet, Journal of Marketing Theory and Practice, 19:2, 139-152, DOI: 10.2753/ MTP1069-6679190202
  • Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford Press.
  • Lajoie, S. P., Pekrun, R., Azevedo, R., & Leighton, J. P. (2019). Understanding and measuring emotions in technology-rich learning environments. Learning and Instruction, 101272.
  • Lam, L. W. (2012). Impact of competitiveness on salespeople's commitment and performance. Journal of Business Research, 65(9), 1328-1334.
  • Marchand, G. C., & Gutierrez, A. P. (2012). The role of emotion in the learning process: Comparisons between online and face-to-face learning settings. The Internet and Higher Education, 15(3), 150-160.
  • Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530.
  • Obergriesser, S., & Stoeger, H. (2020). Students’ emotions of enjoyment and boredom and their use of cognitive learning strategies–How do they affect one another?. Learning and Instruction, 66, 101285.
  • Park, T., & Lim, C. (2019). Design principles for improving emotional affordances in an online learning environment. Asia Pacific Education Review, 20(1), 53-67. Parkinson, B., & Totterdell, P. (1999). Classifying affect-regulation strategies. Cognition & Emotion, 13(3), 277-303.
  • Peixoto, F., Mata, L., Monteiro, V., Sanches, C., & Pekrun, R. (2015). The achievement emotions questionnaire: Validation for pre-adolescent students. European Journal of Developmental Psychology, 12(4), 472-481.
  • Peklaj, C., & Pečjak, S. (2011). Emotions, motivation and self-regulation in boys’ and girls’ learning mathematics. Horizons of Psychology, 20(3), 33-58.
  • Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41(4), 359-376.
  • Pekrun, R., Goetz, T., & Perry, R. P. (2005). Achievement emotions questionnaire (AEQ). User’s manual. Unpublished Manuscript, University of Munich, Munich.
  • Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational psychology review, 18(4), 315-341.
  • Pekrun, R. (2019). Inquiry on emotions in higher education: progress and open problems. Studies in Higher Education, 44(10), 1806-1811.
  • Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. International handbook of emotions in education, 120-141.
  • Pekrun, R., & Stephens, E. J. (2010). Achievement emotions: A control‐value approach. Social and Personality Psychology Compass, 4(4), 238-255.
  • Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary educational psychology, 36(1), 36-48.
  • Raccanello, D., Brondino, M., Crescentini, A., Castelli, L., & Calvo, S. (2021): A brief measure for school-related achievement emotions: The achievement Emotions Adjective List (AEAL) for secondary students, European Journal of Developmental Psychology, DOI: 10.1080/17405629.2021.1898940
  • Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied nultivariate analysis. New York, NY: Taylor and Francis.
  • Redmond, P., Abawi, L. A., Brown, A., Henderson, R., & Heffernan, A. (2018). An online engagement framework for higher education. Online Learning, 22(1), 183-204.
  • Regan, K., Evmenova, A., Baker, P., Jerome, M. K., Spencer, V., Lawson, H., & Werner, T. (2012). Experiences of instructors in online learning environments: Identifying and regulating emotions. The Internet and Higher Education, 15(3), 204-212.
  • Rienties, B., & Rivers, B. A. (2014). Measuring and understanding learner emotions: Evidence and prospects. Learning Analytics Review, 1, 1-28.
  • Sanches, C., Monteiro, V., Mata, L., Santos, N., & Gomes, M. (2020). Psychometric properties of the Portuguese version of the Achievement Emotions Questionnaire for Elementary School. Análise Psicológica, 38(1), 127-139.
  • Schrader, C., & Kalyuga, S. (2020). Linking students’ emotions to engagement and writing performance when learning Japanese letters with a pen-based tablet: An investigation based on individual pen pressure parameters. International Journal of Human-Computer Studies, 135, 102374.
  • Stephan, M., Gläser-Zikuda, M., & Markus, S. (2019). Students’ achievement emotions and online learning in teacher education. In Frontiers in Education (Vol. 4, p. 109). Frontiers.
  • Wosnitza, M., & Volet, S. (2005). Origin, direction and impact of emotions in social online learning. Learning and instruction, 15(5), 449-464.
  • You, J. W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Computers & Education, 77, 125-133.
  • Yu, J., Huang, C., Han, Z., He, T., & Li, M. (2020). Investigating the Influence of Interaction on Learning Persistence in Online Settings: Moderation or Mediation of Academic Emotions?. International Journal of Environmental Research and Public Health, 17(7), 2320.
  • Ziegler, M., Kemper, C. J., & Kruyen, P. (2014). Short scales—Five misunderstandings and ways to overcome them. Journal of Individual Differences, 35, 185–189.
Year 2021, Issue: 52, 412 - 438, 31.12.2021
https://doi.org/10.53444/deubefd.919467

Abstract

References

  • Can, Y , Sarıkaya, E. E., Bardakçı, S . (2020). Başarı Duyguları Anketinin Türk Kültürüne Uyarlanması. Kastamonu Eğitim Dergisi , 28 (2) , 673-693 . DOI: 10.24106/kefdergi.697110.
  • D'Errico, F., Paciello, M., & Cerniglia, L. (2016). When emotions enhance students’ engagement in e-learning processes. Journal of e-Learning and Knowledge Society, 12(4).
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  • Frenzel, A.C., Pekrun, R., Goetz, T., Daniels, L.M., Durksen, T.L., Becker-Kurz, B. & Robert M. Klassen, R.M. (2016). Measuring Teachers’ enjoyment, anger, and anxiety: The Teacher Emotions Scales (TES). Contemporary Educational Psychology, 46, 148–163. doi: 10.1016/j.cedpsych.2016.05.003
  • Fried, L. (2011). Teaching teachers about emotion regulation in the classroom. Australian Journal of Teacher Education (Online), 36(3), 1.
  • Gefen, D., Straub, D. W., & Boudreau, M.-C. (2000). Structural equation modeling and regressing: guidelines for research practice. Communications of the Association of In formation Systems, 4(7), 1–70.
  • Hacıömeroğlu, G. (2020). Öğretmen Adayları için Öğretmen Duygu Ölçeği-Matematik Türkçe Formu: Geçerlik ve Güvenilirlik Çalışması. Sakarya Üniversitesi Eğitim Fakültesi Dergisi, 20(2), 133-147.
  • Hacıömeroğlu, G., Bilgen, S., ve Tabuk, M. (2013). Başarı duygusu ölçeği-ilkokul’un Türkçe’ye uyarlama çalışması. Eğitim Bilimleri Dergisi, 38, 85-96. doi:10.15285/EBD.2013385568.
  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA
  • Harley, J. M., Lajoie, S. P., Tressel, T., & Jarrell, A. (2018). Fostering positive emotions and history knowledge with location-based augmented reality and tour-guide prompts. Learning and Instruction.
  • Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academicself-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: a structural equation model. BMC MedicalEducation, 20(1), 1-11.
  • Hilliard, J., Kear, K., Donelan, H., & Heaney, C. (2020). Students’ experiences of anxiety in an assessed, online, collaborative project. Computers & Education, 143, 103675.
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
  • Immordino-Yang, M. H., & Damasio, A. (2007). We Feel, Therefore We Learn. The Jossey-Bass reader on the brain and learning, 183.
  • Hair, J. F. , Ringle, C M. & Sarstedt, M. (2011) PLS-SEM: Indeed a Silver Bullet, Journal of Marketing Theory and Practice, 19:2, 139-152, DOI: 10.2753/ MTP1069-6679190202
  • Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford Press.
  • Lajoie, S. P., Pekrun, R., Azevedo, R., & Leighton, J. P. (2019). Understanding and measuring emotions in technology-rich learning environments. Learning and Instruction, 101272.
  • Lam, L. W. (2012). Impact of competitiveness on salespeople's commitment and performance. Journal of Business Research, 65(9), 1328-1334.
  • Marchand, G. C., & Gutierrez, A. P. (2012). The role of emotion in the learning process: Comparisons between online and face-to-face learning settings. The Internet and Higher Education, 15(3), 150-160.
  • Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530.
  • Obergriesser, S., & Stoeger, H. (2020). Students’ emotions of enjoyment and boredom and their use of cognitive learning strategies–How do they affect one another?. Learning and Instruction, 66, 101285.
  • Park, T., & Lim, C. (2019). Design principles for improving emotional affordances in an online learning environment. Asia Pacific Education Review, 20(1), 53-67. Parkinson, B., & Totterdell, P. (1999). Classifying affect-regulation strategies. Cognition & Emotion, 13(3), 277-303.
  • Peixoto, F., Mata, L., Monteiro, V., Sanches, C., & Pekrun, R. (2015). The achievement emotions questionnaire: Validation for pre-adolescent students. European Journal of Developmental Psychology, 12(4), 472-481.
  • Peklaj, C., & Pečjak, S. (2011). Emotions, motivation and self-regulation in boys’ and girls’ learning mathematics. Horizons of Psychology, 20(3), 33-58.
  • Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41(4), 359-376.
  • Pekrun, R., Goetz, T., & Perry, R. P. (2005). Achievement emotions questionnaire (AEQ). User’s manual. Unpublished Manuscript, University of Munich, Munich.
  • Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational psychology review, 18(4), 315-341.
  • Pekrun, R. (2019). Inquiry on emotions in higher education: progress and open problems. Studies in Higher Education, 44(10), 1806-1811.
  • Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. International handbook of emotions in education, 120-141.
  • Pekrun, R., & Stephens, E. J. (2010). Achievement emotions: A control‐value approach. Social and Personality Psychology Compass, 4(4), 238-255.
  • Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary educational psychology, 36(1), 36-48.
  • Raccanello, D., Brondino, M., Crescentini, A., Castelli, L., & Calvo, S. (2021): A brief measure for school-related achievement emotions: The achievement Emotions Adjective List (AEAL) for secondary students, European Journal of Developmental Psychology, DOI: 10.1080/17405629.2021.1898940
  • Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied nultivariate analysis. New York, NY: Taylor and Francis.
  • Redmond, P., Abawi, L. A., Brown, A., Henderson, R., & Heffernan, A. (2018). An online engagement framework for higher education. Online Learning, 22(1), 183-204.
  • Regan, K., Evmenova, A., Baker, P., Jerome, M. K., Spencer, V., Lawson, H., & Werner, T. (2012). Experiences of instructors in online learning environments: Identifying and regulating emotions. The Internet and Higher Education, 15(3), 204-212.
  • Rienties, B., & Rivers, B. A. (2014). Measuring and understanding learner emotions: Evidence and prospects. Learning Analytics Review, 1, 1-28.
  • Sanches, C., Monteiro, V., Mata, L., Santos, N., & Gomes, M. (2020). Psychometric properties of the Portuguese version of the Achievement Emotions Questionnaire for Elementary School. Análise Psicológica, 38(1), 127-139.
  • Schrader, C., & Kalyuga, S. (2020). Linking students’ emotions to engagement and writing performance when learning Japanese letters with a pen-based tablet: An investigation based on individual pen pressure parameters. International Journal of Human-Computer Studies, 135, 102374.
  • Stephan, M., Gläser-Zikuda, M., & Markus, S. (2019). Students’ achievement emotions and online learning in teacher education. In Frontiers in Education (Vol. 4, p. 109). Frontiers.
  • Wosnitza, M., & Volet, S. (2005). Origin, direction and impact of emotions in social online learning. Learning and instruction, 15(5), 449-464.
  • You, J. W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Computers & Education, 77, 125-133.
  • Yu, J., Huang, C., Han, Z., He, T., & Li, M. (2020). Investigating the Influence of Interaction on Learning Persistence in Online Settings: Moderation or Mediation of Academic Emotions?. International Journal of Environmental Research and Public Health, 17(7), 2320.
  • Ziegler, M., Kemper, C. J., & Kruyen, P. (2014). Short scales—Five misunderstandings and ways to overcome them. Journal of Individual Differences, 35, 185–189.
There are 44 citations in total.

Details

Primary Language Turkish
Subjects Studies on Education
Journal Section Articles
Authors

Eda Bakır 0000-0001-5178-486X

Nilüfer Atman Uslu 0000-0003-2322-4210

Yasemin Usluel 0000-0002-6147-3333

Publication Date December 31, 2021
Published in Issue Year 2021 Issue: 52

Cite

APA Bakır, E., Atman Uslu, N., & Usluel, Y. (2021). Başarımla İlgili Duygular Anketinin Öğretmen Adayları için Geçerleme Çalışması ve Kısa Formu. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi(52), 412-438. https://doi.org/10.53444/deubefd.919467