School and Student Factors and Their Influence on Affective Mathematics Engagement

Yujin Lee; Robert M. Capraro; Mary M. Capraro; Ali Bicer

doi:10.29333/ejecs/1212

Authors

Yujin Lee University of North Dakota https://orcid.org/0000-0002-3701-3089
Robert M. Capraro https://orcid.org/0000-0001-8868-4432
Mary M. Capraro https://orcid.org/0000-0001-8868-4432
Ali Bicer https://orcid.org/0000-0002-2147-5393

DOI:

https://doi.org/10.29333/ejecs/1212

Keywords:

affective mathematics engagement, economic disadvantage, gender, immigration, language

Abstract

This study examined the student-level (i.e., gender, home language, and immigration status) and school-level (i.e., school economic disadvantage status) variability of the students’ affective mathematics engagement. It was hypothesized that there is a school effect that contributes toward explaining differences in affective mathematics engagement besides the student-level differences. For the sake of the nested structure of the data in Trends in International Mathematics and Science Study (TIMSS), we used the Hierarchical Linear Modeling (HLM) methodology. There were 10,221 students from 246 schools in the study. The results of this study explained 5.3% of variance in students’ affective mathematics engagement by school-mean economic disadvantage status, where students’ demographic factors explained 1.2%. The present study contributed to a better understanding of the opportunity to learn variables at the student- and school-level in students’ affective mathematics engagement.

Downloads

Download data is not yet available.

Author Biographies

Yujin Lee, University of North Dakota

Yujin Lee is an assistant professor in the Department of Mathematics Education at Kangwon National University. Her research interests have centered on affective mathematics engagement, STEM education, teacher identity, large-scale national/international data analysis. Dr. Lee received her Ph.D. in Curriculum and Instruction with a specialization in mathematics education from Texas A&M University in 2019.

Robert M. Capraro

Robert M. Capraro is a co-director of the Aggie STEM center and professor of Mathematics Education in the Department of Teaching, Learning, and Culture at Texas A&M University. His research interests include representational models and learning transfer, curriculum design and evaluation in mathematics and science, STEM Project-Based Learning (PBL), and school change.

Mary M. Capraro

Mary M. Capraro is a co-director of the Aggie STEM center and professor of Mathematics Education in the Department of Teaching, Learning, and Culture at Texas A&M University. Her research interests include teacher knowledge and preparation in mathematics education and student understanding of mathematical concepts and how students pose problems.

Ali Bicer

Ali Bicer is an assistant professor in the School of Teacher Education at the University of Wyoming, where he teaches undergraduate and graduate courses in mathematics education. His research interests have centered on mathematical creativity, creativity-directed problem solving and -posing tasks, STEM education, and writing in mathematics. Dr. Bicer received his Ph.D. in Curriculum and Instruction with a mathematics specialization from Texas A&M University in 2016.

References

Areepattamannil, S., & Freeman, J. G. (2008). Academic achievement, academic self-concept, and academic motivation of immigrant adolescents in the greater Toronto area secondary schools. Journal of Advanced Academics, 19, 700–743. DOI: https://doi.org/10.4219/jaa-2008-831

Baroudi, S. (2019). Designing the Lebanese public education budget: A policy document analysis. International Education Journal: Comparative Perspectives, 18(3), 25–38.

Bergomi, C., Tschacher, W., & Kupper, Z. (2013). The assessment of mindfulness with self-report measures: Existing scales and open issues. Mindfulness, 4(3), 191–202. https://doi.org/10.1007/s12671-012-0110-9 DOI: https://doi.org/10.1007/s12671-012-0110-9

Bicer, A., Perihan, C., & Lee, Y. (2020). A Meta-Analysis: The Effects of CBT as a Clinic-& School-Based Treatment on Students' Mathematics Anxiety. International Electronic Journal of Mathematics Education, 15(2), 1-14. https://doi.org/10.29333/iejme/7598 DOI: https://doi.org/10.29333/iejme/7598

Bicer, A., Nite, S. B., Capraro, R. M., Barroso, L. R., Capraro, M. M., & Lee, Y. (2017). Moving from STEM to STEAM: The effects of informal STEM learning on students' creativity and problem solving skills with 3D printing. Proceedings of the 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1-6). Indianapolis, IN. https://doi.org/10.1109/FIE.2017.8190545 DOI: https://doi.org/10.1109/FIE.2017.8190545

Boedeker, P., Nite, S., Capraro, R. M., & Capraro, M. M. (2015). Women in STEM: The impact of STEM PBL implementation on performance, attrition, and course choice of women. Proceedings of 2015 IEEE Frontiers in Education Conference (FIE) (pp. 1-8). El Paso, TX. https://doi.org/10.1109/FIE.2015.7344178 DOI: https://doi.org/10.1109/FIE.2015.7344178

Buck, G. A., Clark, V. L. P., Leslie‐Pelecky, D., Lu, Y., & Cerda‐Lizarraga, P. (2008). Examining the cognitive processes used by adolescent girls and women scientists in identifying science role models: A feminist approach. Science Education, 92(4), 688–707. https://doi.org/10.1002/sce.20257 DOI: https://doi.org/10.1002/sce.20257

Bystydzienski, J., & Bird, S. (Eds.). (2006). Removing barriers: Women in academic science, technology, engineering, and mathematics. Indiana University Press.

Cai, X. (2008). Missing data treatment of a level-2 variable in a 3-level hierarchical linear model [Unpublished doctoral dissertation]. Western Michigan University.

Capraro, R. M., & Slough, S. W. (2013). Why PBL? Why STEM? Why now? An introduction to project-based learning: An integrated science, technology, engineering, and mathematics (STEM) approach. In R. M. Capraro, M. M. Capraro, & J. Morgan (Eds.), STEM Project-based learning: An integrated science technology engineering and mathematics (STEM) approach (pp. 1-5). Rotterdam, Netherlands: Sense. DOI: https://doi.org/10.1007/978-94-6209-143-6_1

Chen, X. (2009). Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education (NCES No. 2009- 161). National Center for Educational Statistics.

Chen, J. A., & Usher, E. L. (2013). Profiles of the sources of science self-efficacy. Learning and Individual Differences, 24, 11–21. https://doi.org/10.1016/j.lindif.2012.11.002 DOI: https://doi.org/10.1016/j.lindif.2012.11.002

Chiu, A., Price, C. A., & Ovrahim, E. (2015, April). Supporting elementary and middle school STEM education at the whole school level: A review of the literature [Paper presentation]. NARST 2015 Annual Conference, Chicago, IL. https://www.msichicago.org/fileadmin/assets/educators/science_leadership_initiative/SLI_Lit_Review.pdf

Cho, S., & Reich, G. A. (2008). New immigrants, new challenges: High school social studies teachers and English language learner instruction. The Social Studies, 99(6), 235–242. https://doi.org/10.3200/TSSS.99.6.235-242 DOI: https://doi.org/10.3200/TSSS.99.6.235-242

Cochran-Smith, M., Villegas, A. M., Abrams, L., Chavez-Moreno, L., Mills, T., & Stern, R. (2015). Critiquing teacher preparation research: An overview of the field, part II. Journal of Teacher Education, 66(2), 109–121. https://doi.org/10.1177/0022487114558268 DOI: https://doi.org/10.1177/0022487114558268

Dotterer, A. M., & Lowe, K. (2011). Classroom context, school engagement, and academic achievement in early adolescence. Journal of Youth and Adolescence, 40(12), 1649–1660. https://doi.org/10.1007/s10964-011-9647-5 DOI: https://doi.org/10.1007/s10964-011-9647-5

Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics anxiety: What have we learned in 60 years? Front. Psychol., 7, Article 508.https://doi.org/10.3389/fpsyg.2016.00508 DOI: https://doi.org/10.3389/fpsyg.2016.00508

Fulmer, S. M., & Frijters, J. C. (2009). A review of self-report and alternative approaches in the measurement of student motivation. Educational Psychology Review, 21(3), 219–246. https://doi.org/10.1007/s10648-009-9107-x DOI: https://doi.org/10.1007/s10648-009-9107-x

Gill, J. (2003). Hierarchical linear models. In K. Kempf-Leonard (Ed.), Encyclopedia of social measurement. Academic Press.

Goldin, G. A., Epstein, Y. M., Schorr, R. Y., & Warner, L. B. (2011). Beliefs and engagement structures: Behind the affective dimension of mathematical learning. ZDM Mathematics Education, 43(4), 547–560. https://doi.org/10.1007/s11858-011-0348-z DOI: https://doi.org/10.1007/s11858-011-0348-z

Griffiths, A. J., Sharkey, J. D., & Furlong, M. J. (2009). Student engagement and positive school adaptation. In R. Gilman, E. S. Huebner, & M. J. Furlong (Eds.), Handbook of positive psychology in schools (pp. 197–211). Routledge/Taylor Francis Group.

Grigg, S., Perera, H. N., McIlveen, P., & Svetleff, Z. (2018). Relations among math self efficacy, interest, intentions, and achievement: A social cognitive perspective. Contemporary Educational Psychology, 53, 73–86. https://doi.org/10.1016/j.cedpsych.2018.01.007 DOI: https://doi.org/10.1016/j.cedpsych.2018.01.007

Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008). Culture, gender and math. Science, 320, 1164 –1165. https://doi.org/10.1126/science.1154094 DOI: https://doi.org/10.1126/science.1154094

Hand, V. (2012). Seeing culture and power in mathematical learning: Toward a model of equitable instruction. Educational Studies in Mathematics, 80(1), 233–247. https://doi.org/10.1007/s10649-012-9387-9 DOI: https://doi.org/10.1007/s10649-012-9387-9

Hox, J. J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum. DOI: https://doi.org/10.4324/9781410604118

Jacobs, J. E., Davis-Kean, P., Bleeker, M., Eccles, J. S., & Malanchuk, O. (2005). I can, but I don’t want to. The impact of parents, interests, and activities on gender differences in math. In A. Gallagher & J. Kaufman (Eds.), Gender difference in mathematics (pp. 246–263). University Press. DOI: https://doi.org/10.1017/CBO9780511614446.013

Krannich, M., Goetz, T., Lipnevich, A. A., Bieg, M., Roos, A. L., Becker, E. S., & Morger, V. (2019). Being over-or underchallenged in class: Effects on students’ career aspirations via academic self-concept and boredom. Learning and Individual Differences, 69, 206–218. https://doi.org/10.1016/j.lindif.2018.10.004 DOI: https://doi.org/10.1016/j.lindif.2018.10.004

Kwok, O. M., Underhill, A. T., Berry, J. W., Luo, W., Elliott, T. R., & Yoon, M. (2008). Analyzing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures. Rehabilitation Psychology, 53(3), 370-386. https://doi.org/10.1037/a0012765 DOI: https://doi.org/10.1037/a0012765

Lauen, D. L., Fuller, B., & Dauter, L. (2015). Positioning charter schools in Los Angeles: Diversity of form and homogeneity of effects. American Journal of Education, 121(2), 213–239. https://doi.org/10.1086/679391 DOI: https://doi.org/10.1086/679391

Lee, Y. (2022). Am I a STEAM Teacher? Professional Identity and PCK through STEAM Project-Based Learning Professional Preparation. School Mathematics, 24(1), 147-171. https://doi.org/10.29275/sm.2022.3.24.1.147 DOI: https://doi.org/10.29275/sm.2022.3.24.1.147

Lee, Y., Capraro, R. M., & Bicer, A. (2019a). Affective mathematics engagement: A comparison of STEM PBL versus non-STEM PBL instruction. Canadian Journal of Science, Mathematics and Technology Education, 19(3), 270-289. https://doi.org/10.1007/s42330-019-00050-0 DOI: https://doi.org/10.1007/s42330-019-00050-0

Lee, Y., Capraro, R. M., & Bicer, A. (2019b). Gender difference on spatial visualization by college students’ major types as STEM and non-STEM: a meta-analysis. International journal of mathematical education in science and technology, 50(8), 1241-1255. https://doi.org/10.1080/0020739X.2019.1640398 DOI: https://doi.org/10.1080/0020739X.2019.1640398

Lee, Y., Capraro, R. M., Capraro, M. M., & Bicer, A. (2022). Cultural Affordance, Motivation, and Affective Mathematics Engagement in Korea and the US. Research in Mathematical Education, 25(1), 21-43.

Lee, Y., Capraro, M. M., & Viruru, R. (2018). The factors motivating students’ STEM career aspirations: Personal and societal contexts. International Journal of Innovation in Science and Mathematics Education, 26(5), 36-48.

Lent, R. W., Paixao, M. P., Da Silva, J. T., & Leitão, L. M. (2010). Predicting occupational interests and choice aspirations in Portuguese high school students: A test of social cognitive career theory. Journal of Vocational Behavior, 76(2), 244–251. https://doi.org/10.1016/j.jvb.2009.10.001 DOI: https://doi.org/10.1016/j.jvb.2009.10.001

Levin, K. (2006). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24–25. https://doi.org/10.1038/sj.ebd.6400375 DOI: https://doi.org/10.1038/sj.ebd.6400375

Maldonado, S. I., Mosqueda, E., Capraro, R. M., & Capraro, M. M. (2018). Language minority students' mathematics achievement in urban schools: Coursework, race-ethnicity, and English-language proficiency. Penn GSE Perspectives on Urban Education, 15(1), 1-9.

McCoach, D. B. (2010). Hierarchical linear modeling. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 123–140). Routledge.

McGraw, R., Lubienski, S. T., & Strutchens, M. E. (2006). A closer look at gender in NAEP mathematics achievement and affect data: Intersections with achievement, race/ethnicity, and socioeconomic status. Journal for Research in Mathematics Education, 37(2), 129–150.

Mo, Y., Singh, K., & Chang, M. (2013). Opportunity to learn and student engagement: A HLM study on eighth grade science achievement. Educational Research for Policy and Practice, 12(1), 3–19. https://doi.org/10.1007/s10671-011-9126-5 DOI: https://doi.org/10.1007/s10671-011-9126-5

Morgan, H. (2012). Poverty-stricken schools: What we can learn from the rest of the world and from successful schools in economically disadvantaged areas in the US. Education, 133(2), 291-297.

Muehlenkamp, J. J., Weiss, N., & Hansen, M. (2015). Problem-based learning for introductory psychology: Preliminary supporting evidence. Scholarship of Teaching and Learning in Psychology, 1(2), 125–136. https://doi.org/10.1037/stl0000027 DOI: https://doi.org/10.1037/stl0000027

Mullis, I. V. S., Martin, M. O., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. TIMSS & PIRLS International Study Center, Boston College.

Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N. M., Devos, T., Ayala, A., ... & Kesebir, S. (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. https://doi.org/10.1073/pnas.0809921106 DOI: https://doi.org/10.1073/pnas.0809921106

Organization for Economic Cooperation and Development. (2003). The PISA 2013 Assessment Framework-mathematics, reading, science and problem solving: knowledge and skills. OECD. https://www.oecd.org/education/school/programmeforinternationalstudentassessmentpisa/pisa2003assessmentframeworkmathematicsreadingscienceandproblemsolvingknowledgeandskills-publications2003.htm

Pekrun, R., & Linnenbrink-Garcia, L. (2012). Academic emotions and student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259–282). Springer. DOI: https://doi.org/10.1007/978-1-4614-2018-7_12

Perry, L., & McConney, A. (2010). Does the SES of the school matter? An examination of socioeconomic status and student achievement using PISA 2003. The Teachers College Record, 112(4), 1137–1162. https://doi.org/10.1177/016146811011200401 DOI: https://doi.org/10.1177/016146811011200401

Preckel, F., Götz, T., Frenzel, A. (2010). Ability grouping of gifted students: Effects on academic self‐concept and boredom. British Journal of Educational Psychology, 80(3), 451–472. https://doi.org/10.1348/000709909X480716 DOI: https://doi.org/10.1348/000709909X480716

President’s Council of Advisors on Science and Technology. (2012, February). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Office of the President.

Quintero, M., Hasty, L., Li, T., Song, S., & Wang, Z. (2022). A multidimensional examination of math anxiety and engagement on math achievement. British Journal of Educational Psychology, 92(3), 955–973. https://doi.org/10.1111/bjep.12482 DOI: https://doi.org/10.1111/bjep.12482

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). SAGE Publications.

Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104(3), 700–712. https://doi.org/10.1037/a0027268 DOI: https://doi.org/10.1037/a0027268

Rillero, P., Koerner, M., Jimenez-Silva, M., Merritt, J., & Farr, W. J. (2017). Developing teacher competencies for problem-based learning pedagogy and for supporting learning in language-minority students. Interdisciplinary Journal of Problem-Based Learning, 11(2), 1–11. https://doi.org/10.7771/1541-5015.1675 DOI: https://doi.org/10.7771/1541-5015.1675

Schneider, R. M., Krajcik, J., Marx, R. W., & Soloway, E. (2002). Performance of students in project‐based science classrooms on a national measure of science achievement. Journal of Research in Science Teaching, 39(5), 410-422. https://doi.org/10.1002/tea.10029 DOI: https://doi.org/10.1002/tea.10029

Skinner, E. A., & Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 21–44). Springer. DOI: https://doi.org/10.1007/978-1-4614-2018-7_2

Slama, R. B. (2012). A longitudinal analysis of academic English proficiency outcomes for adolescent English language learners in the United States. Journal of Education Psychology, 10(2), 265–285. https://doi.org/10.1037/a0025861 DOI: https://doi.org/10.1037/a0025861

Spelke, E. S. (2005) Sex differences in intrinsic aptitude for mathematics and science? American Psychologist, 60(9), 950 –958. https://doi.org/10.1037/0003-066X.60.9.950 DOI: https://doi.org/10.1037/0003-066X.60.9.950

Steegh, A. M., Höffler, T. N., Keller, M. M., & Parchmann, I. (2019). Gender differences in mathematics and science competitions: A systematic review. Journal of Research in Science Teaching, 56(10), 1431–1460. https://doi.org/10.1002/tea.21580 DOI: https://doi.org/10.1002/tea.21580

Tomlinson, C. A., & Jarvis, J. M. (2014). Case studies of success: Supporting academic success for students with high potential from ethnic minority and economically disadvantaged backgrounds. Journal for the Education of the Gifted, 37(3), 191–219. https://doi.org/10.1177/0162353214540826 DOI: https://doi.org/10.1177/0162353214540826

U. S. Department of Education. (2016). The condition of education 2016 (NCES 2016-144). https://nces.ed.gov/pubs2016/2016144.pdf

Wai, J., Cacchio, M., Putellaz, M., & Makel, M. C. (2010). Sex differences in the right tail of cognitive abilities: A 30-year examination. Intelligence, 38(4), 412–423. https://doi.org/10.1016/j.intell.2010.04.006 DOI: https://doi.org/10.1016/j.intell.2010.04.006

Wang, M. T., & Degol, J. (2014). Staying engaged: Knowledge and research needs in student engagement. Child Development Perspectives, 8(3), 137–143. https://doi.org/10.1111/cdep.12073 DOI: https://doi.org/10.1111/cdep.12073

Wang, M. T., Scanlon, C. L., & Del Toro, J. (2022). Does anyone benefit from exclusionary discipline? An exploration on the direct and vicarious links between suspensions for minor infraction and adolescents’ academic achievement. American Psychologist. Advance online publication. https://doi.org/10.1037/amp0001030 DOI: https://doi.org/10.1037/amp0001030

Watt, H. M., & Goos, M. (2017). Theoretical foundations of engagement in mathematics. Mathematics Education Research Journal, 29(2), 133–142. https://doi.org/10.1007/s13394-017-0206-6 DOI: https://doi.org/10.1007/s13394-017-0206-6

Wright, L. A., & Slate, J. R. (2015). Differences in critical-thinking skills for Texas middle school students as a function of economic disadvantage. Journal of Education Research, 9(4), 345–356.

Wright, W. (2015). Foundations for teaching English language learners: Research, theory, policy, and practice (2nd ed.). Caslon Publishing.