Sekizinci Sınıf Öğrencilerinin Fen Başarısı Üzerinde Etkili Olan Değişkenlerin Ordinal Lojistik Regresyon Yöntemiyle İncelenmesi

Bu çalışmanın amacı, Ordinal Lojistik Regresyon yöntemi kullanılarak sekizinci sınıf öğrencilerinin fen başarısını etkileyen faktörleri araştırmaktır. Bu çalışmada, ABİDE 2016 uygulamasına katılan 2202’si (%52.1) erkek ve 2022’si (%47.9) kız olmak üzere, toplam 4224 öğrenciden toplanan bilgiler kullanılmıştır. ABİDE 2016 uygulamasına giren öğrencilerin Fen Bilimleri dersinden almış oldukları puanlar belirlenmiş eşik değerlere göre kategorik hale dönüştürülmüştür.Analizde; kategorik hale getirilen fen başarı puanı yordanan değişken; fen başarısını etkilediği düşünülen 15 adet değişken ise yordayıcı değişken olarak kullanılmıştır. Daha sonra elde edilen veriler Ordinal Lojistik Regresyon yöntemi ile incelenmiştir. İnceleme sonucu fen başarısını etkileyen faktörler sırasıyla; akran zorbalığı, öz yeterlilik algısı, derse verilen değer, aile ilgisi, aile baskısı, baba eğitim durumu, anne eğitim durumu, aylık gelir, bilgisayar ya da tablet sahibi olma durumu, kendisine ait odaya sahip olma durumu, öğrencinin eğitimdeki hedefi ve fen destekleme kurslarına katılma durumu olarak belirlenmiştir. Elde edilen sonuçların literatür ile büyük oranda benzerlik gösterdiği görülmüştür. Bu da OLR yönteminin eğitim verileri üzerinde tahmin yeteneğinin yüksek olduğuna işaret etmektedir. Bu tür çalışmaları yansız-sapmasız ele alacağı düşünülmektedir

Examination of Variables Affecting Science Success of Eighth Grade Students Using Ordinal Logistic Regression Method

The aim of this study was to investigate the factors affecting the science success of eighth-grade students using the Ordinal Logistic Regression method. In this study, information collected from a total of 4224 students, 2202 (52.1%) male and 2022 (47.9%) female, who participated in the ABIDE 2016 application was used. The scores obtained from the science course of the students entering the ABIDE 2016 application were transformed into categorical ones according to the determined threshold values. In the analysis, the categorical science success score was used as the predicted variable, and 15 variables that were thought to affect science success were used as predictive variables. The data obtained later were examined by the Ordinal Logistic Regression method. As a result of the examination, the factors affecting science success were determined as peer bullying, perceived self-efficiency, value given to the course, parental attention, family pressure, father’s educational status, mother's educational status, monthly income, having a computer or tablet, having a room of one's own, student's educational goal and participation in science support courses. The findings were found to be remarkably similar to those found in the literature. This indicates that the OLR method is quite effective in predicting training data. Such research is expected to be handled objectively and without bias.

___

  • Abacı, Ç. Ç. (2015). The evaluation of central system common exams in terms of different variables, (Master Thesis). Gazi Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Abacıoğlu,S. & Ünal, İ.H. (2017). Determining the efficiency of companies by using data envelopment and ordinal logistic regression analysis: a study of weaving, apperal and leather industry. Eurasian Journal of Researches in Social and Economics, 4 (12), 1-19.
  • Güvendir, M. A. (2014). Student and school characteristics' relation to Turkish achievement in student achievement determination exam. Egitim ve Bilim, 39(172). Large-Scale Assessment Special Issue
  • Akamca, G. Ö. & Hamurcu, H. (2005). The effects of instruction based on multiple intelligence theory on students’ science achievement, attitudes and retention of knowledge. Hacettepe University Faculty of Educational Sciences Journal, 28(28), 178-187.
  • Akboğa Kale, Ö.. (2020). Determınation of poor compliance wıth osh rules of construction workers using ordinal regression model. Mugla Journal of Science and Technology, 6(1), 78-88.
  • Akhan, Ş. & Bindak, R. (2017). The effect of some individual variables on secondary school students’ math achievement: a regression model. Ihlara Journal of Educational Research, 2(2), 5-17.
  • Akın, H.B. & Şentürk, E. (2012). Analysing levels of happiness of individuals with ordinal logistic analysis. Öneri Journal, 10.(37),183-193.
  • Akıncı, B. (2020). The examination of science curriculum and assessment studies according to the monitoring and evaluation of academic skills (ABİDE) (Master Thesis) Ankara Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Aksu, G., Güzeller, C. O. & Eser, M. T. (2017). Analysis of Maths Literacy Performances of Students with Hierarchical Linear Modeling (HLM): The Case of PISA 2012 Turkey. Education and Science, 42(191), 247-266.
  • Aktürk, Ü. & Aylaz, R. (2013). Students’ levels of self-sufficiency in a primary school. DEUHYO ED, 6(4), 177-183.
  • Aküzüm, C. & Saraçoğlu, M. (2018). Investigation of Secondary School Teachers’ Attitudes towards Supporting and Training Courses, Turkish Journal of Educational Studies, 5(2), 97-121.
  • Alkan, İ., Alkan, İ., & Bayri, N. (2017). A meta analysis study on the relationship between motivation for science learning and science achievement, Dicle University Journal of Ziya Gökalp Faculty of Education, (32), 865-874.
  • Anıl, D. (2009). Factors Effecting Science Achievement of Science Students in Programme for International Students’ Achievement (PISA) in Turkey. Education and Science, 152(34), 87-100.
  • Arı, E. (2013). Parallel lines assumption in ordinal logistic regression and analysis approaches, (Doctoral Thesis). Eskişehir Osmangazi Üniversitesi Fen Bilimleri Enstitüsü, Eskişehir.
  • Arı, E. & Yıldız, Z. (2016). An examination of factors that affect the individuals’ life satisfaction with ordinal logit regression analysis, The Journal of International Social Research, 9(42), 1362-1374.
  • Aslanargun, E. (2007). The review of literature on school-parent cooperation and students’ school success. Manas University Journal of Social Sciences, 18, 119-135.
  • Aslanargun, E., Bozkurt, S. & Sarıoğlu, S. (2016). The Impacts of Socioeconomic Variables on the Academic Success of the Students. Uşak University Journal of Social Sciences, 9(3), 214-234.
  • Azapağası İlbağı E. & Akgün L., 2012. Examination of Attitudes of the Students Aged 15 In Terms of Pisa 2003 Student Questionnaire. Western Anatolia Journal of Educational Science, 3(6), 67-90.
  • Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational psychologist, 28(2), 117-148.
  • Bayat, N., Şekercioğlu, G. & Bakır, S. (2014). The Relationship Between Reading Comprehension and Success in Science. Education and Science, 39(176).
  • Bezek Güre, Ö. Kayri, M. &Erdoğan, F.(2020). Analysis of Factors Effecting PISA 2015 Mathematics Literacy via Educational Data Mining. Education and Science, 45(202), 393-415.
  • Bezek Güre, Ö., Kayri, M. & Erdoğan, F. (2019). Predicting factors affecting PISA 2015 Mathematics literacy via Radial Basis Function Artificial Neural Network. Journal of Engineering and Technology, 3(1), 1-11.
  • Bozpolat, E. (2016). Investigation of the Self-Regulated Learning Strategies of StudentsfromtheFaculty of Education Using Ordinal Logistic Regression Analysis. Educational Sciences: Theory and Practice, 16(1), 301-318.
  • Büyüköztürk Ş., (2011), Sosyal Bilimler İçin Veri Analizi El Kitabı.Ankara:Pegem Akademi Yayınları,
  • Ceylan, E.,& Berberoğlu, G. (2010). Factors Related With Students’ Science Achievement: A Modeling Study. Education and Science, 32(144), 36-48.
  • Chang,Y.C. & Bangsri,A. (2020). ThaiStudents’ Perceived Teacher Support on Their Reading Ability: Mediating Effects of Self-Efficacyand Sense of School Belonging. International Journal of Educational Methodology, 6(2), 435-446.
  • Çalık, G. (2020). Investigation of 8th grade students' science achievement in Turkey: Results from monitoring and evaluating academic skills study (ABIDE) 2016, (Master Thesis). Orta Doğu Teknik Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.
  • Çalışkan, M. (2008).The impact of school and student related factors on scientific literacy skills in the programme for international student assessment PISA 2006, (Doktora Tezi). Orta Doğu Teknik Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Celenk, S. (2003). The prerequisite for school success: Home-school cooperation. Elementary Education Online, 2(2), 28-34.
  • Çokluk Ö., Şekercioğlu G. & Büyüköztürk Ş. (2012). Sosyal bilimler için Çok değişkenli istatistik SPSS ve LISREL uygulamaları. Ankara: Pegem Yayınları.
  • Çokluk, Ö. (2010). Logistic Regression: Concept and Application. Educational Sciences: Theory&Practise, 10(3), 1357-1407.
  • Dağlıoğlu, H. & Oral Erbaş, S. (2017). A Study On Correct Classıfıcatıon Performance Of Unconstraınt Partıal Proportıonal Odds Model. Gazi Journal of Engineering Sciences 3(3), 14-26.
  • Diaz, S. L. (1989, April-May). The Home Environment and Puerto Rıcan Children’s Achievement: A Researcher’s Diary, (Bul. Kyn. Satır, (1996). The Natiohal Association for Education Conference, Hulston.
  • Doğan, N. & Barış, F. (2010). Tutum, Değer ve Özyeterlik Değişkenlerinin TIMSS-1999 ve TIMSS-2007 Sınavlarında Öğrencilerin Matematik Başarılarını Yordama Düzeyleri. Journal of Measurement and Evaluation in Education and Psychology, 1(1), 44-50.
  • Doğan, N., Beyaztaş, İ. B. & Koçak, Z. (2012). The Effect of Self Efficacy Level of Students on Their Achievement in Terms of Their Grade Levels and Gender in A Social Studies Class: The Case Of Erzurum. Education and Science, 37(165), 224-236.
  • Elkonca, F. (2020). An analysis of the DIF sources of ABIDE self-efficacy scale by means of a latent class approach, (Doctoral Thesis). Gazi Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Erdinç Akan, O. (2016). The investigation of the relationship between the characteristics of students and teachers and TIMSS 2011 8th grade students' achievement in respect to the cognitive domains: A two level hierarchical linear model analysis,(Master Thesis). Gazi Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Erdoğan, E. & Acar Güvendir, M. (2018). The Relationship Between Students Socioeconomic Attributes and Their Reading Skills in Programme for International Student Assessment. Eskişehir Osmangazi University Journal of Social Sciences, 20, 493-523.
  • Fullerton, A.S. (2009). “A Conceptual Framework for Ordered Logistic Regression Models”. Sociological Methods and Research, 38(2), 306-347.
  • Fullerton, A.S. & Xu, J.(2012). “The Proportional Odds With Partial Proportionality Constraints Model for Ordinal ResponseVariables”. Social Science Research, 41(1), 182-198.
  • Gelbal, S. (2008). The Effect of Socio-Economic Status of Eighth Grade Students On Their Achievement in Turkish. Education and Science, 33(150), 1-13.
  • Güvendir, M. (2017). Determination of the Relationship between the Students’ “Mathematical Literacy” and “Home and School Educational Resources” in Program for International Student Assessment - (PISA 2012). Mersin University Journal of The Faculty of Education 13(1), 94-109.
  • Halaby, Charles N. (1986). ‘‘Worker Attachment and Workplace Authority’’. American Sociological Review 51, 634-649.
  • Jeynes, W. H. (2005). A meta-analysis of therelation of parental involvementto urban elementary school student academic achievement. Urban education, 40(3), 237- 269.
  • Juan, A.,Hannan, S. & Namome, C. (2018). I believe I can do science: Self-efficacy and science achievement of Grade 9 students in South Africa. South African Journal of Science, 114(7-8), 48-54.
  • Karasar, N.(2006). Bilimsel araştırma yöntemi. Ankara: Nobel Yayın Dağıtım.
  • Kartal, H.,ve Bilgin, A. (2009). Bullying and School Climate from theAspects of the Students and Teachers. Eurasian Journal of Educational Research, (36), 209-226
  • Keller, P. S., El-Sheikh, M., Granger, D. A. & Buckhalt, J. A. (2012). Interactions between salivary cortisol and alpha amylase as predictors of children’s cognitive functioning and academic performance. Physiology&Behavior, 105, 987-995.
  • Kılıç, S. (2016). Cronbachs Alpha Reliability Coefficient. Journal of Mood Disorders, 6(1), 47-48.
  • Kırmızı, B. & İşıgüzel, B. (2014). Determining the Factors Affecting Students’ Achievement in German Language Learning, Fırat University Journal of Social Science, 24(1), 13-24
  • Kula-Kartal, S.,& Mor-Dirlik, E. (2017). Historical development of the concept of validity and the most preferred technique of reliability: cronbach alpha coefficient, Abant İzzet Baysal University Education Faculty Journal, 16(4), 1865-1879.
  • Liu, X. (2009). Ordinal regression analysis: Fitting the proportional odds model using Stata, SAS and SPSS. Journal of Modern Applied Statistical Methods, 8(2), 30.
  • Long, J. S., & Cheng, S. (2004). Regression models for categorical outcomes. Handbook of data analysis, 704.
  • Maddala, G. S. (1983), Limited-Dependent and QualitativeVariables in Economics, New York: Cambridge University Press. Maier, S. R. & Curtin, P.A. (2005). Self-EfficacyTheory: A Prescriptive Model for Teaching Research Methods. Journalism and Mass Communication Educator, 59(4), 352-364.
  • McCullagh, P. (2005). Proportional‐odds model. Encyclopedia of Biostatistics, 6.
  • McCullagh, P. & Nelder, J. A. (1983). Generalized linear models. New York: Chapman and Hall London..
  • McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109-127.
  • McCullagh, P., (1998). The proportional odds model. In: Armitage, Peter, Colton, Theodore (Eds.), The Encyclopedia of Biostatistics. John Wiley and Sons,
  • MEB (2017). Examination and evaluation of academic skills 8th grade report, Ankara.
  • Nartgün, Ş. S. & Dilekçi, Ü. (2016). Student and Teacher Views on Educational Support and Training Courses Kuram ve Uygulamada Eğitim Yönetimi, 22(4), 537-564.
  • O’Connell, A.A. (2006). Logistic Regression Models for Ordinal Response Variables. California: Sage Publications.
  • Okutan, S. (2017). Investigation of secondary school students' success in science in terms of various variables, (Doctoral Thesis). Ordu Üniversitesi Fen Bilimleri Enstitüsü, Ordu.
  • Özdamar, K. (2013). Paket programlar ile istatiksel veri analizi: MINITAB 16-IBM SPSS 21. Eskişehir: Nisan Kitabevi
  • Pala, N. M. (2008). The effect of student and class characteristics on mathematical literacy and problem solving in accordance with PISA 2003 results, (Master Thesis). Balıkesir Üniversitesi Fen Bilimleri Enstitüsü, Balıkesir.
  • Peterson, B. & Harrell, F. E. (1990). Partial proportional odds models for ordinal response variables. Applied Statistics, 39, 205-217.
  • Ramirez, F. O.,Luo, X., Schofer, E. & Meyer, J. W. (2006). Student achievement and national economic growth. American Journal of Education, 113(1), 1-29.
  • Sadıç, A. & Çam, A. (2015). 8th grade students’ epistemological beliefs for PISA performances and science and technology literacy, Bilgisayar ve Eğitim Araştırmaları Dergisi, 3(5), 18-49.
  • Şaşmazel, G.A. (2006). Factors that affecting success of scientific literacy on students in Turkey that participate programme for international student assessment (PISA)(Master Thesis). Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.
  • Senel, S.,& Alatli, B. (2014). A Review of Articles Used Logistic Regression Analysis. Journal of Measurement and Evaluation in Education and Psychology-EPOD, 5(1), 35-52.
  • Şevgin, H. (2013). Reearch of secondary school studendents motivation to the Physical Science with the method of ordinal logistic regression, (Master Thesis). Yüzüncü Yıl Üniversitesi Eğitim Bilimleri Enstitüsü, Van.
  • Şevgin, H. (2020). Predicting the ABIDE 2016 science achievement: The comparison of MARS and BRT data mining methods, (Doctoral Thesis). Gazi Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Tabachnick, B. G.,& Fidell, L. S. (1996). Using Multivariate Statistics (3rd ed.). New York: Harper Collins.
  • Tansel, A. & Güngör, N. D. (2004). Türkiye’den yurt dışına beyin göçü: Ampirik bir uygulama. ERC (Economic Research Center) Working Papers in Economics, 4(02), 1-10.
  • Türkan, A., Üner, S., &Alcı, B. (2015). An Analysis of 2012 PISA Mathematics Test Scores in Terms of Some Variables. Ege Journal of Education, 16(2), 358-372.
  • Urfalı Dadandı, P. Dadandı, İ. & Koca, F. (2018). The relationships between socieconomic factors and reading literacy according to PISA 2015 Turkey results. International Journal of Turkish Literature, Culture Education, 7(2), 1239-1252.
  • Uzun, B. & Öğretmen, T. (2010). Assessing the Measurement Invariance of Factors that are Related to Students’ Science Achievement across Gender in TIMSS-R Turkey Sample, Education and Science, 35(155).
  • Uzun, N. B., Gelbal, S. & Öğretmen, T. (2010). Modeling the realitionship between timss-r science achievement and affective characteristics and comparing the model according to gender. Kastamonu University, Kastamonu Education Journal, 18(2), 531-544.
  • Ülkü, S. (2019). Investigation of measurement invariance of turkish and science's subtests on ABIDE 2016 in relation to characteristics of teachers (Master Thesis). Hacettepe Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Whitted K. S. & Dupper, D. R. 2005. Best practises for preventing or reducing bullying in schools. Children And Schools, 27(3), 167–175.
  • Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. Stata Journal, 6(1), 58-82.
  • Winnaar, L.,Arends, F. &Beku, U. (2018). Reducing bullying in schools by focusing on school climate and school socio-economic status. South African Journal of Education, 38(1).
  • Wright, E. O., Baxter, J. & Birkelund, G. E. (1995). The gender gap in workplace authority: A cross-national study. American sociological review, 407-435.
  • Yılmaz, M. (2021). Investigation of differantial item functioning of the test items in the abide study by using propensity scores, (Master Thesis). Hacettepe Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Yurdugül, H. (2006). The comparison of reliability coefficients in parallel, tau-equivalent, and congeneric measurements. Ankara University Journal of Faculty of Educational Sciences, 39(1), 15-37.