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Comparative analysis of algorithms with data mining methods for examining attitudes towards STEM fields

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Abstract

Examining students’ attitudes towards STEM (science, technology, engineering, and mathematics) fields starting from middle school level is important in their career choices and future planning. However, there is a need to investigate which variables affect students’ attitudes towards STEM. Here, we aimed to estimate middle school students’ attitudes towards STEM with data mining algorithms using classification analysis. Data mining is one of the data analysis methods used successfully in different fields, including education, in recent years. 600 middle school students from different grade levels selected from various districts of Istanbul province participated in the study. The data collection tools of the research are the STEM Attitude Scale and Personal Information Form. The data obtained from the Personal Information Form is about the students’ school type, grade level, gender, academic achievement, mother and father occupation, education level of father and mother. According to the results of the research, the K-Star algorithm from the lazy group and the Random Tree algorithm from the trees group performed the best results in classifying data. According to the decision tree technique, the dominant factor influencing middle school students’ attitudes towards STEM fields is the grade levels. Besides, the factors that the K-Star algorithm finds important after grade level variable in classification are mother occupation and academic achievement level. It is hoped that this study will enlighten future research on setting an example for the use of data mining methods in educational research and determining the factors that affect students’ attitudes towards STEM fields.

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Data Availability

Data is available upon request from the corresponding author.

References

  • Acar, D., Tertemiz, N., & Taşdemir, A. (2019). The relationship between mathematics and science problem solving skills and achievements of students who were being educated with STEM. Bartın University Journal of Educational Research, 3(2), 12–23

    Google Scholar 

  • Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52, 27–58

    Google Scholar 

  • Akçapınar, G., & Coşgun, E. (2019). Öğrencilerin Stem Eğitimi Tercihlerinin Veri Madenciliği Yaklaşımı Ile Tahmin Edilmesi. Eğitim Teknolojisi Kuram ve Uygulama, 9(1), 73–88

    Google Scholar 

  • Akgündüz, D., Ertepınar, H., Ger, A. M., Kaplan Sayı, A., & Türk, Z. (2015). The report of STEM education workshop: An assessment on STEM education in Turkey. Retrieved July 22, 2020, http://www.aydin.edu.tr/belgeler/IAU-STEM-Egitimi-Turkiye-Raporu-2015.pdf

  • Akgündüz, D., Aydeniz, M., Çakmakçı, G., Çavaş, B., Çorlu, M. S., Öner, T., & Özdemir, S. (2015). STEM Education Turkey Report: Is it a necessity or today’s fashion? Aydin Univesity, Istanbul. https://www.aydin.edu.tr/

  • Aksoy, E., Narli, S., & Aksoy, M. A. (2018). Examination of mathematically gifted students using data mining techniques in terms of some variables. International Journal of Research in Education and Science, 4(2), 471–485

  • Alacaci, C., & Erbaş, A. K. (2010). Unpacking the inequality among Turkish schools: Findings from PISA 2009. International Journal of Educational Development, 30(2), 182–192

    Google Scholar 

  • Alan, M. A. (2012). Data Mining and an application on graduate students’ data. DPUJSS, 33, 165–174

    Google Scholar 

  • Allen, A. (2016). Don’t fear STEM-you already teach it. School Age/ After School Exchange, September/October 56–59

  • Almasri, F., Hewapathirana, G. I., Ghaddar, F., Lee, N., & Ibrahim, B. (2021). Measuring attitudes towards biology major and non-major: Effect of students’ gender, group composition, and learning environment.PloS one, 16(5), e0251453

  • Almasri, F. (2022). The impact of e-learning, gender-groupings and learning pedagogies in biology undergraduate female and male students’ attitudes and achievement.Education and Information Technologies,1–52

  • Altaş, D., & Gülpinar, V. (2012). A comparison of classification performances of the decision trees and the artifıcial neural networks: European unıon. Trakya University Journal of Social Science, 14(1), 1–22

    Google Scholar 

  • Altunel, M. (2018). STEM eğitimi ve Türkiye: fırsatlar ve riskler. Seta Perspektif, 207, 1–7

    Google Scholar 

  • Anaya, L., Stafford, F. P., & Zamarro, G. (2017). Gender gaps in math performance, perceived mathematical ability and college stem education: The role of parental occupation. University of Arkansas, Department of Education Reform (EDRE), EDRE Working Paper No. 2017–2021

  • Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2012). Science aspirations, capital, and family habitus: How families shape children’s engagement and identification with science. American Educational Research Journal, 49(5), 881–908

    Google Scholar 

  • Aydın, G., Saka, M., & Guzey, S. (2017). Science, technology, engineering, mathematic (STEM) attitude levels in grades 4th – 8th. Mersin University Journal of the Faculty of Education, 13(2), 787–802

    Google Scholar 

  • Azgın, A. O., & Şenler, B. (2019). STEM in primary school: Students’ career interest and attitudes. Journal of Computer and Education Research Year, 7(13), 213–232

    Google Scholar 

  • Azizoğlu, N., & Çetin, G. (2009). 6 ve 7. sınıf öğrencilerinin öğrenme stilleri, fen dersine yönelik tutumları ve motivasyonları arasındaki ilişki. Kastamonu Eğitim Dergisi, 17(1), 171–182

    Google Scholar 

  • Balçın, M. D., Çavuş, R., & Topaloğlu, M. Y. (2018). Investigation of secondary school students’ attitudes towards STEM and their İnterest towards professions in STEM fields. Asian Journal of Instruction, 6(2), 40–62

    Google Scholar 

  • Berberoğlu, G. (2007). Türk bakış açısından PISA araştırma sonuçları [The results of PISA survey from Turkish perspective]. Retrieved from http://www.konrad.org.tr/Egitimturk/07girayberberoglu.pdf

  • Bircan, M. A., & Köksal, Ç. (2020). Investigation of STEM attitudes and STEM career interests of gifted students. Turkish Journal of Primary Education, 5(1), 16–32

    Google Scholar 

  • Bramer, M. (2007). Principles of data mining. London: Springer

    MATH  Google Scholar 

  • Brown, R., Brown, J., Reardon, K., & Merrill, C. (2011). Understanding STEM: Current perceptions. Technology and Engineering Teacher, 70(6), 5–9

    Google Scholar 

  • Bulut, T. (2020). Ortaokul öğrencilerinin STEM tutumlarının farklı değişkenler açısından incelenmesi [Examination of STEM attitudes of secondary school students in terms of different variables]. Asian Journal of Instruction, 8(2), 17–32

    Google Scholar 

  • Buxton, C. A. (2001). Modeling science teaching on science practice? Painting a more accurate picture through an ethnographic lab study. Journal of Research in Science Teaching, 38(4), 387–407

    Google Scholar 

  • Bybee, R. W. (2010). Advancing STEM Education: A 2020 Vision. Technology and Engineering Teacher, 70, 30–35

    Google Scholar 

  • Chachashvili-Bolotin, S., Milner-Bolotin, M., & Lissitsa, S. (2016). Examination of factors predicting secondary students’ interest in tertiary STEM education. International Journal of Science Education, 38(3), 366–390

    Google Scholar 

  • Charles, G. W. (2011). Parental perceptions of school quality in public and private schools (doctoral dissertation). Retrieved from ProQuest Dissertations Publishing., 3447723

  • Charlesworth, T. E., & Banaji, M. R. (2019). Gender in science, technology, engineering, and mathematics: Issues, causes, solutions. Journal of Neuroscience, 39(37), 7228–7243

    Google Scholar 

  • Chavez, L. F. (2001). Access to Advanced Math for Latino High School Graduates: The Role of Gate Keeping Math Courses. Berkeley: University of California

    Google Scholar 

  • Chevalier, A., & Lanot, G. (2002). The relative effect of family characteristics and financial situation on educational achievement. Education Economics, 10(2), 165–181

    Google Scholar 

  • Choi, N., & Chang, M. (2009). Performance of middle school students comparing U.S and Japanese inquiry-based science practices in middle schools. Middle Grades Research Journal, 6(1), 29–47

    Google Scholar 

  • Christensen, R., Knezek, G., & Tyler-Wood, T. (2015). Gender differences in high school student dispositions toward science, technology, engineering, and mathematics careers. Journal of Computers in Mathematics and Science Teaching, 34(4), 395–408

    Google Scholar 

  • Corlu, M. S., Capraro, R. M., & Capraro, M. M. (2014). Introducing STEM education: implications for educating our teachers for the age of innovation. Egitim ve Bilim, 39(171), 74–85

    Google Scholar 

  • Crano, W. D., & Prislin, R. (2006). Attitudes and persuasion. Annu Rev Psychol, 57, 345–374

    Google Scholar 

  • Crisp, R. J., & Turner, R. N. (2007). Essential social psychology. London, Thousand Oaks, New Delhi: Sage Publications

    Google Scholar 

  • Cunningham, B. C., & Hoyer, K. M. (2015). Gender differences in science, technology, engineering

  • and mathematics (STEM) interest, credits earned, and NAEP performance in the 12th grade Retrieved from https://nces.ed.gov/pubs2015/2015075.pdf

  • Cvencek, D., Meltzoff, A. N., & Greenwald, A. G. (2011). Math–gender stereotypes in elementary school children. Child Development, 82(3), 766–779

    Google Scholar 

  • Dasgupta, N., & Stout, J. G. (2014). Girls and women in science, technology, engineering, and mathematics: STEMing the tide and broadening participation in STEM careers. Policy Insights from the Behavioral and Brain Sciences, 1(1), 21–29

    Google Scholar 

  • David, S. K., Saeb, A. T., & Al Rubeaan, K. (2013). Comparative analysis of data mining tools and classification techniques using weka in medical bioinformatics. Computer Engineering and Intelligent Systems, 4(13), 28–38

    Google Scholar 

  • Diekman, A. B., Brown, E. R., Johnston, A. M., & Clark, E. K. (2010). Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers. Psychological science, 21(8), 1051–1057

    Google Scholar 

  • Dong-Peng, Y., Jin-Lin, L., Lun, R., & Chao, Z. (2008, June). Applications of data mining methods in the evaluation of client credibility. In Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance (pp. 35–43)

  • Eddy, S. L., Brownell, S. E., & Wenderoth, M. P. (2014). Gender gaps in achievement and participation in multiple introductory biology classrooms. CBE—Life Sciences Education, 13(3), 478–492

    Google Scholar 

  • Elliott, B., Oty, K., McArthur, J., & Clark, B. (2001). The effect of an interdisciplinary algebra/science course on students’ problem solving skills, critical thinking skills and attitudes towards mathematics. International Journal of Mathematical Education in Science and Technology, 32(6), 811–816

    Google Scholar 

  • Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in

  • mathematics: a meta-analysis,Psychological Bulletin, 136 (1),103–127

  • Emel, G. G., & Taşkın, Ç. (2005). Decision trees in data mining and a sales analysis application. Eskişehir Osmangazi University Journal of Social Sciences, 6(2), 221–239

    Google Scholar 

  • Fidan, N., & Erden, M. (1992). Eğitime giriş. Ankara: Feryal Matbaacılık

    Google Scholar 

  • Freedman, J. L., Sears, D. O., & Carlsmith, J. M. (1989). Social Psychology. (Translated by: Ali Dönmez). Istanbul: Ara Publications

  • Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the national academy of sciences, 111(23), 8410–8415

  • Furner, J. M., & Kumar, D. D. (2007). The mathematics and science integration argument: A stand for teacher education. Eurasia Journal of Mathematics Science & Technology Education, 3(3), 185–189

    Google Scholar 

  • Gay, L. R., & Airasian, P. (2000). Educational research (6th ed.).). New York: Prentice-Hall

    Google Scholar 

  • Gonzalez, H. B., & Kuenzi, J. J. (2012). Science, technology, engineering, and mathematics (STEM) education: A primer. Congressional Research Service, Library of Congress. http://www.fas.org/sgp/crs/misc/R42642.pdf

  • Gorunescu, F. (2011). Data mining concepts, models and techniques. Berlin: Springer, Deutschland

    MATH  Google Scholar 

  • Göktepe Yıldız, S., & Göktepe Körpeoğlu, S. (2020). Ortaokul Öğrencilerinin STEM Mesleklerine Yönelik İlgilerinin Eğitsel Veri Madenciliği İle İncelenmesi. Jass Studies-The Journal of Academic Social Science Studies, 83, 89–106

    Google Scholar 

  • Gul, D., Cetin, I., & Ozden, M. Y. (2022). A scale for measuring middle school students’ attitudes toward programming. Computer Applications in Engineering Education, 30(1), 251–258

    Google Scholar 

  • Gülhan, F., & Sahin, F. (2016). The effects of science-technology-engineering-math (STEM) integration on 5th grade students’ perceptions and attitudes towards these areas. Journal of Human Sciences, 13(1), 602–620

    Google Scholar 

  • Günbatar, M. S., & Bakırcı, H. (2019). STEM teaching intention and computational thinking skills of pre-service teachers. Education and Information Technologies, 24(2), 1615–1629

    Google Scholar 

  • Gürler, M. (2020). Devlet Okuluyla Özel Okul Arasındaki Farklar. Kapadokya Eğitim Dergisi, 1(1), 1–6

    Google Scholar 

  • Guzey, S. S., Harwell, M., & Moore, T. (2014). Development of an instrument to assess attitudes toward science, technology, engineering, and mathematics (STEM). School Science and Mathematics, 114(6), 271–279

    Google Scholar 

  • Hakkinen, I., Kirjavainen, T., & Uusitalo, R. (2003). School resources and student achievement revisited: New evidence from panel data. Economics of Education Review, 22, 329–335

    Google Scholar 

  • Hammouri, H. (2004). Attitudinal and motivational variables related to mathematics achievement in Jordan: Findings from the Third International Mathematics and Science Study (TIMSS). Educational Research, 46(3), 241–257

    Google Scholar 

  • Han, J., & Kamber, M. (2001). Data mining: concepts and techniques. San Francisco: Morgan Kaufmann

    MATH  Google Scholar 

  • Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S., & Hyde, J. S. (2012). Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention. Psychological science, 23(8), 899–906

    Google Scholar 

  • Hartung, P. J., Porfeli, E. J., & Vondracek, F. W. (2005). Child vocational development: A review and reconsideration. Journal of vocational behavior, 66(3), 385–419

    Google Scholar 

  • Hartzler, D. S. (2000). A meta-analysis of studies conducted on integrated curriculum programs and their effects on student achievement. Doctoral dissertation. Indiana University

  • Herdem, K., & Ünal, İ. (2018). Analysis of studies about STEM education: A Meta-Synthesis study. Marmara University Atatürk Education Faculty Journal of Educational Sciences, 48, 145–163

    Google Scholar 

  • Hiğde, E., Aktamış, H., Arabacıoğlu, T., Şen, H. C., Özen Ünal, D., & Yazıcı, E. (2020). Investigation of teachers’ and preservice teachers’ attitudes towards stem fields and stem teaching orientations in terms of different variables. Uşak University Journal of Educational Research, 6(1), 34–56

    Google Scholar 

  • Ho, M. T., La, V. P., Nguyen, M. H., Pham, T. H., Vuong, T. T., Vuong, H. M., & Vuong, Q. H. (2020). An analytical view on STEM education and outcomes: Examples of the social gap and gender disparity in Vietnam. Children and Youth Services Review, 119, 105650

    Google Scholar 

  • Honey, M., Pearson, G., Schweingruber, H., & of Engineering and National Research Council. (2014). National Academy (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research. Washington D.C.: The National Academies Press

  • Hullermeier, E. (2005). Experience-based decision making: a satisficing decision tree approach. IEEE Transactions on Systems Man and Cybernetics-Part A: Systems and Humans, 35(5), 641–653

    Google Scholar 

  • Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers is an imprint of Elsevier

  • Idil, F. H., Narli, S., & Aksoy, E. (2016). Using data mining techniques examination of the middle school students’ attitude towards mathematics in the context of some variables. International Journal of Education in Mathematics Science and Technology, 4(3), 210–228

    Google Scholar 

  • İnce, K., Mısır, M. E., Küpeli, M. A., & Fırat, A. (2018). Examining the effect of stem-based approach on the problem solving ability and academic success of students in teaching the enigma of the earth’s crust unit of the 5th grade life sciences course. Journal of STEAM Education, 1(1), 64–78

    Google Scholar 

  • Jain, Y. K., Yadav, V. K., & Panday, G. S. (2011). An efficient association rule hiding algorithm for privacy preserving data mining. International Journal On Computer Science And Engineering, 3(7), 2792–2798

    Google Scholar 

  • Kalmegh, S. (2015). Analysis of weka data mining algorithm reptree, simple cart and randomtree for classification of indian news. International Journal of Innovative Science Engineering & Technology, 2(2), 438–446

    Google Scholar 

  • Kandemir, Y. (2015). Devlet Okulu Öğretmenlerinin Özel Okullara İlişkin Tutumlarının İncelenmesi, Yüksek Lisans Tezi. İstanbul: Yeditepe Üniversitesi Eğitim Bilimleri Enstitüsü

    Google Scholar 

  • Karışan, D., & Yurdakul, Y. (2017). The effects of microprocessors based science technology engineering and mathematics (STEM) ınvestigations on 6th grade students’ attitudes towards these subject areas. Adnan Menderes University Faculty of Education Journal of Education Sciences, 8(1), 37–52

    Google Scholar 

  • Kaya, M., & Özel, S. A. (2014). Açık kaynak kodlu veri madenciliği yazılımlarının karşılaştırılması.Akademik Bilişim,1–8

  • Kennedy, J., Quinn, F., & Taylor, N. (2016). The school science attitude survey: a new instrument for measuring attitudes towards school science. International Journal of Research & Method in Education, 39(4), 422–445

    Google Scholar 

  • Kennedy, T. J., & Odell, M. R. L. (2014). Engaging students in STEM education. Science Education International, 25(3), 246–258

    Google Scholar 

  • Ketenci, T., Leroux, A., & Renken, M. (2020). Beyond student factors: A study of the impact on STEM career attainment. Journal for STEM Education Research, 3(3), 368–386

    Google Scholar 

  • Kiefer, A. K., & Sekaquaptewa, D. (2007). Implicit stereotypes, gender identification, and math-related outcomes: A prospective study of female college students. Psychological Science, 18(1), 13–18

    Google Scholar 

  • Kind, P. M., Jones, K., & Barmby, P. (2007). Developing attitudes towards science measures. International Journal of Science Education, 27(7), 871–893

    Google Scholar 

  • Kiray, S. A., Gok, B., & Bozkir, A. S. (2015). Identifying the factors affecting science and mathematics achievement using data mining methods. Journal of Education in Science Environment and Health, 1(1), 28–48

    Google Scholar 

  • Knezek, G., Christensen, R., Tyler-Wood, T., & Periathiruvadi, S. (2013). Impact of environmental power monitoring activities on middle school student perceptions of STEM. Science Education International, 24(1), 98–123

    Google Scholar 

  • Korkmaz, F. (2018). STEM Education and its reflection on the secondary school science lesson draft curriculum. Pegem Eğitim ve Öğretim Dergisi, 8(3), 439–468. https://doi.org/10.14527/pegegog.2018.018

    Article  Google Scholar 

  • Köğce, D., Yıldız, C., Aydın, M., & Altındağ, R. (2009). Examining elementary school students‟ attitudes towards mathematics in terms of some variables. Procedia-Social and Behavioral Sciences, 1(1), 291–295

    Google Scholar 

  • Kumar, S. A., & Vijayalakshmi, M. N. (2011). Implication of classification techniques in predicting student’s recital. Int J Data Mining Knowl Manage Process (IJDKP), 1(5), 41–51

    Google Scholar 

  • Lacey, T. A., & Wright, B. (2009). Employment Outlook: 2008-18-Occupational employment projections to 2018. Monthly Lab Rev, 132, 82

    Google Scholar 

  • Legewie, J., & DiPrete, T. A. (2012). High School Environments, STEM Orientations, and the Gender Gap in Science and Engineering Degrees. Sociology of Education, 87, 259–280. https://doi.org/10.1177/0038040714547770

    Article  Google Scholar 

  • Lent, R., Brown, S., & Hackett, G. (2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology, 47(1), 36–49

    Google Scholar 

  • Lent, R. W., Sheu, H. B., Singley, D., Schmidt, J. A., Schmidt, L. C., & Gloster, C. S. (2008). Longitudinal relations of self-efficacy to outcome expectations, interests, and major choice goals in engineering students. Journal of Vocational Behavior, 73(2), 328–335

    Google Scholar 

  • Liu, F. (2008). Impact of online discussion on elementary teacher candidates’ anxiety towards teaching mathematics. Education, 128(4), 614–630

    Google Scholar 

  • Luna, J. M., Castro, C., & Romero, C. (2017). MDM tool: A data mining framework integrated into Moodle. Computer Applications in Engineering Education, 25(1), 90–102

    Google Scholar 

  • Özan, M. B. (2003). Öğrencilerin sınav kaygılarının öğrenmeleri üzerindeki etkileri. Fırat Üniversitesi Doğu Araştırmaları Dergisi, 1(3), 64–70

    Google Scholar 

  • Mahoney, M. P. (2009). Student attitude toward STEM: Development of an instrument for high school STEM-based programs. Unpublished PhD thesis, The Ohio State University

  • Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877–907

    Google Scholar 

  • Manjarres, A. V., Sandoval, L. G. M., & Suárez, M. S. (2018). Data mining techniques applied in educational environments: Literature review.Digital Education Review, (33),235–266

  • Marjoribanks, K. (1979). Families and their learning environments: An empirical analysis. London: Routledge & Kegan Paul

    Google Scholar 

  • Mehalik, M. M., Doppelt, Y., & Schunn, C. D. (2008). Middle-school science through design-based learning versus scripted inquiry: Better overall science concept learning and equity gap reduction. Journal of Engineering Education, 97(1), 71–85

    Google Scholar 

  • Moakler, M. W. Jr., & Kim, M. M. (2014). College major choice in STEM: Revisiting confidence and demographic factors. The Career Development Quarterly, 62(2), 128–142

    Google Scholar 

  • Montt, G. (2011). Private schools: Who benefits? PISA in Focus, 7

  • Morrison, J. (2006). Attributes of STEM education: The student, the school, the classroom. TIES (Teaching Institute for Excellence in STEM), 20, 2–7

    Google Scholar 

  • National Academy of Engineering (NAE) & National Research Council (NRC). (2014). STEM Integration in K-12 education: Status, prospects, and an agenda for research. Washington, DC: The National Academies Press

    Google Scholar 

  • National Science Foundation (2003). New formulas for America’s workforce: Girls in science and engineering. Retrieved June 16, 2020, from https://www.nsf.gov/pubs/2003/nsf03207/nsf03207_1.pdf

  • National Science Foundation. (2011). Empowering the nation through discovery and innovation NSF strategic plan for fscal year 2011–2016. Washington: National Science Foundation. https://www.nsf.gov/news/strategicplan/nsfstrategicplan_2011_2016.pdf

    Google Scholar 

  • Natural Sciences and Engineering Research Council of Canada (2010). Women in science and engineering in Canada. Retrieved May 12, 2021, from https://www.nserc-crsng.gc.ca/_doc/Reports-Rapports/Women_Science_Engineering_e.pdf

  • Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications. USA: Academic Press

    MATH  Google Scholar 

  • Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math = male, me = female, therefore math∕=me. Journal of personality and social psychology, 83(1), 44

    Google Scholar 

  • Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N. M., Devos, T., Ayala, A., & Gonsalkorale, K. (2009). National differences in gender–science stereotypes predict national sex differences in science and math achievement. Proceedings of the National Academy of Sciences, 106(26), 10593–10597

  • OECD. (2015). OECD Science, Technology, and Industry Scoreboard 2015. Innovation for Growth and

  • Society,OECD, Paris

  • Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049–1079

    Google Scholar 

  • Özcan, H., & Koca, E. (2019). The impact of teaching the subject “Pressure” with STEM approach on the academic achievements of the secondary school 7th grade students and their attitudes towards STEM. Education ve Science, 44(198), 201–227

    Google Scholar 

  • Özgüven, İ. E. (2000). Psikolojik testler [Psychological tests]. Ankara, Turkey: PDREM Press

    Google Scholar 

  • Ozkan, G., & Topsakal, U. U. (2019). Exploring the effectiveness of STEAM design processes on middle school students’ creativity.International Journal of Technology and Design Education,1–22

  • Papanastasiou, C. (2000). Effects of Attitudes and Beliefs on Mathematics Achievement. Studies in educational evaluation, 26(1), 27–42

    Google Scholar 

  • Parcel, T. L., & Dufur, J. M. (2001). Capital at home and at school: Effects on student achievement. Social Forces, 79(3), 881–911

    Google Scholar 

  • Peralta, C., Caspary, M., & Boothe, D. (2013). Success factors impacting Latina/o persistence in higher education leading to STEM opportunities. Cultural Studies of Science Education, 8(4), 905–918

    Google Scholar 

  • Perie, M., Vanneman, A., & Goldstein, A. S. (2005). Student achievement in private schools: Results from NAEP 2000–2005 (NCES 2006–459). Washington, DC: U.S. Department of Education, National Center for Education Statistics

    Google Scholar 

  • Pfahringer, B. (2010). Random model trees: an effective and scalable regression method. (Working paper 03/2010). Hamilton, New Zealand: University of Waikato, Department of Computer Science

  • Regner, I., Steele, J. R., Ambady, N., Thinus-Blanc, C., & Huguet, P. (2014). Our future scientists:a

  • review of stereotype threat in girls from early elementary school to middle school,Revue Internationale de Psychologie Sociale, 27(3),13–51

  • Robinson, M. (2003). Student enrollment in high school AP sciences and calculus: How does it correlate with STEM careers? Bulletin of Science Technology & Society, 23(4), 265–273

    Google Scholar 

  • Rogers, C., & Portsmore, M. (2004). Bringing engineering to elementary school. Journal of STEM Education: Innovations and Research, 5(3), 17–28

    Google Scholar 

  • Romero, C., & Ventura, S. (2017). Educational data science in massive open online courses. WIREs Data Mining Knowledge Discovery, in press

  • Root-Bernstein, R., Van Dyke, M., Peruski, A., & Root-Bernstein, M. (2019). Correlation between tools for thinking; arts, crafts, and design avocations; and scientific achievement among STEMM professionals. Proceedings of the National Academy of Sciences, 116(6), 1910–1917. https://doi.org/10.1073/pnas.1807189116

  • Rozek, C. S., Ramirez, G., Fine, R. D., & Beilock, S. L. (2019). Reducing socioeconomic disparities in the STEM pipeline through student emotion regulation. Proceedings of the National Academy of Sciences, 116(5), 1553–1558

  • Sanders, M. (2009). Stem, stem education, stemmania. The Technology Teacher, 68(4), 20–26

    Google Scholar 

  • Sarı, U., Pektaş, H. M., Şen, Ö. F., & Çelik, H. (2022). Algorithmic thinking development through physical computing activities with Arduino in STEM education.Education and Information Technologies,1–21

  • Savaş, E., & Duru, A. (2005). Lise Birinci Sınıflar Arasında Matematik Başarısında Ve Matematiğe Karşı Olan Tutumdaki Cinsiyet Farklılığı. Eurasian Journal of Educational Research (EJER), 19, 263–271

    Google Scholar 

  • Savaş, E., Taş, S., & Duru, A. (2010). Factors Affecting Students’ Achievement in Mathematics. Inonu University Journal of the Faculty of Education (INUJFE), 11(1), 113–132

    Google Scholar 

  • Şengezer, B., Ansal, A., & Bilen, Ö. (2008). Evaluation of parameters affecting earthquake damage by decision tree techniques. Natural hazards, 47(3), 547–568

    Google Scholar 

  • Shapiro, C. A., & Sax, L. J. (2011). Major selection and persistence for women in STEM. New Directions for Institutional Research, 2011(152), 5–18

    Google Scholar 

  • Šimunović, M., Reić Ercegovac, I., & Burušić, J. (2018). How important is it to my parents? Transmission of STEM academic values: The role of parents’ values and practices and children’s perceptions of parental influences. International Journal of Science Education, 40(9), 977–995

    Google Scholar 

  • Sjøberg, S., & Schreiner, C. (2010). The ROSE Project: An Overview and Key Findings, University of Oslo, Oslo. http://www.roseproject.no/network/countries/norway/eng/nor-Sjoberg- Schreiner-overview-2010. pdf

  • Stinson, K., Harkness, S., Meyer, H., & Stallworth, J. (2009). Mathematics and science integration: Models and characterizations. School Science and Mathematics, 109(3), 153–161

    Google Scholar 

  • Sullivan, F. R. (2008). Robotics and science literacy: Thinking skills, science process skills and systems understanding. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 45(3), 373–394

    Google Scholar 

  • Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312, 1143–1144

    Google Scholar 

  • Tandrayen-Ragoobur, V., & Gokulsing, D. (2021). Gender gap in STEM education and career choices: what matters?. Journal of Applied Research in Higher Education

  • Taşdemir, C. (2009). İlköğretim ikinci kademe öğrencilerinin matematik dersine karşı tutumları: Bitlis ili örneği. Dicle Üniversitesi Ziya Gökalp Eğitim Fakültesi Dergisi, 12, 89–96

    Google Scholar 

  • Taylor, K. L. (2010). Oracle Data Mining Concepts, 11 g Release 2 (11.2) E16808-06

  • The Girl Scout Research Instıtute (2012). Generation STEM: What girls say about science, technology, engineering and mathematics. Girl Scout: Randolph. Retrieved June 14, 2021, from http://www.girlscouts.org/content/dam/girlscoutsgsusa/forms-anddocu-ments/about-girlscouts/research/generation_stem_full_report.pdf

  • Timur, S., Timur, B., Yalçınkaya-Önder, E., & Küçük, D. (2020). Attitudes of the students attending out-of-school STEM workshops towards STEM education. Journal of Theoretical Educational Science/Kuramsal Eğitimbilim Dergisi, 13(2), 334–351

    Google Scholar 

  • Tseng, K. H., Chang, C. C., Lou, S. J., & Chen, W. P. (2013). Attitudes towards science, technology, engineering and mathematics (STEM) in a project-based learning (PjBL) environment. International Journal of Technology and Design Education, 23(1), 87–102

    Google Scholar 

  • Uğraş, M. (2019). Middle school students’ interest in science-technology-engineering and mathematics (STEM) career. Electronic Turkish Studies, 14(1), 751–774

    Google Scholar 

  • Ulaş, Ö., Zorbaz, S. D., Dinçel, E. F., Kınay, G., & Coştur, R. (2017). Özel Okulda Öğrenim Görmekte Olan Lise Öğrencilerinin Meslek Seçimini Etkileyen Etmenler. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 17(1), 441–456

    Google Scholar 

  • Ulular, G. F. (1997). Ortaokul öğrencilerinin okul başarılarını etkileyen zihinsel olmayan etmenler Yayınlanmamış Yüksek Lisans Tezi. Gazi Üniversitesi Sosyal Bilimler Enstitüsü, Ankara

  • Unfried, A., Faber, M., & Wiebe, E. (2014). Gender and student attitudes toward science, technology, engineering, and mathematics. The Friday Institute for Educational Innovation at North Carolina State University, 51, 1–26. https://miso.ncsu.edu/wpcontent/uploads/2014/08/AERA-2014-paper-Student-Attitudes-Toward-STEM.pdf

  • Utgoff, P. E., Berkman, N. C., & Clouse, J. A. (1997). Decision tree induction based on efficient tree restructuring. Machine Learning, 29(1), 5–44

    MATH  Google Scholar 

  • Wang, M. T. (2012). Educational and career interests in math: A longitudinal examination of the links between classroom environment, motivational beliefs, and interests. Developmental Psychology, 48(6), 1643–1657

    Google Scholar 

  • Wang, M. T., & Degol, J. (2013). Motivational pathways to STEM career choices: Using expectancy-value perspective to understand individual and gender differences in STEM fields. Developmental Review, 33, 304–340

    Google Scholar 

  • Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081–1121

    MathSciNet  Google Scholar 

  • Watson, M., & McMahon, M. (2005). Children’s career development: A research review from a learning perspective. Journal of Vocational Behavior, 67(2), 119–132

    Google Scholar 

  • Wiebe, E., Unfried, A., & Faber, M. (2018). The relationship of STEM attitudes and career interest. EURASIA Journal of Mathematics Science and Technology Education, 14(10), 1–17

    Google Scholar 

  • Wild, E., & Lorenz, D. P. F. (2009). Familie [Family]. In E. Wild, & J. Möller (Eds.), Paedagogische Psychologie [Educational Psychology] (pp. 235–259). Berlin, Heidelberg: Springer

    Google Scholar 

  • Xie, Y., Fang, M., & Shauman, K. (2015). STEM education. Annual review of sociology, 41, 331.

  • Wyss, V. L., Heulskamp, D., & Siebert, C. J. (2012). Increasing middle school student interest in STEM careers with videos of scientists. International Journal of Environmental and Science Education, 7(4), 501–522

    Google Scholar 

  • Yalçın, N., Kılıç, B., & Atatay, Ç. A. (2016). Model suggestion for STEM activity design within the scope of the curriculum. Participatory Educational Research (PER) Journal (pp. 95–107). Special Issue

  • Yamak, H., Bulut, N., & Dündar, S. (2014). 5. sınıf öğrencilerinin bilimsel süreç becerileri ilefene karşı tutumlarına FeTEMM etkinliklerinin etkisi. Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi, 34(2), 249–265

    Google Scholar 

  • Yayan, B., & Berberoğlu, G. (2004). A re-analysis of the TIMSS 1999 mathematics assessment data of the Turkish students. Studies in Educational Evaluation, 30, 87–104

    Google Scholar 

  • Yenilmez, K., & Özabaci, N. (2003). Yatili öğretmen okulu öğrencilerinin matematik ile. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 14(14), 132–146

    Google Scholar 

  • Yerdelen, S., Kahraman, N., & Yasemin, T. A. Ş. (2016). Low socioeconomic status students’ STEM career interest in relation to gender, grade level, and STEM attitude. Journal of Turkish Science Education, 13(special), 59–74

    Google Scholar 

  • Yıldırım, B., & Türk, C. (2018). The effect of STEM applications on girls’ STEM attitude and engineering perception. Adiyaman University Journal of Social Sciences, 10(30), 842–884

    Google Scholar 

  • Yılmaz, H., Koyunkaya, M. Y., Güler, F., & Güzey, S. (2017). Fen, teknoloji, mühendislik, matematik (STEM) eğitimi tutum ölçeğinin Türkçe’ye uyarlanması [Turkish adaptation of the attitudes toward science, technology, engineering, and mathematics (STEM) education scale]. Kastamonu Eğitim Dergisi [Kastamonu Education Journal], 25(5), 1787–1800

    Google Scholar 

  • Zan, R., Brown, L., Evans, J., & Hannula, M. S. (2006). Affect in mathematics education: An introduction. Educational studies in mathematics, 63(2), 113–121

    Google Scholar 

  • Zupan, B., & Demsar, J. (2008). Open-source tools for data mining. Clinics in laboratory medicine, 28(1), 37–54

    Google Scholar 

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Acknowledgements

The authors acknowledge Yildiz Technical University Scientific Research Projects Coordination Unit under project number FKD-2021-4488.

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  1. Department of Mathematical Engineering, Yildiz Technical University, Istanbul, Turkey

    Seda Göktepe Körpeoğlu

  2. Department of Mathematics and Science Education, Biruni University, Istanbul, Turkey

    Sevda Göktepe Yıldız

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Göktepe Körpeoğlu, S., Göktepe Yıldız, S. Comparative analysis of algorithms with data mining methods for examining attitudes towards STEM fields. Educ Inf Technol 28, 2791–2826 (2023). https://doi.org/10.1007/s10639-022-11216-z

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