EXAMINING THE ROLE OF CONTEXT IN STATISTICAL LITERACY OUTCOMES USING AN ISOMORPHIC ASSESSMENT INSTRUMENT
DOI:
https://doi.org/10.52041/serj.v23i1.529Keywords:
Statistical literacy, Relevant contexts, Isomorphic assessment, Transfer, COVID-19 pandemic, Statistics education researchAbstract
The Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report advocates for use of real data with context and purpose. This work contributes to the growing literature on assessing statistical literacy by investigating the influence of context as it relates to assessment performance among post-secondary introductory statistics students. We discuss the development of an isomorphic form of an existing assessment instrument, and report results which concluded that test takers demonstrated lower statistical literacy scores when assessment tasks incorporated real data from published studies as context when compared with functionally similar tasks such as those with a contrived data set and a realistic context.
References
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. https://www.apa.org/science/programs/testing/standards
Barbieri, G. A., & Giacché, P. (2006). The worth of data: The tale of an experience for promoting and improving statistical literacy. http://iase-web.org/documents/papers/icots7/1A1_BARB.pdf
Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Sheri Johnson, S., Perez, L., & Spangler, D. A. (2020). Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education. https://www.amstat.org/asa/files/pdfs/GAISE/GAISEIIPreK-12_Full.pdf
Barniol, P., & Zavala, G. (2014). Force, velocity, and work: The effects of different contexts on students’ understanding of vector concepts using isomorphic problems. Physical Review Special Topics: Physics Education Research, 10(2), Article 020115. https://doi.org/10.1103/PhysRevSTPER.10.020115
Bassok, M., & Holyoak, K. J. (1989). Interdomain transfer between isomorphic topics in algebra and physics. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(1), 153–166. https://doi.org/10.1037/0278-7393.15.1.153
Bates, D., Machler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
Ben-Zvi, D., & Garfield, J. (Eds.). (2004). The challenge of developing statistical literacy, reasoning, and thinking. Kluwer Academic Publishers.
Ben-Zvi, D., & Garfield, J. (2008). Introducing the emerging discipline of statistics education. School Science and Mathematics, 108(8), 355–361. https://doi.org/10.1111/j.1949-8594.2008.tb17850.x
Ben-Zvi, D., Makar, K., & Garfield, J. (Eds.). (2018). International handbook of research in statistics education. Springer. http://link.springer.com/10.1007/978-3-319-66195-7
Brown, M. (2016). Engaging students in quantitative methods: Real questions, real data. In Promoting understanding of statistics about society. Proceedings of the roundtable conference of the International Association of Statistics Education (IASE). ISI/IASE.
Carmichael, C. S. (2010). The development of middle school children’s interest in statistical literacy. [Doctoral dissertation, University of Tasmania].
Carotenuto, G., Di Martino, P., & Lemmi, M. (2021). Students’ suspension of sense making in problem solving. ZDM Mathematics Education, 53, 817–830.
Cobb, P., Confrey, J., DiSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.
Engel, J. (2017). Statistical literacy for active citizenship: A call for data science education. Statistics Education Research Journal, 16(1), 44–49. https://doi.org/10.52041/serj.v16i1.213
Fallstrom, S., Firouzian, S., Kubo, K., & Peck, R. (2021). Increasing student engagement at two-year colleges using socially relevant contexts. https://www.causeweb.org/cause/uscots/uscots21/workshop/12-2
Ferligoj, A. (2015). How to improve statistical literacy? Metodoloski Zvezki, 12(1), 1–10.
GAISE College Report ASA Revision Committee. (2016). Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016. AMSTAT. http://www.amstat.org/education/gaise
Gal, I. (2002). Adults’ statistical literacy: Meanings, components, responsibilities. International Statistical Review, 70(1), 1–25. https://doi.org/10.1111/j.1751-5823.2002.tb00336.x
Gal, I. (2019). Understanding statistical literacy: About knowledge of contexts and models. Actas Del Tercer Congreso Internacional Virtual de Educación Estadística. http://digibug.ugr.es/bitstream/handle/10481/55029/gal.pdf?sequence=1&isAllowed=y
Garfield, J., del Mas, R., & Zieffler, A. (2010). Assessing important learning outcomes in introductory tertiary statistics courses. In P. Bidgood, N. Hunt, & F. Jolliffe (Eds.) Assessment Methods in Statistical Education: An International Perspective (pp. 75–86). Wiley.
Garfield, J., DelMas, R., & Zieffler, A. (2012). Developing statistical modelers and thinkers in an introductory, tertiary-level statistics course. ZDM Mathematics Education, 44(7), 883–898. https://doi.org/10.1007/s11858-012-0447-5
Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12(3), 306–355. https://doi.org/10.1016/0010-0285(80)90013-4
Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 22–25. https://doi.org/10.52041/serj.v16i1.209
Kusairi, S., Hidayat, A., & Hidayat, N. (2017). Web-based diagnostic test: Introducing isomorphic items to assess students’ misconceptions and error patterns. Chemistry: Bulgarian Journal of Science Education, 26(4).
Kusairi, S., Puspita, D. A., Suryadi, A., & Suwono, H. (2020). Physics formative feedback game: Utilization of isomorphic multiple-choice items to help students learn kinematics. TEM Journal, 9(4), 1625–1632. https://doi.org/10.18421/TEM94-39
Lee, H., & Tran, D. (2015). Statistical habits of mind. Teaching Statistics through Data Investigations MOOC-Ed. Friday Institute for Educational Innovation. https://www-data.fi.ncsu.edu/wp-content/uploads/2020/12/12125035/Habitsofmind.pdf
Lehrer, R., & Schauble, L. (2007). A developmental approach for supporting the epistemology of modeling. In W. Blum, P. L. Galbraith, H.-W. Henn, & M. Niss (Eds.), Modeling and applications in mathematics education (14th ed., pp. 153–160). Springer.
Lemon, J., (2006). Plotrix: A package in the red light district of R. R-News, 6(4), 8–12.
Lin, S.-Y. Y., & Singh, C. (2011). Using isomorphic problems to learn introductory physics. Physical Review Special Topics: Physics Education Research, 7(2), Article 20104. https://doi.org/10.1103/PhysRevSTPER.7.020104
Livingston, S. A. (2004). Equating test scores (Without IRT). Educational Testing Service.
Lovett, M. C., & Greenhouse, J. R. (2000). Applying Cognitive Theory to Statistics Instruction. American Statistician, 54(3), 196–206. https://doi.org/10.1080/00031305.2000.10474545
Luger, G. F., & Bauer, M. A. (1978). Transfer effects in isomorphic problem situations. Acta Psychologica, 42(2), 121–131. https://doi.org/10.1016/0001-6918(78)90011-2
Millar, R., & Manoharan, S. (2021). Repeat individualized assessment using isomorphic questions: a novel approach to increase peer discussion and learning. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00257-y
Parker, M. C., Guzdial, M., & Engleman, S. (2016). Replication, validation, and use of a language independent CS1 knowledge assessment. ICER 2016. Proceedings of the 2016 ACM Conference on International Computing Education Research, Melbourne, Australia, pp. 93–101. https://doi.org/10.1145/2960310.2960316
Pearl, D. K., Garfield, J. B., DelMas, R. C., Groth, R. E., Kaplan, J. J., McGowan, H., & Lee, H. S. (2012). Connecting research to practice in a culture of assessment for introductory college-level statistics. https://www.amstat.org/docs/default-source/amstat-documents/researchreport_dec_2012.pdf
R Core Team. (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/
Rao, C. R. (1975). Teaching of statistics at the secondary level An interdisciplinary approach. International Journal of Mathematical Education in Science and Technology, 6(2), 151–162.
Ratnawati, O. A., Siswono, T. Y. E., & Wijayanti, P. (2020). Statistical literacy comprehension of students in the context of COVID-19 with collaborative problem solving (CPS). Math Didactic: Jurnal Pendidikan Mathematika, 6(3), 264–276.
Sabbag, A., Garfield, J., & Zieffler, A. (2018). Assessing statistical literacy and statistical reasoning: The REALI instrument. Statistics Education Research Journal, 17(2), 141–160. https://doi.org/10.52041/serj.v17i2.163
Sanchez, J. (2007). Building statistical literacy assessment tools with the IASE/ISLP. In B. Phillips & L. Weldon (Eds.), Assessing student learning in statistics. IASE/ISI Satellite Conference. https://iase-web.org/documents/papers/sat2007/Sanchez.pdf
Schield, M. (2004). Statistical literacy curriculum design. In G. Burrill & M. Camden (Eds.), Curricular development in statistics education. International Association for Statistical Education Roundtable (pp. 54–74).
Sharma, S. (2017). Definitions and models of statistical literacy: A literature review. Open Review of Educational Research, 4(1), 118–133. https://doi.org/10.1080/23265507.2017.1354313
Suganda, T., Kusairi, S., Azizah, N., & Parno, P. (2020). The correlation of isomorphic, open-ended, and conventional score on the ability to solve kinematics graph questions. Jurnal Penelitian & Pengembangan Pendidikan Fisika, 6(2), 173–180. https://doi.org/10.21009/1.06204
Suhermi, & Widjajanti, D. B. (2020). What are the roles of technology in improving student statistical literacy? Journal of Physics: Conference Series, the 3rd International Seminar on Innovation in Mathematics and Mathematics Education, 1581, Article 012067. https://doi.org/10.1088/1742-6596/1581/1/012067
Utts, J. (2021). Enhancing data science ethics through statistical education and practice. International Statistical Review, 89(1), 1–17. https://doi.org/10.1111/insr.12446
Wallman, K. K. (1993). Enhancing statistical literacy: Enriching our society. Journal of the American Statistical Association, 88(421), 1–8. https://doi.org/10.1080/01621459.1993.10594283
Watson, J. M. (1998). The role of statistical literacy in decisions about risk: Where to start. For the Learning of Mathematics, 18(3), 25–27.
Watson, J. M. (2011). Foundations for improving statistical literacy. Statistical Journal of the IAOS, 27, 197–204. https://doi.org/10.3233/SJI-2011-0728
Watson, J. M., & Callingham, R. (2003). Statistical literacy: A complex hierarchical construct. Statistics Education Research Journal, 2(2), 3–46. https://doi.org/10.52041/serj.v2i2.553
Weiland, T. (2017). Problematizing statistical literacy: An intersection of critical and statistical literacies. Educational Studies in Mathematics, 96(1), 33–47. https://doi.org/10.1007/s10649-017-9764-5
Wickham, H., (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag.
Wilks, S. S. (1951). Presidential address. Journal of the American Statistical Association, 46(253), 1–18. https://www.causeweb.org/cause/resources/library/r1266/
Williamson, D. M., Johnson, M. S., Sinharay, S., & Bejar, I. I. (2002). Hierarchical IRT examination of isomorphic equivalence of complex structured response tasks.
Ziegler, L. (2014). Reconceptualizing statistics literacy: Developing an assessment for the modern introductory statistics course [University of Minnesota]. http://hdl.handle.net/11299/165153
Ziegler, L., & Garfield, J. (2018). Developing a statistical literacy assessment for the modern introductory statistics course. Statistics Education Research Journal, 17(2), 161–178. https://doi.org/10.52041/serj.v17i2.164