Identifying the Factors Affecting Science and Mathematics Achievement Using Data Mining Methods

Year 2015, , 28 - 48, 21.01.2015

S. Ahmet Kiray , Bilge Gok A. Selman Bozkir

https://doi.org/10.21891/jeseh.41216

Cited By: 12

Abstract

The purpose of this article is to identify the order of significance of the variables that affect science and mathematics achievement in middle school students. For this aim, the study deals with the relationship between science and math in terms of different angles using the perspectives of multiple causes-single effect and of multiple causes-multiple effects. Furthermore, the study examines and reveals how the reading skills, problem solving skills, cognitive and affective variables influence the math and science achievement. The data was collected from the results of Turkish students who participated in three international examinations; TIMSS 1999, PISA 2003 and PISA 2006. We analyzed the data using two data-mining methods (decision trees and clustering). The findings show that science or mathematics achievement is not influenced by the course-specific variable alone but also by other related variables. The following variables are the most important; the students’ reading and problem-solving skills affected both mathematics and science achievement; the mathematics achievement affected the science achievement; and the science achievement affected the mathematics achievement. It is also found that the affective variables have almost equally significant effects on the science and mathematics achievement.

Keywords

Integrated science and mathematics; data mining; TIMSS; PISA

References

• Akbaş, A., & Kan, A. (2007). Affective factors that influence chemistry achievement (motivation and anxiety) and the power of these factors to predict chemistry achievement-II. Journal of Turkish Science Educatıon, 4(1), 9–18.
• Alataş, B., & Akın, E. (2004). Veri madenciliğinde yeni yaklaşımlar. Paper presented at the YA-EM'2004 Congress, Adana.
• Altaş, İ. H. (1999). Bulanık mantık: Bulanıklık kavramı (Fuzzy logic: concept of fuzzy logic). Enerji, Elektrik, Elektromekanik-3e, 62, 80–85.
• Altun, M. (2008). Matematik öğretimi (Teaching mathematics). Bursa: Alfa Basım Yayım Dağıtım.
• Aypay, A., Erdoğan, E., & Sözer, M. A. (2007). Variation among schools on classroom practices in science based on TIMSS–1999 in Turkey. Journal of Research in Science Teaching, 44(10), 1417–1435.
• Baloğlu, M. (2001). Matematik korkusunu yenmek (To cope with math anxity). Educational Sciences: Theory & Practice, 1(1), 59–76.
• Bandura, A. (1995). Self-efficacy in changing societies. Cambridge: Cambridge University Press.
• Barkatsas, A, Kasimatis, K., & Gialamas, V. (2009). Learning secondary mathematics with technology: Exploring the complex interrelationship between students’ attitudes, engagement, gender and achievement. Computers and Education, 52, 562–570.
• Basista, B., & Mathews, S. (2002). Integrated science and mathematics professional development programs. School Science and Mathematics, 102(7), 359–370.
• Bassok, M., & Holyoak, K. J. (1989). Interdomain transfer between isomorphic topics in algebra and physics. Learning, Memory, and Cognition, 15(1), 153–166.
• Basson, I. (2002). Physics and mathematics as interrelated fields of thought development using acceleration as an example. International Journal of Mathematical Education in Science and Technology, 33(5), 679–690.
• Bekdemir, M. (2007). The causes of mathematics anxiety in elementary preservice teachers and proposals for decreasing mathematics anxiety. The example of faculty of Erzincan education. Erzincan Eğitim Fakültesi Dergisi, 9(2), 131–144.
• Belbase, S. (2013). Images, anxieties, and attitudes toward mathematics. International Journal of Education in Mathematics, Science and Technology, 1(4), 230-237.
• Berberoğlu, G., & Kalender, İ. (2005). Investigation of student achievement across years, school types and regions: The SSE and PISA analyses. Eğitim Bilimleri ve Uygulama, 4(7), 21–35.
• Berlin, D.F. & White, A.L. (1994). The Berlin-White Integrated Science and Mathematics Model. School Science and Mathematics, 94(1), 2–4.
• Berlin, D. F. (1991). A bibliography of integrated science and mathematics teaching and learning literature. School Science and Mathematics Association Topics for Teacher Series (No. 6). Bowling Green, OH: School Science and Mathematics Association.
• Berry, M., & Linoff, G. (2000). Mastering data mining: The art and science of customer relationship management. New York: John Wiley & Sons.
• Bilgin, İ. (2006). The effects of hands-on activities incorporating a cooperative learning approach on eighth-grade students’ science process skills and attitudes toward science. Journal of Baltic Science Education, 1(9), 27–37.
• Blanchette, I., & Dunbar, K. (2002). Representational change and analogy: How analogical inferences alter target representations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 672–685.
• Brookover, W. B., Thomas, S. & Peterson, A. (1964). Self-concept of ability and school performance. Sociology of Education, 37, 271–278.
• Bursal, M. (2008). Changes in Turkish pre-service elementary teachers’ personal science teaching efficacy beliefs and science anxieties during a science method course. Journal of Turkish Science Education, 5 (1).
• Bursal, M., & Paznokas, L. (2006). Mathematics anxiety and preservice elementary teachers’ confidence to teach mathematics and science. School Science and Mathematics, 106(4), 173–180.
• Byrne, B. M. (1996). Measuring self-concept across the life span. Issues and instrumentation. Washington, DC: American Psychological Association.
• Cahan, D. (2003). From natural philosophy to the sciences: Writing the history of nineteenth- century science. London: University of Chicago Press.
• Caston, M. (1986). Parent and student attitudes toward mathematics as they relate to third grade mathematics achievement. Research report. (Eric Document Reproduction No. ED334078).
• Chien, C., & Chen, L. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34, 280–290.
• Chui, C., Chen, Y., Kou, I., & Ku, H. C. (2009). An intelligent market segmentation system using k-means and particle swarm optimization. Expert Systems with Applications, 36(1), 4558–4565.
• Davison, D. M., Miller, K. W., & Metheny, D. L. (1995). What does integration of science and mathematics really mean? School Science and Mathematics, 95(5), 226–230.
• Dede, Y., & Yaman, S. (2008). A questionnaire for motivation toward science learning: A validity and reliability study. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education, 2(1), 19–37.
• Delen, D., Walker, G., & Kadam, A. (2005). Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligence in Medicine, 34, 113–127.
• Demir, İ., & Kılıç, S. (2009). Matematikte başarı üzerine öğrencilerin kendileriyle ilgili görüşleri (Students’ self efficacy about themselves on the math achievement). Paper peresented at the 6th Statistics Congress, Antalya.
• Dewey, J. (1969). Interest and effort in education. New York: Augustus M. Kelley Publishers.
• Dewey, J. (1933). How we think. Boston, MA: Heath and Company.
• Dotterer, A. M., McHale, S. M., & Crouter, A. C. (2009). The development and correlates of academic interests from childhood through adolescence. Journal of Educational Psychology, 101(2), 509–519.
• EARGED. (2005). Öğrenci başarısını belirleme programı (PISA–2003), ulusal ön rapor (Student achievement determining program (PISA-2003), national preliminary report). Ankara: MEB-Eğitimi Araştırma ve Geliştirme Dairesi Başkanlığı.
• EARGED. (2003). TIMSS-R: Third international mathematics and science study-repeat/ üçüncü uluslararası matematik ve fen araştırmasının tekrarı-uluslararası ölçme ve değerlendirme çalışmaları (international measurement and assessment studies). Ankara: MEB Eğitimi Araştırma ve Geliştirme Dairesi (EARGED) Yay. (http://earged.meb.gov.tr)
• Eggen, P., & Kauchak, D. (2001). Educational psychology: Windows on classrooms. New Jersey: Prentice Hall.
• Erten, S. (2008). Interests of 5th through 10th grade students toward human biology. Hacettepe University, Journal of Education, 35, 135–147.
• Friend, H. (1985). The effect of science and mathematics integration on selected seventh- grade students’ attitudes toward and achievement in science. School Science and Mathematics, 85(6), 453–461.
• Gardner, P. L., & Tamir, P. (1989). Interest in biology. Part I: A multidimensional construct. Journal of Research in Science Teaching, 26(5), 409–423.
• Girasoli, A. J., & Hannafin, R. D. (2008). Using asynchronous AV communication tools to increase academic self-efficacy. Computers and Education, 51, 1676–1682.
• Gleick, J. (1987). Chaos: Making a new science. New York: Viking Press.
• Gonzalez, E. J., & Miles, J. A. (Eds.) (2001). TIMSS 1999 user guide for the international database: IEA’s repeat of the third ınternational mathematics and science study at the eighth grade. Lynch School of Education, Boston, MA: International Study Center, Boston College.
• Gömleksiz, M. N. (2003). Validity and reliability of an attitude scale on affective domain in English course. Fırat University Journal of Social Science, 13(1), 215-226.
• Guay, F., Marsh, H. W., & Boivin, M. (2003). Academic self-concept and academic achievement: Developmental perspectives and their causal ordering. Journal of Educational Psychology, 95, 124–136.
• Güleç, S., & Alkış, S. (2003). Relations among primary school students’ course performances. İlköğretim-Online, 2 (2), 19–27.
• Güngör, A., Eryılmaz, A., & Fakıoğlu, T. (2007). The relationship of freshmen’s physics achievement and their related affective characteristics. Journal of Research in Science Teachıng, 44(8), 1036–1056.
• Harper, N. W., & Daane, C. J. (1998). Causes and reduction of math anxiety in preservice elementary teachers. Action in Teacher Education, 19(4), 29–38.
• Hembree, R. (1990). The nature, effects and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21(1), 33–46.
• Hendrickson, A. B. (1997). Predicting student success with the learning and study strategies 14. inventory (LASSI). Unpublished Master’s thesis. Iowa State University, Ames, IA.
• Hizarcı, T., Atılboz, N. G., & Salman, S. (2005). A study on the attitudes of elementary school students from two different socio-economic regions towards living organisms. Journal of Gazi Educational Faculty, 25(2), 55–69.
• House, J. D. (2004). Cognitive-motivational characteristics and science achievement of adolescent students: results from the tımss 1995 and tımss 1999 assesments. International Journal of Instructional Media, 31(4), 411-424.
• House, P. (1990). Science and mathematics: partners then… partners now. School Science and Mathematics Association Topics for Teachers Series, No. 5. Bowling Green, OH: School Science and Mathematics Association.
• İnceoğlu, M. (2004). Tutum algı iletişim (Attitude, perception, communication). Ankara: Elips Kitap Kesit Tanıtım.
• Ireland, J. D. (1987). The effect of reading performance on high school science achievement. Unpublished Master’s thesis, Curtin University of Technology, Perth, Western Australia.
• Kıray, S.A. (2010). İlköğretim ikinci kademede uygulanan fen ve matematik entegrasyonunun etkililiği, Doktora Tezi, Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.
• Kıray, S.A. (2003). İlköğretim 7. sınıflarda fen bilgisi dersinde uygulanan problem çözme stratejisinin öğrencilerin kavramları anlama ve problem çözme performansları üzerine etkisi. Yayımlanmamış Yüksek Lisans Tezi. Konya: Selçuk Üniversitesi, Fen Bilimleri Enstitüsü.
• Kıray, S.A. & Kaptan, F. (2012). The effectiveness of an integrated science and mathematics programme: Science-centred mathematics-assisted integration. Energy Education Science and Technology Part B: Social and Educational Studies, 4(2), 943-956.
• Kıray, S.A. (2012). A new model for the integration of science and mathematics: The balance model. Energy Education Science and Technology Part B: Social and Educational Studies, 4(3), 1181-1196.
• Kurt, K. & Pehlivan, M. (2013). Integrated programs for science and mathematics: review of related literature. International Journal of Education in Mathematics, Science and Technology, 1(2), 116-121.
• Labenne, W. D., & Greene, B. L. (1969). Educational implications of self concept theory. Pacific Palisades, CA: Goodyear Publishing.
• Lederman, N. G., & Niess, M. L. (1998). 5 Apples + 4 oranges=? School Science and Mathematics, 98(6), 281–284.
• Manger, T., & Eikeland, O. J. (1998). The effect of mathematics self-concept on girls’ and boys’ mathematical achievement. School Psychology International, 19(1), 5–18.
• Marsh, H. (1992). Content specificity of relations between academic achievement and academic self concept. Journal of Educational Phychology, 84(1), 34–52.
• Mataka, L.M., Cobern, W.W., Grunert, M., Mutambuki, J., & Akom, G. (2014). The effect of using an explicit general problem solving teaching approach on elementary pre-service teachers’ ability to solve heat transfer problems. International Journal of Education in Mathematics, Science and Technology, 2(3), 164-174.
• MEB (2005). İlköğretim fen ve teknoloji dersi öğretim programı ve kılavuzu (6–8.sınıflar) (Primary science and technology course teaching program and guide). Ankara: MEB yayınevi.
• McBride, J. W., & Silverman, F. L. (1991). Integrating elementary/middle school science and mathematics. School Science and Mathematics, 91(7), 285–292.
• Mehmetlioglu, D. & Ozdem, Y. (2014). Connectivity Theory at work: The referrals between science and mathematics in a science unit. International Journal of Education in Mathematics, Science and Technology, 2(1), 36-48.
• Miller, H. J., & Han, J. (2001). Geographic data mining and knowledge discovery. In H. J. Miller & J. Han (eds), Geographic data mining and knowledge discovery (pp. 3–32). London and New York: Taylor & Francis.
• Mitra, S., Pal, S. K., & Mitra, P. (2002). Data mining in soft computing framework: A survey. IEEE Transactions on Neural Networks, 13(1).
• Oliver, J. S., & Simpson, R. D. (1988). Influences of attitude toward science, achievement, motivation, and science self-concept on achievement in science: A longitudinal study. Science Education, 72, 143–155.
• Papanastasiou, E., & Zembylas, M. (2004). Differential effects of science attitudes and science achievement in Australia, Cyprus, and the USA. International Journal of Science Education, 26, 259–280.
• Perla, R. J. & Carifio, J. (2005). The nature of scientific revolutions from the vantage point of chaos theory. Science & Education, 14, 263–290.
• Polya, G. (1945). How to solve it: A new aspect of mathematical method. New Jersey: Princeton University Pres.
• Serin, O. (2004). Öğretmen adaylarının problem çözme becerisi ve fene yönelik tutum ile başarıları arasındaki ilişki (Relationship between pre-service teachers’ problem solving skills, attitude towards science and achievement). Paper presented at the 13th Educational Sciences Congress, University of İnönü, Malatya. Retrieved May 10, 2010, from http://www.pegema.net/dosya/dokuman/415.pdf
• Shelley, M. & Yildirim, A. (2013). Transfer of learning in mathematics, science, and reading among students in Turkey: A study using 2009 PISA data. International Journal of Education in Mathematics, Science and Technology, 1(2), 83-95.
• Sherman B. F., & Wither, D. P. (2003). Mathematics anxiety and mathematics achievement. Mathematics Education Research Journal, 15(2), 138–150.
• Sloan, T., Daane, C. J., & Giesen, J. (2002). Mathematics anxiety and learning styles: What is the relationship in elementary preservice teachers? School Science and Mathematics, 102(2), 84–87.
• Sloutsky, V. M., Kaminski, J. A., & Heckler, A. F. (2004). Transfer of learning between isomorphic artificial domains: Advantage for the abstract. In Proceedings of the XXVI Annual Conference of the Cognitive Science Society (pp. 1167–1172). Mahwah, NJ: Erlbaum.
• Sloutsky, V. M., Kaminski, J. A., & Heckler, A. F. (2005). The advantage of simple symbols for learning and transfer. Psychonomic Bulletin & Review, 12, 508–513.
• Sousa, D. A. (2006). How the brain learns. Thousand Oaks, CA: Corwin Press.
• Tan, P. N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Boston, MA: Addison-Wesley.
• Tang, Z., & MacLennan, J. (2005). Data mining with SQL server. Indianapolis, IN: Wiley Publishing, Inc.
• Taşdemir, M., & Taşdemir, A. (2008). A comparison of Turkish primary school students’ achievement in science and maths subjects. Journal of Qafqaz University, 22, 190–198.
• Tavşancıl, E. (2006). Tutumların ölçülmesi ve spss ile veri analizi (Measurement of attitudes and data analysis with SPSS). Ankara: Nobel Yayın Dağıtım.
• Tschannen-Moran, M. & Mcmaster, P. (2009). Sources of self-efficacy: Four professional development formats and their relationship to self-efficacy and implementation of a new teaching strategy. The Elementary School Journal, 110(2), 228-248.
• Uzun, S., Bütüner, S. E. &Yiğit, N. (2010). A comparison of the results of TIMSS 1999-2007: The most successful five countries-Turkey sample. Elementary Education Online, 9 (2), 1174-1188.
• Wang, J. (2007). A trend study of self-concept and mathematics achievement in a cross cultural context. Mathematics Education Research, 19(3), 33–47.
• Wang, J. (2005). Relationship between mathematics and science achievement at the 8th grade. International Online Journal of Science Mathematics Education, 5, 1–17.
• Wilkins, J. L. (2004). Mathematics and science self-concept: An international investigation. Journal of Experimental Education, 72, 331–346.
• Wilson, P. S. (1971). Interest and discipline in education. London: Routledge and Kegan Paul Ltd.
• Wright, M., & Corin, A. (1999). Mathematics and science. Report to the division of mathematical sciences. Arlington, VA: National Science Foundation.
• Yaman, M., Gerçek, S., & Soran, H. (2008). Analysis of biology teacher candidates’ occupational interests in terms of different variables. Hacettepe University Journal of Education, 35, 351–361.
• Yüksel-Şahin, F. (2008). Mathematics anxiety among 4th and 5th grade Turkish elementary school students. International Electronic Journal of Mathematics Education, 3(3), 179–192.
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
• Zakaria, E., & Nordin, N. M. (2008). The effects of mathematics anxiety on matriculation students as related to motivation and achievement. Eurasia Journal of Mathematics, Science & Technology Education, 4(1), 27–30.
• Zimmerman, B. J., Bonner, S., & Kovach, R. (1996). Developing self-regulated learners beyond achievement to self-efficacy. Washington, DC: American Psychological Association.