TY  - JOUR
T1  - Latent profile analysis of students' science motivation and cognitive dimensions relationships
TT  - Öğrencilerin fen motivasyonu ve bilişsel boyutları arasındaki ilişkilerin örtük profil analiziyle incelenmesi
AU  - Akın Arıkan, Çiğdem
AU  - Acar Erdol, Tuba
AU  - Çüm, Sait
PY  - 2025
DA  - April
Y2  - 2025
DO  - 10.19128/turje.1487696
JF  - Turkish Journal of Education
JO  - TURJE
PB  - Mehmet TEKEREK
WT  - DergiPark
SN  - 2147-2858
SP  - 172
EP  - 192
VL  - 14
IS  - 2
LA  - en
AB  - The aim of this study was to identify students’ motivational beliefs in science and revealed the connections between the profiles and three cognitive dimensions of science achievement through the examination of the socio-economic status (SES) and gender covariates of the profile memberships. Latent profile analysis using science motivational beliefs was conducted, and resulted in a four-profile model. The emerging profiles were named “low motivation”, “moderate motivation”, “high motivation”, and “high motivation with very high confident”. The results showed that the boys were less likely to have “high motivation” and “high motivation with very high confidence” profiles than the girls. The students with high SES were more likely to belong to the high motivation groups. The differences between the mean scores of the students in different motivation profiles were statistically significant in all other pairwise comparisons, except for the comparisons between the low and moderate motivation profiles. Our findings suggest that students’ motivation toward science should take an integrative approach to improve students' cognitive dimensions of science achievement by considering students' gender and SES.
KW  - Cognitive domains
KW  - Latent profile analysis
KW  - Science’s achievement
KW  - Science’s motivational beliefs
KW  - TIMSS
N2  - Bu çalışmanın amacı, öğrencilerin fen alanındaki motivasyonel inançlarını belirlemek ve profil üyeliklerinin sosyo-ekonomik duzey (SES) ve cinsiyet ortak değişkenlerinin incelenmesiyle ortaya konan profiller ile fen başarısının üç bilişsel alanı arasındaki iliskiyi ortaya çıkarmaktır. Bu çalışmada, fen motivasyonel inançları kullanılarak örtük profil analizi yapılmış ve dört profilli bir model ortaya çıkmıştır. Ortaya çıkan profiller “düşük motivasyon”, “orta motivasyon”, “yüksek motivasyon” ve “çok yüksek özgüvenli yüksek motivasyon” olarak adlandırılmıştır. Sonuçlar, erkek öğrencilerin kız öğrencilere kıyasla “yüksek motivasyon” ve “yüksek motivasyon ve çok yüksek özgüven” profillerine daha az sahip olduğunu göstermiştir. Yüksek SES'e sahip öğrencilerin yüksek motivasyon gruplarına ait olma olasılığının daha yüksek olduğu elde edilmiştir. Farklı motivasyon profillerinde yer alan öğrencilerin ortalama puanları arasındaki farklar, düşük ve orta motivasyon profilleri arasındaki karşılaştırmalar hariç, diğer tüm ikili karşılaştırmalarda istatistiksel olarak anlamlı çıkmıştır.  Bulgularımız, öğrencilerin fene yönelik motivasyonlarının, öğrencilerin cinsiyet ve SES'lerini dikkate alarak fen başarısının bilişsel boyutlarını geliştirmek için bütüncül bir yaklaşım benimsenmesi gerektiğini göstermektedir.
CR  - Acar, Ö. (2019). Investigation of the science achievement models for low and high achieving schools and gender differences in Turkey. Journal of Research in Science Teaching, 56(5), 649-675. https://doi.org/10.1002/tea.21517
CR  - Ainley, M., & Ainley, J. (2011). A cultural perspective on the structure of student interest in science. International Journal of Science Education, 33(1), 51-71. https://doi.org/10.1080/09500 693.2010.518640
CR  - Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions on Automatic Control, AC-19(6), 716-723. https://doi.org/10.1109/tac.1974.1100705
CR  - Akgündüz, D. (2016). A Research about the placement of the top thousand students placed in STEM fields in Turkey between the years 2000 and 2014. EURASIA Journal of Mathematics, Science and Technology Education, 12(5), 1365-1377.
CR  - Asparouhov, T., & Muthén, B. (2010). Plausible values for latent variables using Mplus. Unpublished manuscript.
CR  - Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus web notes, 21(2), 1-22.
CR  - Banchefsky, S., & Park, B. (2018). Negative gender ideologies and gender-science stereotypes are more pervasive in male-dominated academic disciplines. Social Sciences, 7(27), 1-21. https://doi.org/10.3390/socsci7020027
CR  - Berger, N., Mackenzie, E., & Holmes, K. (2020). Positive attitudes towards mathematics and science are mutually beneficial for student achievement: a latent profile analysis of TIMSS 2015. The Australian Educational Researcher, 47(3), 409-444.7. https://doi.org/10.1007/s13384-020-00379-8
CR  - Bodovski, K., Munoz, I., Byun, S. Y., & Chykina, V. (2020). Do Education System Characteristics Moderate the Socioeconomic, Gender and Immigrant Gaps in Math and Science Achievement?. International Journal of Sociology of Education, 9(2), 122-154. https://doi.org/10.17583/rise.2020.4807
CR  - Bøe, M. V., & Henriksen, E. K. (2013). Love it or leave it: Norwegian students’ motivations and expectations for post compulsory physics. Science Education, 97(4), 550-573. https://doi.org/10.1002/sce.21068
CR  - Chang, C. Y., & Cheng, W. Y. (2008). Science achievement and students’ self‐confidence and interest in science: A Taiwanese representative sample study. International Journal of Science Education, 30(9), 1183-1200. https://doi.org/10.1080/09500690701435384
CR  - Chen, S.-F., Lin, C.-Y., Wang, J.-R., Lin, S.-W., & Kao, H.-L. (2012). A cross-grade comparison to examine the context effect on the relationships among family resources, school climate, learning participation, science attitude, and science achievement based on TIMSS 2003 in Taiwan. International Journal of Science Education, 34(14), 2089-2106.  https://doi.org/10.1080/09500693.2012.701352
CR  - Curran, F. C., & Kellogg, A. T. (2016). Understanding science achievement gaps by race/ethnicity and gender in kindergarten and first grade. Educational Researcher, 45(5), 273-282. https://doi.org/10.3102/0013189X16656611
CR  - Çetin, H., & Türkan, A. (2021). The effect of augmented reality based applications on achievement and attitude towards science course in distance education process. Education and Information Technologies, 27, 1397-1415. https://doi.org/10.1007/s10639-021-10625-w
CR  - Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). L. Erlbaum Associates.
CR  - Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgely, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and Achievement Motives: Psychological and sociological approaches (pp. 75–138). W.H. Freeman and Company.
CR  - Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
CR  - Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61. https://doi.org/10.1016/j.cedpsych.2020.101859
CR  - Fleming, M. L., & Malone, M. R. (1983). The relationship of student characteristics and student performance in science as viewed by meta-analysis research. Journal of Research in Science Teaching, 20(5), 481-495. https://doi.org/10.1002/tea.3660200510
CR  - Fong, C. J., Kremer, K. P., Cox, C. H. T., & Lawson, C. A. (2021). Expectancy-value profiles in math and science: A person-centered approach to cross-domain motivation with academic and STEM-related outcomes. Contemporary Educational Psychology, 65. https://doi.org/10.1016/j.cedpsych.2021.101962
CR  - Gaspard, H., Wille, E., Wormington, S. V., & Hulleman, C. S. (2019). How are upper secondary school students’ expectancy-value profiles associated with achievement and university STEM major? A cross-domain comparison. Contemporary Educational Psychology, 58, 149-162. https://doi.org/10.1016/j.cedpsych.2019.02.005
CR  - Geesa, R. L., Izci, B., Song, H. S., & Chen, S. (2019). Exploring the roles of students’ home resources and attitudes towards science in science achievement: A comparison of South Korea, Turkey, and the United States in TIMSS 2015. Asia-Pacific Science Education, 5(17), 1-22. https://doi.org/10.1186/s41029-019-0038-7
CR  - George, R. (2006). A cross-domain analysis of change in students’ attitudes toward science and attitudes about the utility of science. International Journal of Science Education, 28(6), 571-589. https://doi.org/10.1080/09500 69050 0338755
CR  - Gonzalez, E. J. (2012). Rescaling sampling weights and selecting mini-samples from large-scale assessment databases. IERI Monograph Series: Issues and Methodologies in Large-Scale Assessments, 5, 117-134.
CR  - Gorard, S., & See, B. H. (2009). The impact of socio‐economic status on participation and attainment in science. Studies in Science Education, 45(1), 93-129. https://doi.org/10.1080/03057260802681821
CR  - Gustafsson, J. E., Nilsen, T., & Hansen-Yang, K. (2018). School characteristics moderating the relation between student socio-economic status and mathematics achievement in grade 8. Evidence from 50 countries in TIMSS 2011. Studies in Educational Evaluation, 57, 16-30. https://doi.org/10.1016/j.stueduc.2016.09.004
CR  - Hong, Z. R., & Lin, H. S. (2011). An investigation of students’ personality traits and attitudes toward science. International Journal of Science Education, 33(7), 1001-1028. https://doi.org/10.1080/09500693.2010.524949
CR  - Hu, X., Leung, F. K., & Chen, G. (2018). School, family, and student factors behind student attitudes towards science: The case of Hong Kong fourth-graders. International Journal of Educational Research, 92, 135-144. https://doi.org/10.1016/j.ijer.2018.09.014
CR  - International Association for the Evaluation of Educational Achievement (2019). TIMSS 2019 science framework. Boston College, TIMSS & PIRLS International Study Center
CR  - Jacobs, J. E., & Eccles, J. S. (2000). Parents, task values, and real-life achievement-related choices. In C. Samsone, J.M. Harackiewicz (Eds.), Intrinsic and extrinsic motivation: The search for optimal motivation and performance, (pp. 405-439). Academic Press. https://doi.org/10.1016/B978-012619070-0/50036-2
CR  - Kağıtçıbaşı, Ç. (1994). A critical appraisal of individualism and collectivism: Toward a new formulation. In U. Kim, H. C. Triandis, Ç. Kâğitçibaşi, S.-C. Choi, & G. Yoon (Eds.), Individualism and collectivism: Theory, method, and applications, (pp. 52-65). Sage Publications.
CR  - Kahraman, N., & Sungur-Vural, S. (2014). The contribution of gender, socio-economic status and socio-cultural influence to Turkish students' task value beliefs in science. Research in Education, 91(1), 30-44. https://doi.org/10.7227/rie.91.1.4
CR  - Lee, S. Y., Friedman, S., Christiaans, E., & Robinson, K. A. (2022). Valuable but costly? University students’ expectancy-value-cost profiles in introductory chemistry courses. Contemporary Educational Psychology, 69. https://doi.org/10.1016/j.cedpsych.2022.102056
CR  - Liou, P. Y., & Liu, E. Z. F. (2015). An analysis of the relationships between Taiwanese eighth and fourth graders’ motivational beliefs and science achievement in TIMSS 2011. Asia Pacific Education Review, 16(3), 433-445. https://doi.org/10.1007/s12564-015-9381-x
CR  - Liou, P. Y. (2017). Profiles of adolescents’ motivational beliefs in science learning and science achievement in 26 countries: Results from TIMSS 2011 data. International Journal of Educational Research, 81, 83-96. https://doi.org/10.1016/j.ijer.2016.11.006
CR  - Liou, P. Y., Wang, C. L., Lin, J. J., & Areepattamannil, S. (2021). Assessing students’ motivational beliefs about learning science across grade level and gender. The Journal of Experimental Education, 89(4), 605-624. https://doi.org/10.1080/00220973.2020.1721413
CR  - Liou, P. Y., Lin, Y. M., Huang, S. C., & Chen, S. (2023). Gender Differences in Science Motivational Beliefs and Their Relations with Achievement over Grades 4 and 8: A Multinational Perspective. International Journal of Science and Mathematics Education, 21(1), 233-249. https://doi.org/10.1007/s10763-021-10243-5
CR  - Liu, A. S., & Schunn, C. D. (2020). Predicting pathways to optional summer science experiences by socioeconomic status and the impact on science attitudes and skills. International Journal of STEM Education, 7(1), 1-22. https://doi.org/10.1186/s40594-020-00247-y
CR  - Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767-778. https://doi.org/10.1093/biomet/88.3.767
CR  - Ma, Y. (2022). Profiles of student science attitudes and its associations with gender and science achievement. International Journal of Science Education, 44(11), 1876-1895. https://doi.org/10.1080/09500693.2022.2101705
CR  - Marsh, H. W., & Martin, A. J. (2011). Academic self-concept and academic achievement: Relations and causal ordering. British Journal of Educational Psychology, 81(1), 59-77. https://doi.org/10.1348/000709910X503501
CR  - Marsh, H. W., & Shavelson, R. (1985). Self-concept: Its multifaceted, hierarchical structure. Educational Psychologist, 20(3), 107-123. https://doi.org/10.1207/s15326985ep2003_1
CR  - Martin, M. O., Mullis, I. V. S., Hooper, M., Yin, L., Foy, P., & Palazzo, L. (2016). Creating and interpreting the TIMSS 2015 context questionnaire scales. In M. O. Martin, I. V. S. Mullis, & M. Hooper (Eds.), Methods and procedures in TIMSS 2015 (pp. 558–869). Boston College.
CR  - Miscevic-Kadijevic, G. (2015). TIMSS 2011: Relationship between self-confidence and cognitive achievement for Serbia and Slovenia. Revista Electrónica de Investigación Educativa, 17(3), 109-115.
CR  - Moakler, M. W., & Kim, M. M. (2014). College major choice in STEM: Revisiting confidence and demographic factors. The Career Development Quarterly, 62(2), 128-142. https://doi.org/10.1002/j.2161-0045.2014.00075.x
CR  - MoNE. (2018). Fen Bilimleri Dersi Öğretim Programı (İlkokul ve Ortaokul 3,4,5,6,7, ve 8. Sınıflar) [Science Curriculum (Primary and Secondary Schools 3,4,5,6,7, and 8th Grades)]. Retrieved October 28, 2021, from http://mufredat.meb.gov.tr/ProgramDetay.aspx?PID=325
CR  - Mullis, I. V. S., Martin, M. O., Goh, S., & Cotter, K. (Eds.). (2016). TIMSS 2015 encyclopedia: Education policy and curriculum in mathematics and science. Retrieved October 2, 2021, from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/timss2015/encyclopedia/
CR  - Mullis, I. V. S., & Martin, M. O. (Eds.). (2017). TIMSS 2019 Assessment Frameworks. Retrieved October 2, 2021, from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/timss2019/frameworks/
CR  - Mullis, I. V. S., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (2020). TIMSS 2019 International Results in Mathematics and Science. Retrieved October 1, 2021, from Boston College, TIMSS & PIRLS International Study Center website: https://timssandpirls.bc.edu/timss2019/international-results/
CR  - Muthén, B., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24(6), 882–891. https://doi.org/10.1111/j.1530-0277.2000.tb02070.x
CR  - Muthen, L.K. and Muthen, B.O. (1998-2017). Mplus User’s Guide. (8th Ed.). Muthén & Muthén
CR  - Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535-569. https://doi.org/10.1080/10705510701575396
CR  - Perez, T., Wormington, S. V., Barger, M. M., Schwartz‐Bloom, R. D., Lee, Y. K., & Linnenbrink‐Garcia, L. T. (2019). Science expectancy, value, and cost profiles and their proximal and distal relations to undergraduate science, technology, engineering, and math persistence. Science Education, 103(2), 264-286, https://doi.org/10.1002/sce.21490.
CR  - Radišić, J., Selleri, P., Carugati, F., & Baucal, A. (2021). Are students in Italy really disinterested in science? A person‐centered approach using the PISA 2015 data. Science Education, 105(2), 438-468. https://doi.org/10.1002/sce.21611
CR  - Rosenzweig, E. Q., Wigfield, A., & Eccles, J. S. (2022). Beyond utility value interventions: The why, when, and how for next steps in expectancy-value intervention research. Educational Psychologist, 57(1), 11-30. https://doi.org/10.1080/00461520.2021.1984242
CR  - Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. https://doi.org/10.1214/aos/1176344136
CR  - Seçgin, T., & Sungur, S. (2021). Investigating the science attitudes of students from low socioeconomic status families: The impact of problem‐based learning. Biochemistry and Molecular Biology Education, 49(2), 228-235. https://doi.org/10.1002/bmb.21447
CR  - Snodgrass Rangel, V., Vaval, L., & Bowers, A. (2020). Investigating underrepresented and first‐generation college students' science and math motivational beliefs: A nationally representative study using latent profile analysis. Science education, 104(6), 1041-1070. https://doi.org/10.1002/sce.21593
CR  - Spencer, S. J., Steele, C. M. & Quinn, D. M. (1999). Stereotype threat and women's math performance. Journal of Experimental Social Psychology, 35(1), 4-28. https://doi.org/10.1006/jesp.1998.1373
CR  - Tein, J. Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640-657. https://doi.org/10.1080/10705511.2013.824781
CR  - Tonks, S. M., Wigfield, A., & Eccles, J. S. (2018). Expectancy value theory in cross-cultural perspective: What have we learned in the last 15 years? G.A.D. Liem, D. McInerney (Eds.), Recent advances in sociocultural influences on motivation and learning: Big theories revisited (2nd ed.), Information Age Publishers.
CR  - Topçu, M. S., Erbilgin, E., & Arikan, S. (2016). Factors predicting Turkish and Korean students’ science and mathematics achievement in TIMSS 2011. Eurasia Journal of Mathematics, Science & Technology Education, 12(7), 1711-1737. https://doi.org/10.12973/eurasia.2016.1530a
CR  - Wan, Z. H. (2021). Exploring the effects of intrinsic motive, utilitarian motive, and self-efficacy on students’ science learning in the classroom using the expectancy-value theory. Research in Science Education, 51,647-659. https://doi.org/10.1007/s11165-018-9811-y
CR  - Wan, Z. H., & Lee, J. C. K. (2017). Hong Kong secondary school students’ attitudes towards science: A study of structural models and gender differences. International Journal of Science Education, 39(5), 507–527. https://doi.org/10.1080/09500693.2017.1292015
CR  - Wang, C. L., & Liou, P. Y. (2018). Patterns of motivational beliefs in the science learning of total, high, and low-achieving students: Evidence of Taiwanese TIMSS 2011 data. International Journal of Science and Mathematics Education, 16(4), 603-618. https://doi.org/10.1007/s10763-017-9797-3
CR  - Wigfield, A., & Eccles, J. S. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12(3), 265-310. https://doi.org/10.1016/0273-2297(92)90011-P
CR  - Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. https://doi.org/10.1006/ceps.1999.1015
CR  - World Economic Forum (2021). The global gender gap report 2021: Insight report. Retrieved October 20, 2021, from https://www3.weforum.org/docs/WEF_GGGR_2021.pdf
UR  - https://doi.org/10.19128/turje.1487696
L1  - https://dergipark.org.tr/en/download/article-file/3945988
ER  -