Farklı yanıtlar farklı duyuşsal özelliklerle ilişkili midir? CHAID analizi çalışması

Year 2022, Volume: 9 Issue: Special Issue, 28 - 45, 29.11.2022

Neslihan Tuğçe Özyeter

https://doi.org/10.21449/ijate.1132687

Abstract

Eğitimde öğrencilerin öğrenmelerini detaylı bir şekilde incelemek, güçlü ve zayıf yönlerini belirlemek ve etkili geri bildirim vermek zamanla önem kazanmıştır. Dereceli puanlama anahtarları, bu noktada çok faydalı araçlardır çünkü öğrenciler ve paydaşlar için geri bildirim sağlamada kullanılabilirler. Bu araştırmanın amacı, öğrencilerin farklı bilişsel düzeylerde yazılan okuduğunu anlama başarı testi maddelerine verdikleri yanıtların dağılımını belirlemek ve dereceli puanlama anahtarı ile belirlenen yanlış, boş ve ilişkisiz yanıtlarına göre öğrencileri sınıflandırmada etkili olan duyuşsal özellikleri belirlemektir. Bu amaçla, veri toplamak için okuduğunu anlama başarı testi, öğrenci bilgi formu, algılanan akademik öz-yeterlik ölçeği ve öğrenilmiş çaresizlik eğilimi ölçeği kullanılmıştır. Öğrenci bilgi formunda kararlık, başarı güdüsü, zorbalığa maruz kalma ve test kaygısı alt ölçekleri yer almaktadır. Öğrencilerin yanıt kategorilerini belirlemek dereceli puanlama anahtarı kullanılmıştır. Araştırma bulgularına göre maddelerin bilişsel düzeyi karmaşıklaştıkça boş ve yanlış yanıt oranı artmaktadır. En doğru yanıt oranları azalırken, kısmen doğru yanıtlar göreli olarak artmaktadır. Öğrencilerin öğrenilmiş çaresizlik eğilimleri, en temel okuduğunu anlama düzeyinde verdikleri boş ve ilgisiz yanıtları sınıflandırmada etkili olurken, bilişsel süreç karmaşıklaştıkça öğrenci tepkilerini sınıflandıran duyuşsal özelliklerin sayısı da artmıştır. Bu değişkenlerin öğrencilerin yanıtlarını geliştirmede ve kısmen doğru ve en doğru cevaba yönlendirmede önemli olduğu sonucuna varılmıştır. Öğrencileri bu özellikler bağlamında donatacak ve geliştirecek eğitimler ve sınıf ortamlarının oluşturulması önerilebilir.

Keywords

CHAID analysis, Rubric, Affective features, Student answer

Are different responses related to the different affective features? CHAID analysis study

Abstract

In education, examining students' learning in detail, determining their strengths and weaknesses and giving effective feedback have gained importance over time. The aim of this study is to determine the distribution of students’ answers to the reading comprehension achievement test items which were written at different cognitive levels and to investigate the affective variables that are effective in classifying students based on their incorrect, blank, and unrelated answers identified via rubric. For this purpose, a reading comprehension achievement test, a student information form, the perceived academic self-efficacy scale and the learned helplessness tendency scale were used to collect data. The student information form included perseverance, achievement motivation, exposure to bullying and test anxiety subscales. A rubric was used to determine the students’ response categories. According to the findings of the study, the rate of blank and incorrect answers increases as the cognitive level of the items become more complex. While the most correct response rates are decreasing, partially-correct answers are increasing relatively. While students' learned helplessness tendencies were effective in classifying their blank and unrelated answers at the most basic reading comprehension level, as the cognitive process became more complex, the affective characteristics classifying the student responses increased in number. It was concluded that these variables are important in improving the students’ answers and in leading them to the partially correct and the most correct answer. It can be suggested to create trainings and classroom environments that will equip and improve students’ features about these variables.

Keywords

CHAID analysis, Rubric, Affective features, Student answer

References

• Badger, E. & Thomas, B. (1991) Open-Ended questions in reading. Practical Assessment, Research, and Evaluation, 3(4). https://doi.org/10.7275/fryf-z044
• Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122. https://doi.org/10.1037/0003-066X.37.2.122
• Bowes, L., Arseneault, L., Maughan, B., Taylor, A., Caspi, A., & Moffitt, T.E. (2009). School, neighborhood, and family factors are associated with children's bullying involvement: A nationally representative longitudinal study. Journal of the American Academy of Child & Adolescent Psychiatry, 48(5), 545 553. https://doi.org/10.1097/CHI.0b013e31819cb017
• Cassady, J.C., & Johnson, R.E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27(2), 270 295. https://doi.org/10.1006/ceps.2001.1094
• Chapell, M.S., Blanding, Z.B., Silverstein, M.E., Takahashi, M., Newman, B., Gubi, A., & McCann, N. (2005). Test anxiety and academic performance in undergraduate and graduate students. Journal of Educational Psychology, 97(2), 268–274. https://doi.org/10.1037/0022-0663.97.2.268
• Culler, R.E., & Holahan, C.J. (1980). Test anxiety and academic performance: The effects of study-related behaviors. Journal of Educational Psychology, 72(1), 16–20. https://doi.org/10.1037/0022-0663.72.1.16
• Dweck, C.S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040–1048. https://doi.org/10.1037/0003-066X.41.10.1040
• Fincham, F.D., Hokoda, A., & Sanders Jr, R. (1989). Learned helplessness, test anxiety, and academic achievement: A longitudinal analysis. Child development, 60(1), 138-145. https://www.jstor.org/stable/1131079
• Ghasemi, F. (2021). A motivational response to the inefficiency of teachers’ practices towards students with learned helplessness. Learning and Motivation, 73(1), 101705. https://doi.org/10.1016/j.lmot.2020.101705
• Hembree, R. (1988). Correlates, causes, effects, and treatment of test anxiety. Review of educational research, 58(1), 47-77. https://doi.org/10.3102/00346543058001047
• Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17(1), 63-84. https://doi.org/10.1016/j.edurev.2015.11.002
• IBM SPSS. Decision trees 21. IBM Cooperation.
• Kass, V.G. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119-127. https://www.jstor.org/stable/2986296
• Komarraju, M., & Nadler, D. (2013). Self-efficacy and academic achievement: Why do implicit beliefs, goals, and effort regulation matter? Learning and Individual Differences, 25(1), 67-72. https://doi.org/10.1016/j.lindif.2013.01.005
• Kutlu, Ö., & Özyeter, N.T. (in press). Development of the learned helplessness tendency scale for children: Validity and reliability studies. Studies in Psychology.
• Kutlu, Ö., Doğan, C.D., & Karakaya, İ. (2017). Ölçme ve değerlendirme performansa ve portfolyoya dayalı durum belirleme [Measurement and evaluation, assessment based on performance and portfolio]. Pegem Akademi.
• Lumeng, J.C., Forrest, P., Appugliese, D.P., Kaciroti, N., Corwyn, R.F., & Bradley, R.H. (2010). Weight status as a predictor of being bullied in third through sixth grades. Pediatrics, 125(6),1301-1307. https://doi.org/10.1542/peds.2009-0774
• Manzano-Sanchez, H., Outley, C., Gonzalez, J.E., & Matarrita-Cascante, D. (2018). The influence of self-efficacy beliefs in the academic performance of Latina Students in the United States: A systematic literature review. Hispanic Journal of Behavioral Sciences, 40(2), 176–209. https://doi.org/10.1177/0739986318761323
• Mullis, I.V., Martin, M.O., & Sainsbury, M. (2016). PIRLS 2016 reading framework. PIRLS, Chapter-1, 11-29. https://timss.bc.edu/pirls2016/downloads/P16_FW_Chap1.pdf
• Nasir, M., & Iqbal, S. (2019). Academic self-efficacy as a predictor of academic achievement of students in pre-service teacher training programs. Bulletin of Education and Research, 41(1), 33-42. https://files.eric.ed.gov/fulltext/EJ1217900.pdf
• Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Elsevier.
• Olivier, E., Archambault, I., De Clercq, M., & Galand, B. (2019). Student self-efficacy, classroom engagement, and academic achievement: Comparing three theoretical frameworks. Journal of Youth and Adolescence, 48(2), 326 340. https://doi.org/10.1007/s10964-018-0952-0
• Organisation for Economic Co-Operation and Development. (2014). PISA 2012 technical report. OECD Publishing. https://www.oecd.org/pisa/pisaproducts/PISA-2012-technical-report-final.pdf
• Organisation for Economic Co-Operation and Development. (2017). PISA 2015 technical report. OECD Publishing. https://www.oecd.org/pisa/data/2015-technical-report/
• Organisation for Economic Co-Operation and Development (2019). PISA 2018 results (Volume II): Where all students can succeed, PISA, OECD Publishing, https://doi.org/10.1787/b5fd1b8f-en
• Özyeter, N.T., & Kutlu, Ö. (2022). Adaptation of the children's perceived academic self-efficacy scale: Validity and reliability study. International Journal of Assessment Tools in Education, 9(2), 430-450. https://doi.org/10.21449/ijate.958871
• Parvez, M., & Shakir, M. (2014). Academic achievement of adolescents in relation to academic anxiety, gender, and choice of academic stream. Research on Humanities and Social Sciences, 4(1), 107-115. ISSN (Paper) 2224-5766, ISSN (Online) 2225-0484 (Online)
• Popham, W.J. (1999). Classroom assessment: What teachers need to know. Allyn & Bacon. http://www.abacon.com
• Román, M., & Murillo, J. (2011). Latin America: School bullying and academic achievement. Cepal Review. https://repositorio.cepal.org/bitstream/handle/11362/11502/104037053I_en.pdf?sequence=1&isAllowed=y
• Strøm, I.F., Thoresen, S., Wentzel-Larsen, T., & Dyb, G. (2013). Violence, bullying and academic achievement: A study of 15-year-old adolescents and their school environment. Child Abuse & Neglect, 37(4), 243-251. https://doi.org/10.1016/j.chiabu.2012.10.010
• Valås, H. (2001). Learned helplessness and psychological adjustment II: Effects of learning disabilities and low achievement. Scandinavian Journal of Educational Research, 45(2), 101-114. https://doi.org/10.1080/00313830120052705
• van der Werf, C. (2014). The effects of bullying on academic achievement. Revista Desarrollo y Sociedad, 74, 275-308. https://doi.org/10.13043/dys.74.6
• Walling, M.D., & Martinek, T.J. (1995). Learned helplessness: A case study of a middle school student. Journal of Teaching in Physical Education, 14(1), 454 466. https://core.ac.uk/reader/213401840
• Zahrakar, K. (2008). The relationship between parents' child rearing practice and young adults' mental health in Islamshahr. Innovation in Management Education (Journal of Modern Thoughts in Education), 3(2), 71 90. https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=181803
• Zysberg, L., & Schwabsky, N. (2021). School climate, academic self-efficacy and student achievement. Educational Psychology, 41(4), 467 482. https://doi.org/10.1080/01443410.2020.1813690