Examining equity in an elementary school computer science intervention using component-based research
Keywords:
computer science, elementary, component-based research, equity, implementation researchAbstract
This article reports on implementation of a problem-based learning intervention developed with the intention of finding time for computer science (CS) in the elementary school day. This study investigated differences in effects on students in particular socio-demographic groups using a quasi-experimental design. We first provide an overview of the perennial problem of group differences or “gaps” in student outcomes. Then we illustrate how, using component-based research (CBR), we moved beyond the question of whether the intervention worked, to focus on which parts of the intervention worked, for whom, and under what conditions. Using hierarchical linear modeling, this study draws from a sample of 16 elementary schools with 321 teachers and 5791 students in Broward County, Florida, the sixth largest school system in the United States. This study complements a previous paper (Authors, 2020), which examined associations between intervention components and student outcomes by investigating how outcomes differ for students in different socio-demographic groups and whether the presence of particular intervention components amplify or reduce differences. Through CBR, our work illustrates that CS interventions which may appear to benefit students overall, may be less beneficial or even detrimental to particular groups.
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References
Abedi, J. (2002). Standardized achievement tests and English language learners: Psychometrics issues. Educational Assessment, 8(3), 231-257.Anderson & Kim, 2006
Archer, J., Cantrell, S., Holtzman, S. L., Joe, J. N., Tocci, C. M., & Wood, J. (2016). Better feedback for better teaching: A practical guide to improving classroom observations. John Wiley & Sons.
Au, W. (2010). Unequal by design: High-stakes testing and the standardization of inequality. Routledge. Authors, 2020
Authors, 2012
Authors, 2014
Authors, 2020
Bailey, D. F., & Bradbury-Bailey, M. E. (2006). Promoting achievement for African American males through group work. The Journal for Specialists in Group Work, 32(1), 83-96.
Beyer, S. (2014). Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Computer Science Education, 24(2-3), 153-192. DOI: https://doi.org/10.1080/08993408.2014.963363
Britner, S. L., & Pajares, F. (2001). Self-efficacy beliefs, motivation, race, and gender in middle school science. Journal of women and Minorities in Science and Engineering, 7(4).
Bullock, E. C. (2012). Conducting “Good” Equity Research in Mathematics Education: A Question of Methodology. Journal of Mathematics Education at Teachers College, 3, 43–55.
Bulsara, C. (2015). Using a mixed methods approach to enhance and validate your research. Brightwater Group Research Centre, 1-82.
Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Carlson Powell, J., Westbrook, A., & Landes, N. (2006). The BSCS 5E Instructional Model: Origins, effectiveness and applications. Retrieved from http://www.bscs.org/bscs-5e-instructional-model
Caro, D. H. (2009). Socio-economic status and academic achievement trajectories from childhood to adolescence. Canadian Journal of Education, 32(3), 558-590.
Celeste, L, Baysu, G., Phalet, K., Meeussen, L. & Kende, J. (2019). Can school diversity policies reduce belonging and achievement gaps between minority and majority youth? Multiculturalism, colorblindness and assimilationism assessed. Personality and Social Psychology Bulletin, 45(11), 1603-1618. DOI: https://doi.org/10.1177/01146167219838577.
Chang, J., & Le, T. N. (2010). Multiculturalism as a dimension of school climate: The impact on the academic achievement of Asian American and Hispanic youth. Cultural Diversity and Ethnic Minority Psychology, 16(4), 485-492.
Chimielewski, A.K. (2019). The global increase in the socioeconomic achievement gap, 1964-2015. American Sociological Review, 84(3), 517-544. DOI: https://doi.org/10.1177/0003122419847165.
Cunningham, B. C., Hoyer, K. M., & Sparks, D. (2015). Gender Differences in Science, Technology, Engineering, and Mathematics (STEM) Interest, Credits Earned, and NAEP Performance in the 12th Grade. Stats in Brief. NCES 2015-075. National Center for Education Statistics.
Dahl, G. B., & Lochner, L. (2012). The impact of family income on child achievement: Evidence from the earned income tax credit. American Economic Review, 102(5), 1927-56.
de Jong, E. J., Harper, C. A., & Coady, M. R. (2013). Enhanced knowledge and skills for elementary mainstream teachers of English language learners. Theory into Practice, 52(2), 89-97. DOI: https://doi.org/10.1080/00405841.2013.770326
Diamond, E., Furlong, M. J., & Quirk, M. (2016). Academically resilient Latino elementary students bridging the achievement gap. Contemporary School Psychology, 20(2), 160-169. DOI: https://doi.org/10.1007/s40688-016-0088-8
Doubé, W., & Lang, C. (2012). Gender and stereotypes in motivation to study computer programming for careers in multimedia. Computer Science Education, 22(1), 63-78. DOI: https://doi.org/10.1080/08993408.2012.666038
Else-Quest, N. M., & Hyde, J. S. (2016a). Intersectionality in quantitative psychological research: I. Theoretical and epistemological issues. Psychology of Women Quarterly, 40(2), 155-170.
Else-Quest, N. M., & Hyde, J. S. (2016b). Intersectionality in quantitative psychological research: II. Methods and techniques. Psychology of Women Quarterly, 40(3), 319-336.
Else-Quest, N. M., Mineo, C. C., & Higgins, A. (2013). Math and science attitudes and achievement at the intersection of gender and ethnicity. Psychology of Women Quarterly, 37(3), 293-309.
Farris, P. J. (2015). Elementary and middle school social studies: An interdisciplinary, multicultural approach. Waveland Press.
Ferguson, A. M., Maloney, E. A., Fugelsang, J., & Risko, E. F. (2015). On the relation between math and spatial ability: The case of math anxiety. Learning and Individual Differences, 39, 1-12. DOI: https://doi.org/10.1016/j.lindif.2015.02.007
Fry, R. (2007). How Far behind in Math and Reading Are English Language Learners? Report. Pew Hispanic Center.
Gilmour, A., Fuchs, D. & Wehby, J. (2019). Are student with disabilities accessing the curriculum? A meta-analysis of the reading achievement gap between students with and without disabilities. Exceptional Children, 85(3), 329-346. DOI: https://doi.org/10.1177/00144291875955830.
Gutiérrez, R., & Dixon-Roman, E. (2011). Beyond Gap Gazing: How Can Thinking About Education Comprehensively Help Us (Re)envision Mathematics Education? In Mapping Equity and Quality in Mathematics Education (pp. 21–34). DOI: https://doi.org/10.1007/978-90-481-9803-0
Gutiérrez, K. D., & Jaramillo, N. E. (2006). Looking for educational equity: The consequences of relying on Brown. Yearbook of the National Society for the Study of Education,
Hadden, I. R., Easterbrook, M. J., Nieuwenhuis, M., Fox, K. J., & Dolan, P. (2020). Self‐affirmation reduces the socioeconomic attainment gap in schools in England. British Journal of Educational Psychology, 90(2), 517-536. DOI: https://doi.org/10.1111/bjep.12291
Hung, M., Smith, W. A., Voss, M. W., Franklin, J. D., Gu, Y., & Bounsanga, J. (2020). Exploring student achievement gaps in school districts across the United States. Education and Urban Society, 52(2), 175-193. DOI: https://doi.org/10.1177/0013124519833442
Hur, J. W., Andrzejewski, C. E., & Marghitu, D. (2017). Girls and computer science: experiences, perceptions, and career aspirations. Computer Science Education, 27(2), 100-120. DOI: https://doi.org/10.1080/08993408.2017.1376385
Hurtado, S., Eagan, K., & Chang, M. (2010). Degrees of success: Bachelor’s degree completion rates among initial STEM majors. Higher Education Research Institute at UCLA, January.
Knoester, M. & Au, W. (2017) Standardized testing and school segregation: like tinder for fire?. Race Ethnicity and Education, 20(1), 1-14, DOI: https://doi.org/10.1080/13613324.2015.1121474
Lubienski, S. T. (2008). On “Gap Gazing” in Mathematics Education: The Need for Gaps Analyses. Journal for Research in Mathematics Education, 39(4), 350–356. Retrieved from https://www.jstor.org/stable/40539301
Maloney, E. A., Waechter, S., Risko, E. F., & Fugelsang, J. A. (2012). Reducing the sex difference in math anxiety: The role of spatial processing ability. Learning and Individual Differences, 22(3), 380-384. DOI: https://doi.org/10.1016/j.lindif.2012.01.001
Master, A., Cheryan, S., & Meltzoff, A. N. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of educational psychology, 108(3), 424. DOI: http://doi.org/10.1037/edu0000061
Morgan, H. (2016). Relying on high-stakes standardized tests to evaluate schools and teachers: A bad idea. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 89(2), 67-72. DOI: https://doi.org/10.1080/00098655.2016.1156628
National Assessment of Educational Progress (Project). (2019a). An Overview of NAEP. National Center for Education Statistics, Institute of Education Sciences, US Department of Education.
OECD (2020). PISA 2018 Results. Are students ready to thrive in an interconnected world?. Volume VI. Paris: OECD Publishing. Retrieved from https://www.oecd-ilibrary.org/docserver/d5f68679-en.pdf?expires=1612562559&id=id&accname=ocid177408&checksum=DFA001F165222D5E66493C4028B68F98.
Orfield, G., Ee, J., Frankenberg, E., & Siegel-Hawley, G. (2016). "Brown" at 62: School Segregation by Race, Poverty and State. Civil Rights Project-Proyecto Derechos Civiles.
Orfield, G., & Lee, C. (2005). Why segregation matters: Poverty and educational inequality. The Civil Rights Project at Harvard University.
Owens, A. (2018). Income segregation between school districts and inequality in students’ achievement. Sociology of Education. 91(1), 1-27. DOI: https://doi.org/10.1177/0038040717741180.
Pajares, F., & Kranzler, J. (1995). Self-efficacy beliefs and general mental ability in mathematical problem-solving. Contemporary Educational Psychology, 20(4), 426-443.
Perry, B. L., Link, T., Boelter, C., & Leukefeld, C. (2012). Blinded to science: Gender differences in the effects of race, ethnicity, and socioeconomic status on academic and science attitudes among sixth graders. Gender and Education, 24(7), 725-743. DOI: https://doi.org/10.1080/09540253.2012.685702
Prottsman, K. (2014). Computer science for the elementary classroom. ACM Inroads, 5(4), 60-63. DOI: https://doi.org/10.1145/2684721.2684735
Rabe, S. B. (2018). Learning from failure: An action research case study on developing growth mindset through academic risk-taking in an athletic training program.
Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. New Delhi, London, United Kingdom: Sage.
Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R. T., & Toit, M. (2011). HLM 7: Hierarchical linear and nonlinear modeling. Chicago, IL: Scientific Software International.
Reardon, S. F., Weathers, E. S., Fahle, E. M., Jang, H., & Kalogrides, D. (2019). Is separate still unequal? New evidence on school segregation and racial academic achievement gaps (No. 19-06). CEPA Working Paper.
Rodríguez, C., Amador, A., & Tarango, B. A. (2016). Mapping Educational Equity and Reform Policy in the Borderlands: LatCrit Spatial Analysis of Grade Retention. Equity and Excellence in Education, 49(2), 228–240. DOI: https://doi.org/10.1080/10665684.2016.1144834
Sax, L. J., Blaney, J. M., Lehman, K. J., Rodriguez, S. L., George, K. L., & Zavala, C. (2018). Sense of belonging in computing: The role of introductory courses for women and underrepresented minority students. Social Sciences, 7(8), 122, 1-23.
Slavin, R. E. (2014). Cooperative learning and academic achievement: Why does groupwork work? Annals of Psychology, 30(3), 785-791. DOI: https://doi.org/10.6018/analesps.30.3.201201
Sokolowski, H. M., Hawes, Z., & Lyons, I. M. (2019). What explains sex differences in math anxiety? A closer look at the role of spatial processing. Cognition, 182, 193-212.
Stevens, T., Olivárez Jr, A., & Hamman, D. (2006). The role of cognition, motivation, and emotion in explaining the mathematics achievement gap between Hispanic and White students. Hispanic Journal of Behavioral Sciences, 28(2), 161-186. DOI: https://doi.org/10.1177/0739986305286103
Van de Mortel, T. F. (2008). Faking it: social desirability response bias in self-report research. Australian Journal of Advanced Nursing, 40-48.
Vossoughi, S., Hooper, P. A. K., & Escudé, M. (2016). Making Through the Lens of Culture Visions for Educational Equity. Harvard Educational Review, 86(2), 206–232. DOI: https://doi.org/10.17763/0017-8055.86.2.206
Wang, J., Hong, H., Ravitz, J., & Hejazi Moghadam, S. (2016, February). Landscape of K-12 computer science education in the US: Perceptions, access, and barriers. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 645-650).
Wells, A.S., Keener, A., Cabral, L., & Cordova-Cobo, D. (2019) The more things change, the more they stay the same: The resegregation of public schools via charter school reform, Peabody Journal of Education, 94(5), 471-492, DOI: https://doi.org/10.1080/0161956X.2019.1668209
Xu, J. (2019). Challenges With Academic Achievement For Esl Students At The Elementary Level From A
Teacher’s Perspective. Retrieved from https://dune.une.edu/theses/222
Abedi, J. (2002). Standardized achievement tests and English language learners: Psychometrics issues. Educational Assessment, 8(3), 231-257.Anderson & Kim, 2006
Archer, J., Cantrell, S., Holtzman, S. L., Joe, J. N., Tocci, C. M., & Wood, J. (2016). Better feedback for better teaching: A practical guide to improving classroom observations. John Wiley & Sons.
Au, W. (2010). Unequal by design: High-stakes testing and the standardization of inequality. Routledge. Authors, 2020
Authors, 2012
Authors, 2014
Authors, 2020
Bailey, D. F., & Bradbury-Bailey, M. E. (2006). Promoting achievement for African American males through group work. The Journal for Specialists in Group Work, 32(1), 83-96.
Beyer, S. (2014). Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Computer Science Education, 24(2-3), 153-192. DOI: https://doi.org/10.1080/08993408.2014.963363
Britner, S. L., & Pajares, F. (2001). Self-efficacy beliefs, motivation, race, and gender in middle school science. Journal of women and Minorities in Science and Engineering, 7(4).
Bullock, E. C. (2012). Conducting “Good” Equity Research in Mathematics Education: A Question of Methodology. Journal of Mathematics Education at Teachers College, 3, 43–55.
Bulsara, C. (2015). Using a mixed methods approach to enhance and validate your research. Brightwater Group Research Centre, 1-82.
Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Carlson Powell, J., Westbrook, A., & Landes, N. (2006). The BSCS 5E Instructional Model: Origins, effectiveness and applications. Retrieved from http://www.bscs.org/bscs-5e-instructional-model
Caro, D. H. (2009). Socio-economic status and academic achievement trajectories from childhood to adolescence. Canadian Journal of Education, 32(3), 558-590.
Celeste, L, Baysu, G., Phalet, K., Meeussen, L. & Kende, J. (2019). Can school diversity policies reduce belonging and achievement gaps between minority and majority youth? Multiculturalism, colorblindness and assimilationism assessed. Personality and Social Psychology Bulletin, 45(11), 1603-1618. DOI: https://doi.org/10.1177/01146167219838577.
Chang, J., & Le, T. N. (2010). Multiculturalism as a dimension of school climate: The impact on the academic achievement of Asian American and Hispanic youth. Cultural Diversity and Ethnic Minority Psychology, 16(4), 485-492.
Chimielewski, A.K. (2019). The global increase in the socioeconomic achievement gap, 1964-2015. American Sociological Review, 84(3), 517-544. DOI: https://doi.org/10.1177/0003122419847165.
Cunningham, B. C., Hoyer, K. M., & Sparks, D. (2015). Gender Differences in Science, Technology, Engineering, and Mathematics (STEM) Interest, Credits Earned, and NAEP Performance in the 12th Grade. Stats in Brief. NCES 2015-075. National Center for Education Statistics.
Dahl, G. B., & Lochner, L. (2012). The impact of family income on child achievement: Evidence from the earned income tax credit. American Economic Review, 102(5), 1927-56.
de Jong, E. J., Harper, C. A., & Coady, M. R. (2013). Enhanced knowledge and skills for elementary mainstream teachers of English language learners. Theory into Practice, 52(2), 89-97. DOI: https://doi.org/10.1080/00405841.2013.770326
Diamond, E., Furlong, M. J., & Quirk, M. (2016). Academically resilient Latino elementary students bridging the achievement gap. Contemporary School Psychology, 20(2), 160-169. DOI: https://doi.org/10.1007/s40688-016-0088-8
Doubé, W., & Lang, C. (2012). Gender and stereotypes in motivation to study computer programming for careers in multimedia. Computer Science Education, 22(1), 63-78. DOI: https://doi.org/10.1080/08993408.2012.666038
Else-Quest, N. M., & Hyde, J. S. (2016a). Intersectionality in quantitative psychological research: I. Theoretical and epistemological issues. Psychology of Women Quarterly, 40(2), 155-170.
Else-Quest, N. M., & Hyde, J. S. (2016b). Intersectionality in quantitative psychological research: II. Methods and techniques. Psychology of Women Quarterly, 40(3), 319-336.
Else-Quest, N. M., Mineo, C. C., & Higgins, A. (2013). Math and science attitudes and achievement at the intersection of gender and ethnicity. Psychology of Women Quarterly, 37(3), 293-309.
Farris, P. J. (2015). Elementary and middle school social studies: An interdisciplinary, multicultural approach. Waveland Press.
Ferguson, A. M., Maloney, E. A., Fugelsang, J., & Risko, E. F. (2015). On the relation between math and spatial ability: The case of math anxiety. Learning and Individual Differences, 39, 1-12. DOI: https://doi.org/10.1016/j.lindif.2015.02.007
Fry, R. (2007). How Far behind in Math and Reading Are English Language Learners? Report. Pew Hispanic Center.
Gilmour, A., Fuchs, D. & Wehby, J. (2019). Are student with disabilities accessing the curriculum? A meta-analysis of the reading achievement gap between students with and without disabilities. Exceptional Children, 85(3), 329-346. DOI: https://doi.org/10.1177/00144291875955830.
Gutiérrez, R., & Dixon-Roman, E. (2011). Beyond Gap Gazing: How Can Thinking About Education Comprehensively Help Us (Re)envision Mathematics Education? In Mapping Equity and Quality in Mathematics Education (pp. 21–34). DOI: https://doi.org/10.1007/978-90-481-9803-0
Gutiérrez, K. D., & Jaramillo, N. E. (2006). Looking for educational equity: The consequences of relying on Brown. Yearbook of the National Society for the Study of Education,
Hadden, I. R., Easterbrook, M. J., Nieuwenhuis, M., Fox, K. J., & Dolan, P. (2020). Self‐affirmation reduces the socioeconomic attainment gap in schools in England. British Journal of Educational Psychology, 90(2), 517-536. DOI: https://doi.org/10.1111/bjep.12291
Hung, M., Smith, W. A., Voss, M. W., Franklin, J. D., Gu, Y., & Bounsanga, J. (2020). Exploring student achievement gaps in school districts across the United States. Education and Urban Society, 52(2), 175-193. DOI: https://doi.org/10.1177/0013124519833442
Hur, J. W., Andrzejewski, C. E., & Marghitu, D. (2017). Girls and computer science: experiences, perceptions, and career aspirations. Computer Science Education, 27(2), 100-120. DOI: https://doi.org/10.1080/08993408.2017.1376385
Hurtado, S., Eagan, K., & Chang, M. (2010). Degrees of success: Bachelor’s degree completion rates among initial STEM majors. Higher Education Research Institute at UCLA, January.
Knoester, M. & Au, W. (2017) Standardized testing and school segregation: like tinder for fire?. Race Ethnicity and Education, 20(1), 1-14, DOI: https://doi.org/10.1080/13613324.2015.1121474
Lubienski, S. T. (2008). On “Gap Gazing” in Mathematics Education: The Need for Gaps Analyses. Journal for Research in Mathematics Education, 39(4), 350–356. Retrieved from https://www.jstor.org/stable/40539301
Maloney, E. A., Waechter, S., Risko, E. F., & Fugelsang, J. A. (2012). Reducing the sex difference in math anxiety: The role of spatial processing ability. Learning and Individual Differences, 22(3), 380-384. DOI: https://doi.org/10.1016/j.lindif.2012.01.001
Master, A., Cheryan, S., & Meltzoff, A. N. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of educational psychology, 108(3), 424. DOI: http://doi.org/10.1037/edu0000061
Morgan, H. (2016). Relying on high-stakes standardized tests to evaluate schools and teachers: A bad idea. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 89(2), 67-72. DOI: https://doi.org/10.1080/00098655.2016.1156628
National Assessment of Educational Progress (Project). (2019a). An Overview of NAEP. National Center for Education Statistics, Institute of Education Sciences, US Department of Education.
OECD (2020). PISA 2018 Results. Are students ready to thrive in an interconnected world?. Volume VI. Paris: OECD Publishing. Retrieved from https://www.oecd-ilibrary.org/docserver/d5f68679-en.pdf?expires=1612562559&id=id&accname=ocid177408&checksum=DFA001F165222D5E66493C4028B68F98.
Orfield, G., Ee, J., Frankenberg, E., & Siegel-Hawley, G. (2016). "Brown" at 62: School Segregation by Race, Poverty and State. Civil Rights Project-Proyecto Derechos Civiles.
Orfield, G., & Lee, C. (2005). Why segregation matters: Poverty and educational inequality. The Civil Rights Project at Harvard University.
Owens, A. (2018). Income segregation between school districts and inequality in students’ achievement. Sociology of Education. 91(1), 1-27. DOI: https://doi.org/10.1177/0038040717741180.
Pajares, F., & Kranzler, J. (1995). Self-efficacy beliefs and general mental ability in mathematical problem-solving. Contemporary Educational Psychology, 20(4), 426-443.
Perry, B. L., Link, T., Boelter, C., & Leukefeld, C. (2012). Blinded to science: Gender differences in the effects of race, ethnicity, and socioeconomic status on academic and science attitudes among sixth graders. Gender and Education, 24(7), 725-743. DOI: https://doi.org/10.1080/09540253.2012.685702
Prottsman, K. (2014). Computer science for the elementary classroom. ACM Inroads, 5(4), 60-63. DOI: https://doi.org/10.1145/2684721.2684735
Rabe, S. B. (2018). Learning from failure: An action research case study on developing growth mindset through academic risk-taking in an athletic training program.
Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. New Delhi, London, United Kingdom: Sage.
Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R. T., & Toit, M. (2011). HLM 7: Hierarchical linear and nonlinear modeling. Chicago, IL: Scientific Software International.
Reardon, S. F., Weathers, E. S., Fahle, E. M., Jang, H., & Kalogrides, D. (2019). Is separate still unequal? New evidence on school segregation and racial academic achievement gaps (No. 19-06). CEPA Working Paper.
Rodríguez, C., Amador, A., & Tarango, B. A. (2016). Mapping Educational Equity and Reform Policy in the Borderlands: LatCrit Spatial Analysis of Grade Retention. Equity and Excellence in Education, 49(2), 228–240. DOI: https://doi.org/10.1080/10665684.2016.1144834
Sax, L. J., Blaney, J. M., Lehman, K. J., Rodriguez, S. L., George, K. L., & Zavala, C. (2018). Sense of belonging in computing: The role of introductory courses for women and underrepresented minority students. Social Sciences, 7(8), 122, 1-23.
Slavin, R. E. (2014). Cooperative learning and academic achievement: Why does groupwork work? Annals of Psychology, 30(3), 785-791. DOI: https://doi.org/10.6018/analesps.30.3.201201
Sokolowski, H. M., Hawes, Z., & Lyons, I. M. (2019). What explains sex differences in math anxiety? A closer look at the role of spatial processing. Cognition, 182, 193-212.
Stevens, T., Olivárez Jr, A., & Hamman, D. (2006). The role of cognition, motivation, and emotion in explaining the mathematics achievement gap between Hispanic and White students. Hispanic Journal of Behavioral Sciences, 28(2), 161-186. DOI: https://doi.org/10.1177/0739986305286103
Van de Mortel, T. F. (2008). Faking it: social desirability response bias in self-report research. Australian Journal of Advanced Nursing, 40-48.
Vossoughi, S., Hooper, P. A. K., & Escudé, M. (2016). Making Through the Lens of Culture Visions for Educational Equity. Harvard Educational Review, 86(2), 206–232. DOI: https://doi.org/10.17763/0017-8055.86.2.206
Wang, J., Hong, H., Ravitz, J., & Hejazi Moghadam, S. (2016, February). Landscape of K-12 computer science education in the US: Perceptions, access, and barriers. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 645-650).
Wells, A.S., Keener, A., Cabral, L., & Cordova-Cobo, D. (2019) The more things change, the more they stay the same: The resegregation of public schools via charter school reform, Peabody Journal of Education, 94(5), 471-492, DOI: https://doi.org/10.1080/0161956X.2019.1668209
Xu, J. (2019). Challenges With Academic Achievement For Esl Students At The Elementary Level From A
Teacher’s Perspective. Retrieved from https://dune.une.edu/theses/222
Published
2021-09-19
How to Cite
Ferris, K., Century, J., & Zuo, H. (2021). Examining equity in an elementary school computer science intervention using component-based research. International Journal of Computer Science Education in Schools, 5(1), 16–34. https://doi.org/10.21585/ijcses.v5i1.121
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