EXPLORING THE PLIGHT OF THE HONEYBEE: USING DATA SENSORS AND CODAP TO SUPPORT EMERGING BILINGUAL LEARNERS IN REASONING ABOUT BIG STATISTICAL IDEAS
DOI:
https://doi.org/10.52041/serj.v23i2.708Keywords:
Statistics education research, Innovative technologies, Emerging bilingual learners, Pollinators, Informal Inference, Data comparisonAbstract
Children require access to high-quality statistics education to develop the skills to participate in a technological and data-reliant workforce. This study consisted of a five-lesson integrated STEM unit designed to develop the statistical literacy of 62 6th-grade (11–12 years old) emerging bilingual (EB) learners. Learning was situated in the study of the honeybee, utilising innovative technologies to gather data and support data visualisation and analysis. Lesson study was used to design lessons targeting understandings of distribution, centre, variability, data comparison, informal measures of association and informal inference. This paper reports on the data comparison lesson. It reveals the influential role of digital technologies in highlighting the relevance of statistics in understanding societal issues and developing students’ statistical agency. This qualitative study also revealed that the development of statistical understanding was supported by the use of inclusive pedagogies guided by the principles of universal design and the incorporation of data analysis technologies.
References
Abedi, J., & Lord, C. (2001). The language factor in mathematics tests. Applied Measurement in Education, 14, 219–234.
Anderson, R., Boaler, J., & Dieckmann, J. (2018). Achieving elusive teacher change through challenging myths about learning: A blended approach. Education Sciences, 8(3), Article 98. https://doi.org/10.3390/educsci8030098
Artiles, A. J., Kozleski, E. B., Dorn, S., & Christensen, C. (2006). Learning in inclusive education research: Remediating theory and methods with a transformative agenda. Review of Research in Education, 30(1), 65–108. https://doi.org/10.3102/0091732X030001065
Baker, S., Lesaux, N., Geva, E., Jayanthi, M., Dimino, J., Proctor, C. P., Morris, J., Gersten, R., Haymond, K., Kieffer, M. J., Linan-Thompson, S., & Newman-Gonchar, R. (2014). Teaching academic content and literacy to English learners in elementary and middle school. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, and the U.S. Department of Education. https://ies.ed.gov/ncee/wwc/Docs/practiceguide/english_learners_pg_040114.pdf
Barwell, R. (2005). Working on arithmetic word problems when English is an additional language. British Educational Research Journal, 31(3), 329–348. https://doi.org/10.1080/01411920500082177
Barwell, R., Moschkovich, J., & Setati Phakeng, M. (2017). Language diversity and mathematics: Second language, bilingual, and multilingual learners. In J. Cai (Ed.), Compendium for research in mathematics education (pp. 583–606). National Council of Teachers of Mathematics.
Ben-Zvi, D. (2006). Scaffolding students’ informal inference and argumentation. In A. Rossman & B. Chance (Eds.), Proceedings of the Seventh International Conference on Teaching Statistics. International Statistical Institute.
Borgioli, G. (2008). Equity for English language learners in the mathematics classrooms. Teaching Children Mathematics, 15(3), 185–191.
Browne, T., Hewitt, R., Jenkins, M., & Walker, R. (2008). 2008 survey of technology enhanced learning for higher education in the UK. Universities and Colleges Information Systems Association.
Central Statistics Office. (2017). Census 2016. https://static.rasset.ie/documents/news/census-2016-summary-results-part-1-full.pdf
Chance, B., delMas, R. C., & Garfield, J. (2004). Reasoning about sampling distributions. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking (pp. 295- 323). Kluwer Academic Publishers.
Charmaz, K. (2014). Constructing grounded theory (2nd ed.). SAGE Publications.
Chita-Tegmark, M., Gravel, J., Serpa, M. B., Domings, Y., & Rose, D. H. (2012). Using the Universal Design for Learning framework to support culturally diverse learners. Journal of Education, 192(1), 17–22.
Clarkson, P. C. (2007). Australian Vietnamese students learning mathematics: High ability bilinguals and their use of their languages. Educational Studies in Mathematics, 64(2), 191–215. https://doi.org/10.1007/s10649-006-4696-5
Clements, D., Sarama, J., Wolfe, C., & Spitler, M. (2013). Longitudinal evaluation of a scale-up model for teaching mathematics with trajectories and technologies: Persistence of effects in the third year. American Educational Research Journal, 50(4), 812–850. https://doi.org/10.3102/0002831212469270
Creswell, J. W. (2009). Research design: Qualitative, quantitative and mixed methods approaches (3rd ed.). SAGE Publications.
de Araujo, Z., Roberts, S. A., Willey, C., & Zahner, W. (2018). English learners in K–12 mathematics education: A review of the literature. Review of Educational Research, 88(6), 879–919. https://doi.org/10.3102/0034654318798093
Dick, T., & Hollebrands, K. (2011). Focus in high school mathematics: Technology to support reasoning and sense making. National Council of Teachers of Mathematics.
Doran, P. R. (2015). Language accessibility in the classroom: How UDL can promote success for linguistically diverse learners. Exceptionality Education International, 25(3), 1–12.
Education Review Office. (2018). Responding to language diversity in Auckland. The child – the heart of the matter. https://ero.govt.nz/sites/default/files/2021-05/ALD-report2.pdf
English, L. D. (2012). Data modelling with first-grade students. Educational Studies in Mathematics, 81(1), 15–30. https://doi.org/10.1007/s10649-011-9377-3
English, L. (2018). Young children’s statistical literacy in modelling with data and chance. In A. M. Leavy, M. Meletiou-Mavrotheris & E. Paparistodemou (Eds.), Statistics in early childhood and primary education: Supporting early statistical and probabilistic thinking (pp. 295–311). Springer Nature. https://doi.org/10.1007/978-981-13-1044-7
Estrella, S., Vergara, A., & Gonzalez, O. (2021). Developing data sense: Making inferences from variability in tsunamis at primary school. Statistics Education Research Journal, 20(2), Article 16. https://doi.org/10.52041/serj.v20i2.413
European Commission. (2015). Language teaching and learning in multilingual classrooms. Education and training brief. http://ec.europa.eu/dgs/education_culture/repository/languages/library/ policy/policy-brief_en.pdf
Facer, K., & Selwyn, N. (2021). Digital technologies and the futures of education: Towards “non-stupid” optimism. Paper commissioned for the UNESCO futures of education report. https://unesdoc.unesco.org/ark:/48223/pf0000377071
Frischemeier, D. (2020). Building statisticians at an early age: Statistical projects exploring meaningful data in primary school. Statistics Education Research Journal, 19(1) 39–56. https://doi.org/10.52041/serj.v19i1.118
Fuchs, L. S., Fuchs, D., Compton, D. L., Hamlett, C. L., & Wang, A. Y. (2015). Is word-problem solving a form of text comprehension? Scientific Studies of Reading, 19, 204–223.
Gadanidis, G., & Geiger, G. (2012). A social perspective on technology-enhances mathematical Learning: From collaboration to performance. ZDM Mathematics Education, 42(1), 91–104. https://doi.org/10.1007/s11858-009-0213-5
Gardiner-Hyland, F. (2021). Don’t forget us! Challenges supporting children with EAL in Irish primary classrooms. European Journal of Applied Linguistics and TEFL, 10(2), https://www.proquest.com/docview/2596629059
Gil, E., & Ben-Zvi, D. (2014). Long-term impact on students’ informal inferential reasoning. In K. Makar, B. de Sousa, & R. Gould (Eds.), Proceedings of the Ninth International Conference on Teaching Statistics, Flagstaff, Arizona (ICOTS9). https://iase-web.org/icots/9/proceedings/pdfs/ICOTS9_8D1_GIL.pdf?1405041729
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory strategies for qualitative research. Sociology Press.
Graham-Matheson, L. (2012). How did we get here? A brief history of inclusion and special educational needs. In J. Cornwall & L. Graham-Matheson (Eds.), Leading on inclusion: Dilemmas, debates and new perspectives (1st ed., pp. 7–21). Routledge.
Hourigan, M., & Leavy, A. M. (2019). Learning from teaching: Pre-service elementary teachers’ perceived learning from engaging in “formal” lesson study. Irish Educational Studies, 38(3), 283–308. https://doi.org/10.1080/03323315.2019.1613252
Hourigan, M., & Leavy, A. M. (2020). Using integrated STEM to develop elementary students’ statistical literacy. Teaching Statistics, 42(3), 77–86. https://doi.org/10.1111/test.12229
Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: what is the “enhance” and how do we know? A critical literature review. Learning, Media and Technology, 29(1), 6–36.
Konold, C., & Higgins, T. (2003). Reasoning about data. In J. Kilpatrick, G. Martin, & D. Schifter (Eds.), A research companion to NCTM’s standards (pp. 193-215). National Council of Teachers of Mathematics.
Leavy, A. M. (2010). The challenge of preparing preservice teachers to teach informal inferential reasoning. Statistics Education Research Journal, 9(1), 46–67. https://doi.org/10.52041/serj.v9i1.387
Leavy, A. M., & Hourigan, M. (2016). Using lesson study to support knowledge development in initial teacher education: Insights from early number classrooms. Teaching and Teacher Education, 57, 161–175. https://doi.org/10.1016/j.tate.2016.04.002
Leavy A. M., & Hourigan, M. (2018). Using lesson study to support the teaching of early number concepts: Examining the development of prospective teachers’ specialized content knowledge. Early Childhood Research Quarterly, 46(1), 47–60. https://doi.org/10.1007/s10643-016-0834-6
Leavy, A. M., Hourigan, M., O’Dwyer, A., Carroll, C. Corry, E., & Hamilton, M. (2021). How slow is your parachute? Reflections on a STEM activity from an Irish classroom. Science and Children, 58(6), 48–55.
Leavy, A. M., & Middleton, J. A. (2011). Elementary and middle graders understanding of typicality. Journal of Mathematical Behaviour, 30(3), 235–254.
Lehrer, R., & Schauble, L. (2002). Inventing data structures for representational purposes: Elementary grade students’ classification models. Mathematical Thinking and Learning, 2, 51–74.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
Little, D., & Kirwan, D. (2021). Language and languages in the primary school: Some guidelines for teachers. Post-primary languages Ireland. https://ppli.ie/ppli-primary-guidelines/
Makar, K., Bakker, A., & Ben-Zvi, D. (2011). The reasoning behind informal statistical inference. Mathematical Thinking and Learning, 13(1–2), 152–173. https://doi.org/10.1080/10986065.2011.538301
Makar, K., Fry, K., & English, L. (2023). Primary students’ learning about citizenship through data science. ZDM Mathematics Education, 55(5), 967–979. https://doi.org/10.1007/s11858-022-01450-7
Makar, K., & Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82–105. https://doi.org/10.52041/serj.v8i1.457
McCulloch, A. W., Lovett, J. N., Dick, L. K., & Cayton, C. (2021). Positioning students to explore math with technology. Mathematics Teacher: Learning & Teaching PK–12, 114(10), 738–749.
Meletiou-Mavrotheris, M., & Paparistodemou, E. (2015). Developing young learners’ reasoning about samples and sampling in the context of informal inferences. Educational Studies in Mathematics, 88(3), 385–404.
Meyer, A., Rose, D. H., & Gordon, D. T. (2014). Universal design for learning: Theory and practice. Center for Applied Special Technology.
Moore, D. S. (1990). Uncertainty. In L. A. Steen (Ed.), On the shoulders of giants: New approaches to numeracy (pp. 95–137). National Academy Press.
Murata, A. (2011). Introduction: Conceptual overview of lesson study. In L. C. Hart, A. S. Alston & A. Murata (Eds.), Lesson study research and practice in mathematics education: learning together (pp. 1–12). Springer Netherlands. https://doi.org/10.1007/978-90-481-9941-9_1
National Biodiversity Data Centre. (2021). All-Ireland pollinator plan 2021–2025. https://pollinators.ie/wp-content/uploads/2021/03/All-Ireland-Pollinator-Plan-2021-2025-WEB.pdf
National Council of Teachers of Mathematics. (2018). Catalyzing change in high school. Initiating critical conversations.
National Council of Teachers of Mathematics. (2023). Equitable integration of technology for mathematics learning: A position of the National Council of Teachers of Mathematics.
National Mathematics Advisory Panel. (2008). Foundations for success: The final report of the National Mathematics Advisory Panel. US Department of Education, Office of Planning, Evaluation and Policy Development.
Nieto, S. (2000). Affirming diversity: The sociopolitical context of multicultural education (3rd ed). Longman.
Ní Ríordáin, M., & Flanagan, E. (2020). Bilingual undergraduate students’ language use and meta-level developments relating to functions. Mathematics Education Research Journal, 32(3), 475–496. https://doi.org/10.1007/s13394-019-00268-z
The Organization for Economic Cooperation and Development (2018). The future of education and skills: Position paper. https://www.oecd.org/education/2030-project/about/documents/E2030%20Position%20Paper%20(05.04.2018).pdf
Patton, M. Q. (2002). Qualitative research & evaluation methods (3rd ed.). SAGE Publications.
Ridgway, J., & Ridgway, R. (2019). Teaching for citizen empowerment and engagement. Radical Statistics, 123, 15–23.
Roos, H. (2018). Inclusion in mathematics education: An ideology, a way of teaching, or both? Educational Studies in Mathematics, 100, 25–41. https://doi.org/10.1007/s10649-018-9854-z
Rossman, A. J., & Chance, B. L. (1999). Teaching the reasoning of statistical inference: A “Top Ten” list. The College Mathematics Journal, 30(4), 297–305. https://doi.org/10.1080/07468342.1999.11974074
Rubin, A., Hammerman, J. K. L., & Konold, C. (2006). Exploring informal inference with interactive visualization software. In A. Rossman & B. Chance (Eds.), Working cooperatively in statistics education: Proceedings of the Seventh International Conference on Teaching Statistics (ICOTS7), Salvador, Brazil. https://iase-web.org/documents/papers/icots7/2D3_RUBI.pdf
Ryan, C. (2013). Language use in the United States: 2011 (ACS-22). US Census Bureau. https://www2.census.gov/library/publications/2013/acs/acs-22/acs-22.pdf
Ryan, B., McGarr, O., & McCormack, O. (2020). Underneath the veneer of techno-positivity: Exploring teachers’ perspectives on technology use in further education and training. Teachers and Teaching theory and practice, 26(5–6), 414–427.
Sarama, J., & Clements, D. H. (2009). Early childhood mathematics education research: Learning trajectories for young children. Routledge.
Saxe, G. B., & Sussman, J. (2019). Mathematics learning in language inclusive classrooms: Supporting the achievement of English learners and their English proficient peers. Educational Researcher, 48(7), 452–465. https://doi.org/10.3102/0013189X19869953
Science for Environment Policy. (2020). Future brief: Pollinators: Importance for nature and human well-being, drivers of decline and the need for monitoring (Issue Brief No. 23). Brief produced for the European Commission DG Environment. Bristol: Science Communication Unit, UWE Bristol.
Secada, W. G., Fennema, E., & Adajian, L. B. (1995). New directions for equity in mathematics education. Cambridge University Press.
Selmer, S. J., & Floyd, K. (2012). UDL for geometric length measurement. Teaching Children Mathematics, 19(3), 146–151.
Selwyn, N. (2017). Education and technology: Key issues and debates (2nd ed.). Bloomsbury.
Sharma, S., & Sharma, S. (2023). Successful teaching practices for English language learners in multilingual mathematics classrooms: A meta?analysis. Mathematics Education Research Journal, 35(4), 821–848. https://doi.org/10.1007/s13394-022-00414-0
Shaughnessy, M., & Pfannkuch, M. (2002). How faithful is old faithful? Statistical thinking: A story of variation and prediction. Mathematics Teacher, 95(4), 252–259.
Suter, W. N. (2012). Introduction to educational research: A critical thinking approach (2nd ed.). SAGE Publications.
Trakulphadetkrai, N. V., Courtney, L., Clenton, J., Treffers-Daller, J. & Tsakalaki, A. (2020). The contribution of general language ability, reading comprehension and working memory to mathematics achievement among children with English as additional language (EAL): An exploratory study. International Journal of Bilingual Education and Bilingualism, 23(4), 473-487. https://doi.org/10.1080/13670050.2017.1373742
United Nations Children’s Fund and International Telecommunication Union. (2020). How many children and young people have internet access at home? Estimating digital connectivity during the COVID-19 pandemic.
Verbisck, J., Barquero, B., Bittar, M., & Bosch, M. (2023). A study and research path for teacher education in statistics: Dealing with the transparency of data treatment. Paper presented at CERME13, Budapest, Hungary.
Vilenius-Tuohimaa, P. M, Aunola, K., & Nurmi, J-E. (2008). The association between mathematical word problems and reading comprehension. Educational Psychology, 28(4), 409–426.
Warren, E., & Miller, J. (2015). Supporting English second-language learners in disadvantaged contexts: Learning approaches that promote success in mathematics. International Journal of Early Years Education, 23(2), 192–208. https://doi.org/10.1080/09669760.2014.969200
Watson, J. (2018). Variation and expectation for six-year-olds. In A. M. Leavy, M. Meletiou-Mavrotheris & E. Paparistodemou (Eds),. Statistics in early childhood and primary education: Supporting early statistical and probabilistic thinking (pp. 55–74). Springer Nature. https://doi.org/10.1007/978-981-13-1044-7
Wild, C., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–265.
Zapata-Cardona, L. (2023). The possibilities of exploring nontraditional datasets with young children. Teaching Statistics, 45(1), 22–29.
Zieffler, A., Garfield, J., delMas, R., & Reading, C. (2008). A framework to support research on informal inferential reasoning. Statistics Education Research Journal, 7(2), 40–58. https://doi.org/10.52041/serj.v7i2.469
