Examining the Factors That Increase Students' Mathematics Achievement with Hybrid Fuzzy DEMATEL & System Dynamics Approach

Yıl 2024, Sayı: 59, 507 - 531, 29.03.2024

Mehmet Akif Aksoy İpek Deveci Kocakoç

https://doi.org/10.53444/deubefd.1388221

Öz

In a rapidly changing world, it has become a necessity to keep up with developing technologies. It is only possible for countries that are competing in many of these developments to achieve the desired results in these areas with qualified education. The importance of mathematics and mathematics education, which directly contributes to the understanding of these developments in order to keep up with developing technologies and not to fall behind in the competitive environment, is increasing day by day. Effective use of mathematics, which exists in almost every field today, is only possible with a well-planned and qualified mathematics education. It is clear that a well-planned mathematics education will bring mathematics success. Mathematics achievement is a very complex problem, consisting of the interaction of many intertwined variables and the interaction of different dimensions. One of the important tools in solving such multidimensional complex problems is System Dynamics. This study aimed to examine the factors that increase students' mathematics achievement. First, a Causal Loop Diagram (CLD) was created with the System Dynamics approach, then the resulting loops were evaluated and one of the reinforcing loops was selected. Which of the variables in the cycle consisting of sixteen variables is effective in increasing success was examined with Fuzzy DEMATEL, one of the Multi-Criteria Decision Making Techniques. While “Student Motivation”, “Study Efficiency”, and “Quality of Education” were the three variables with the highest impact power, “Ability to Use Technical Opportunities”, “Study Efficiency”, and “Quality of Education” were determined to be the three variables with the highest degree of importance.

Anahtar Kelimeler

Mathematics Achievement, System Dynamics, Causal Loop Diagrams, Fuzzy DEMATEL.

Öğrencilerin Matematik Başarısını Artıran Faktörlerin Hibrit Bulanık DEMATEL & Sistem Dinamikleri Yaklaşımıyla İncelenmesi

Öz

Günümüz dünyasında teknoloji ile uyum sağlamak giderek daha fazla önem kazanmakta ve bu, özellikle hızla ilerleyen ülkeler için zorunlu bir hal almaktadır. İstenen düzeydeki teknolojik başarıya ulaşabilmek için kaliteli eğitimin kilit bir rol oynadığı görülmektedir. Bu bağlamda, gelişen teknolojilerle paralel olarak ilerlemeyi ve rekabetçi kalmayı mümkün kılan matematik eğitiminin değeri gün geçtikçe artmaktadır. Kapsamlı ve kaliteli matematik eğitiminin, matematiksel başarıyı ve bu alanın her yönüyle etkin kullanımını getireceği açıktır. Matematik başarısının, birçok değişkenin etkileşiminden oluşan karmaşık yapısını çözmek için Sistem Dinamikleri gibi ileri araçlar bu çalışmada kullanılmış, öğrenci başarısını artırma potansiyeline sahip unsurlar detaylı bir şekilde incelenmiştir. Çalışmada, Nedensel Döngü Diyagramları kullanılarak oluşturulan model üzerinden, öğrencilerin matematik başarısını artıran faktörler analiz edilmiş ve Bulanık DEMATEL yöntemi ile öne çıkan başlıca etkenler belirlenmiştir. “Öğrenci Motivasyonu”, “Çalışma Verimliliği” ve “Eğitim öğretim kalitesi” değişkenleri etkileme gücü en yüksek üç değişken olurken, “Teknik İmkanları Kullanabilme”, “Çalışma Verimliliği” ve “Eğitim öğretim kalitesi” değişkenleri de önem derecesi en yüksek üç değişken olarak tespit edilmiştir.

Anahtar Kelimeler

Matematik Başarısı, Sistem Dinamikleri, Nedensel Döngü Diyagramları, Bulanık DEMATEL

Kaynakça

• Akarsu, B. (2017). Modern Öğretim Teknolojisi ve Materyal Tasarımı. İstanbul: Cinius Yayınları.
• Anderson, M. (2010). Linking perceptions of school belonging to academic motivation and academic achievement amongst student athletes: A comparative study between high-revenue student athletes and non-revenue student athletes (Yayımlanmış Doktora Tezi). Berkeley: University of California.
• Arıkan, S. (2016). Factors contributing to mathematics achievement differences of Turkish and Australian students in TIMSS 2007 and 2011. Eurasia Journal of Mathematics, Science & Technology Education, 12(8), 2039-2059.
• Austin, G. & Bailey, J. (2008). What teachers and other staff tell us about California schools: Statewide results of the 2004-06 California school climate survey. Sacramento: California Department of Education.
• Bala, B. K., Arshad, F. M., & Noh, K. M. (2017). System dynamics. Modelling and Simulation, Springer, 274.
• Berkowitz, R., Moore, H., Astor, R. A. & Benbenishty, R. (2017). A research synthesis of the associations between socioeconomic background, inequality, school climate, and academic achievement. Review of Educational Research, 87(2), 425-469.
• Braxton, J. M., & Hirschy, A. S. (2005). Theoretical developments in the study of college student departure. College student retention: Formula for student success, 3, 61-87.
• Carpenter, W. A. (2000). Ten years of silver bullets: Dissenting thoughts on education reform. The Phi Delta Kappan, 81(5), 383-389.
• Carroll, J. B. (1963). A model of school learning. Teachers college record. 64(8), 1-9
• Chen, J. K., & Chen, I. S. (2010). Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Systems with Applications, 37(3), 1981-1990.
• Chiang, Y. H. (2015). MCDM modelling on learning achievement determinants of undergraduate students in Taiwan. International Journal of Education Economics and Development, 6(2), 130-141.
• Darling-Hammond, L. (2000). Teacher quality and student achievement. Education policy analysis archives, 8, 1.
• Datta, S., Beriha, G. S., Patnaik, B., & Mahapatra, S. S. (2009). Use of compromise ranking method for supervisor selection: A multi-criteria decision making (MCDM) approach. International Journal of Vocational and Technical Education, 1(1), 007-013.
• Duru, E. & Balkıs, M. (2015). Birey-çevre uyumu, aidiyet duygusu, akademik doyum ve akademik başarı arasındaki ilişkilerin analizi. Ege Eğitim Dergisi, 16(1), 122-141.
• Ehrenberg, R. G., & Brewer, D. J. (1994). Do school and teacher characteristics matter? Evidence from high school and beyond. Economics of Education Review, 13(1), 1-17.
• Ehrenberg, R. G., & Brewer, D. J. (1995). Did teachers' verbal ability and race matter in the 1960s Coleman revisited. Economics of Education Review, 14(1), 1-21.
• Engelmann, S., & Carnine, D. (1982). Theory of instruction: Principles and applications. New York: Irvington Publishers.
• Fan, X. & Chen, M. (2001). Parental involvement and students’ academic achievement: A meta-analysis. Educational Psychology Review, 13(1), 1-22.
• Fisher, D. M. (2018). Reflections on Teaching System Dynamics Modeling to Secondary School Students for over 20 Years. Systems, 6(12).
• Forrester, J. W. (1969). Urban Dynamics Cambridge. MIT Press: MA,USA
• Foster, M. A., Lambert, R., Abbott-Shim, M., McCarty, F. V. & Franze, S. (2005). A model of home learning environment and social risk factors in relation to children's emergent literacy and social outcomes. Early Childhood Research Quarterly, 20(1), 13-36.
• Green, R. (2000). Natural forces: How to significantly increase school performance in the third millennium. Unpublished manuscript.
• Gupta, S., & Gupta, A. (2013). The Systems Approach in Education. International Journal of Management, MIT College of Management, 52-55.
• Hancock, D. R. (2001). Effects of test anxiety and evaluative threat on students' achievement and motivation. The Journal of Educational Research, 94(5), 284-290.
• Hanushek, E. A. (1986). The economics of schooling: Production and efficiency in public schools. Journal of economic literature, 24(3), 1141-1177.
• Hanushek, E. A. (1996). A more complete picture of school resource policies.Review of Educational Research, 66(3), 397-409.
• Hanushek, E. A. (1996). School resources and student performance. Does money matter? The effect of school resources on student achievement and adult success, 43-73.
• Herreras, E. B. (2017). Risk low math performance PISA 2012: Impact of assistance to early childhood education and other possible cognitive variables. Acta de Investigación Psicológica, 7, 2606-2617.
• Heyneman, S. P. & Loxley, W. A. (1983). The effects of primary school quality on academic achievement across twenty-nine high- and low-income countries. American Journal of Sociology, 88(6), 1162-1194.
• Ho, W., Dey, P. K., & Higson, H. E. (2006). Multiple criteria decision-making techniques in higher education. International journal of educational management, 20(5), 319-337.
• Jones-White, D. R., Radcliffe, P. M., Huesman, R. L., & Kellogg, J. P. (2010). Redefining student success: Applying different multinomial regression techniques for the study of student graduation across institutions of higher education. Research in Higher Education, 51(2), 154-174.
• Kaya, S. (2008). The effects of student-level and classroom-level factors on elementary students’ science achievement in five countries (Yayımlanmış Doktora Tezi)). [Florida State University]. https://www.proquest.com/openview/e7167615d13ae6143852c0163cfd084d/1?pq-origsite=gscholar&cbl=18750
• Lin, C. J. & Wu, W. W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205-213.
• Lonsdale, M. (2003). Impact of School Libraries on Student Achievement: A Review of the Research. For full text: http://www.asla.org.au/research/
• Ma, X. & Willms, D. J. (2004). School disciplinary climate: Characteristics and effects on eight grade achievement. The Alberta journal of Educational Research, 50(2), 169-188.
• Marchant, G. J., Paulson, S. E., & Rothlisberg, B. A. (2001). Relations of middle school students’ perceptions of family and school contexts with academic achievement. Psychology in the Schools, 38(6), 505-519.
• Martin, L. A., (1997b), Road Map 2: An Introduction To Feedback. MIT System Dynamics In Education Project. http://static.clexchange.org/ftp/documents/roadmaps/RM2/D-4691
• Merry, J. J. (2013). Tracing the U.S. deficit in PISA reading skills to early childhood: Evidence from the United States and Canada. Sociology of Education, 86(3), 234-252.
• Mustafa, A., & Goh, M. (1996). Multi-criterion models for higher education administration. Omega, 24(2), 167-178.
• Nuhoğlu, H. (2008). İlköğretim Fen ve Teknoloji Dersinde Sistem Dinamiği Yaklaşımının Tutuma, Başarıya ve Farklı Becerilere Etkisinin Araştırılması. [Tez No:226872], (Doktora Tezi, Gazi Üniversitesi).
• Osterman, K. F. (2000). Students' need for belonging in the school community. Review of Educational Research, 70(3), 323-367.
• Özkan, R. (2004). Öğretmen yeterlikleri üzerine bazı düşünceler. Bilim ve Aklın Aydınlığında Eğitim Dergisi, 54, 46-50.
• Perkhounkova, E., Noble, J., & McLaughliin, G. W. (2006). Factors related to persistence of freshmen, freshman transfers, and nonfreshman transfer students. AIR Professional File, 99, 1-9.
• Pholphirul, P. (2017). Pre-primary education and long-term education performance: Evidence from programme for international student assessment (PISA) Thailand. Journal of Early Childhood Research, 15(4), 410-432.
• Polat, M. (2019). TIMSS 2015 matematik ve fen duyuşsal özellik modellerinin kültürlere, cinsiyete ve bölgelere göre ölçme değişmezliğinin incelenmesi (Tez No: 564356) [Yüksek Lisans Tezi Tezi, Hacettepe Üniversitesi].
• Pramanik, S., & Mukhopadhyaya, D. (2011). Grey relational analysis based intuitionistic fuzzy multi-criteria group decision-making approach for teacher selection in higher education. International Journal of Computer Applications,34(10), 21-29.
• Román, M., & Murillo, J. F. (2011). Latin America: School bullying and academic achievement. CEPAL Review No.104, 37-53.
• Sanders, W. L., Wright, S. P., & Horn, S. P. (1997). Teacher and classroom context effects on student achievement: Implications for teacher evaluation.Journal of personnel evaluation in education, 11(1), 57-67.
• Sezen, H. K., & Günal, M. (2009), Yöneylem Araştırmasında Benzetim, Ekin Yayınevi, Bursa.
• Smith, P. K. (2006). Tackling violence in schools: A European perspective. C. G. Violencereduction in schools – How to make a difference (s. 11-22). Strasbourg: Council of Europe.
• Snyder, K. J., Acker-Hocevar, M., & Snyder, K. M. (2008). Living on the edge of chaos: Leading schools into the global age. ASQ Quality Press.
• Sterman, J. D. (2000), Business Dynamics Systems Thinking And Modelling In A Complex World, Mcgraw-Hill, New York.
• Suh-Ruu, O. & Reynolds, A. J. (2004). Preschool education and school completion, encyclopedia on early childhood development. Centre of Childhood Development.
• Walberg, H. J., & Paik, S. J. (2000). Effective Educational Practices. Educational Practices Series-3.
• Wang, M. T., Degol, J. V & Ye, F. (2015). Math achievement is important, but task values are critical, too: examining the intellectual and motivational factors leading to gender disparities in STEM careers. Frontiers in psychology, 6(36).
• Wenglinsky, H. (2001). Teacher classroom practices and student performance: How schools can make a difference. ETS Research Report Series, 2001(2).
• Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary educational psychology, 25(1), 68-81.
• Wittrock, M. C., & American Educational Research Association. (1986). Handbook of research on teaching: a project of the American Educational Research Association. Macmillan; Collier-Macmillan.
• Wolstenholme, E. F. (1990). System enquiry: a system dynamics approach. John Wiley & Sons, Inc.. Chichester, England.
• Yatağan, M. (2014). Fen ve teknoloji dersi öğretim programının öğrenci ve öğretmen özelliklerine göre değerlendirilmesi: TIMSS 2007 ve 2011 verileri ile bir durum analizi (Tez No: 366252) [Doktora Tezi, Gazi Üniversitesi].