Catalyzing teacher moves in small-group problem solving: a quantitative discourse analysis

References

• Alcalá, L., Rogoff, B., and López Fraire, A., 2018. Sophisticated collaboration is common among Mexican-heritage US children. Proceedings of the national academy of sciences, 115 (45), 11377–11384. doi:10.1073/pnas.1805707115.
   PubMed Web of Science ®Google Scholar
• Auerbach, A.J., et al., 2018. Teacher knowledge for active-learning instruction: expert–novice comparison reveals differences. CBE—life sciences education, 17 (1), ar12. doi:10.1187/cbe.17-07-0149.
   PubMed Web of Science ®Google Scholar
• Bicak, Z., 2020. Modeling a causal relationship between Poisson processes. Unpublished thesis. San Marcos, TX: Texas State University.
   Google Scholar
• Boaler, J., and Greeno, J.G., 2000. Identity, agency, and knowing. In: Multiple perspectives on mathematics teaching and learning, 171. doi:10.5040/9798400688362.0011
   Google Scholar
• Cazden, C.B., 2001. Classroom discourse: the language of teaching and learning. 2nd ed. Portsmouth: Heinemann.
   Google Scholar
• Chakrabarti, P., et al., 2020. Compound Poisson process and its applications in business. In: Proceedings of the 2020 international conference on business and technology, 678–685. doi:10.1007/978-981-15-1420-3_72.
   Google Scholar
• Chapin, S.H., et al., 2009. Classroom discussions: using math talk to help students learn, Grades K-6. Math Solutions.
   Google Scholar
• Chiu, M.M., 2004. Adapting teacher interventions to student needs during cooperative learning: how to improve student problem solving and time on-task. American educational research journal, 41 (2), 365–399. doi:10.3102/00028312041002365.
   Web of Science ®Google Scholar
• Chiu, M., 2008. Flowing toward correct contributions during group problem solving: a statistical discourse analysis. The journal of the learning sciences 17 (3), 415–463.
   Web of Science ®Google Scholar
• Chiu, M., and Khoo, L., 2003. Rudeness and status effects during group problem solving: do they bias evaluations and reduce the likelihood of correct solutions? Journal of educational psychology, 95, 506–523. doi:10.1037/0022-0663.95.3.506.
   Web of Science ®Google Scholar
• Chiu, M.M., and Khoo, L., 2005. A new method for analyzing sequential processes: dynamic multilevel analysis. Small group research, 36 (5), 600–631. doi:10.1177/1046496405279309.
   Web of Science ®Google Scholar
• Chiu, M., and Lehmann-Willenbrock, N., 2016. Statistical discourse analysis: modeling sequences of individual actions during group interactions across time. Group dynamics: theory, research, and practice, 20, 242–258. doi:10.1037/gdn0000048.
   Web of Science ®Google Scholar
• Common Core State Standards Initiative, 2010. Common core state standards for mathematics. Washington, DC: National Governors Association Center for Best Practices, Council of Chief State School Officers. http://www.corestandards.org.
   Google Scholar
• Corbin, J., and Strauss, A., 1990. Grounded theory research: procedures, canons and evaluative criteria. Zeitschrift für soziologie, 19 (6), 418–427. doi:10.1515/zfsoz-1990-0602.
   Web of Science ®Google Scholar
• DeJarnette, A.F., 2022. What do correct answers reveal? The interpersonal and mathematical aspects of students’ interactions during groupwork in seventh grade mathematics. Journal of the learning sciences, 31 (4-5), 509–544. doi:10.1080/10508406.2022.2073232.
   Web of Science ®Google Scholar
• DeJarnette, A.F., and Hord, C., 2022. Mathematics questioning practices of pre-service special education teachers providing algebra tutoring for students with learning disabilities. Teacher education and special education, 45 (4), 309–330. doi:10.1177/08884064211073573.
   Web of Science ®Google Scholar
• Dobrow, R.P., 2016. Introduction to stochastic processes with R. Hoboken, NJ: Wiley.
   Google Scholar
• Gillborn, D., Warmington, P., and Demack, S., 2018. QuantCrit: education, policy, ‘big data’ and principles for a critical race theory of statistics. Race ethnicity and education, 21 (2), 158–179. doi:10.1080/13613324.2017.1377417.
   Web of Science ®Google Scholar
• González, G., and DeJarnette, A.F., 2015. Teachers’ and students’ negotiation moves when teachers scaffold group work. Cognition and instruction, 33 (1), 1–45. doi:10.1080/07370008.2014.987058.
   Web of Science ®Google Scholar
• Herbel-Eisenmann, B.A., and Otten, S., 2011. Mapping mathematics in classroom discourse. Journal for research in mathematics education, 42 (5), 451–485. doi:10.5951/jresematheduc.42.5.0451.
   Web of Science ®Google Scholar
• Kim, S.-H., and Whitt, W., 2014. Are call center and hospital arrivals well modeled by nonhomogeneous Poisson processes? Manufacturing & service operations management, 16 (3), 464–480. doi:10.1287/msom.2014.0490.
   Web of Science ®Google Scholar
• Lawrence, E., et al., 2017. The nonhomogeneous Poisson process for fast radio burst rates. The astronomical journal, 154 (3), 117. doi:10.3847/1538-3881/aa844e.
   Web of Science ®Google Scholar
• Maxwell, J.A., 2005. Qualitative research design: an interactive approach. 2nd ed. Thousand Oaks, CA: Sage.
   Google Scholar
• Michaels, S., and O’Connor, C., 2015. Conceptualizing talk moves as tools: professional development approaches for academically productive discussions, In: L.B. Resnick, C.S.C. Asterhan, and S.N. Clarke, eds. Socializing intelligence through academic talk and dialogue. Washington, DC: American Educational Research Association, 347–361. doi:10.3102/978-0-935302-43-1_27.
   Google Scholar
• Miles, M.B., and Huberman, A.M., 1994. Qualitative data analysis. 2nd ed. Thousand Oaks, CA: Sage.
   Google Scholar
• Moore, K.C., et al., 2022. Abstracted quantitative structures: using quantitative reasoning to define concept construction. In: G. Karagöz Akar, İ.Ö. Zembat, S. Arslan, and P.W. Thompson, eds. Quantitative reasoning in mathematics and science education. Cham: Springer International Publishing, 35–69. doi:10.1007/978-3-031-14553-7_3.
   Google Scholar
• Moore, K.C., and Carlson, M.P., 2012. Students’ images of problem contexts when solving applied problems. The journal of mathematical behavior, 31 (1), 48–59. doi:10.1016/j.jmathb.2011.09.001.
   Google Scholar
• Nathan, M.J., and Knuth, E.J., 2003. A study of whole classroom mathematical discourse and teacher change. Cognition and instruction, 21 (2), 175–207. doi:10.1207/S1532690XCI2102_03.
   Web of Science ®Google Scholar
• Post, M., and Prediger, S., 2024. Teaching practices for unfolding information and connecting multiple representations: the case of conditional probability information. Mathematics education research journal 36 (1), 97–129. doi:10.1007/s13394-022-00431-z.
   Web of Science ®Google Scholar
• Prediger, S., 2022. Leveraging and connecting conceptions of amount and change: a content-specific approach to adaptive teaching practices. The journal of mathematical behavior, 66, 100970. doi:10.1016/j.jmathb.2022.100970.
   Web of Science ®Google Scholar
• Prediger, S., and Zindel, C., 2017. School academic language demands for understanding functional relationships: a design research project on the role of language in reading and learning. EURASIA journal of mathematics, science and technology education, 13 (7b), 4157–4188. doi:10.12973/eurasia.2017.00804a.
   Google Scholar
• Richardson, K., et al., 2014. Āwhina reloaded: updated results from a programme for Māori and Pacific tertiary graduate and postgraduate success in science, engineering, and architecture and design. In: F. Cram, H. Phillips, P. Sauni, and C. Tuagalu, eds. Māori and Pasifika higher education horizons. Leeds: Emerald Group Publishing Limited, 179–200. doi:10.1108/S1479-364420140000015017.
   Google Scholar
• Smith, J., and Thompson, P.W., 2007. Quantitative reasoning and the development of algebraic reasoning. In: J.J. Kaput, D.W. Carraher, and M.L. Blanton, eds. Algebra in the early grades, 95–132. New York: Lawrence Erlbaum.
   Google Scholar
• Theobald, E.J., et al., 2019. Beyond linear regression: a reference for analyzing common data types in discipline based education research. Physical review physics education research, 15 (2), 020110. doi:10.1103/PhysRevPhysEducRes.15.020110.
   Web of Science ®Google Scholar
• Trujillo, C.M., Anderson, T.R., and Pelaez, N.J., 2016. Exploring the MACH model’s potential as a metacognitive tool to help undergraduate students monitor their explanations of biological mechanisms. CBE—life sciences education, 15 (2), ar12. doi:10.1187/cbe.15-03-0051.
   PubMed Web of Science ®Google Scholar
• Webb, N.M., Nemer, K.M., and Ing, M., 2006. Small-group reflections: parallels between teacher discourse and student behavior in peer-directed groups. Journal of the learning sciences, 15 (1), 63–119. doi:10.1207/s15327809jls1501_8.
   Web of Science ®Google Scholar
• Zahner, W., et al. April 2019. Exploring patterns of quantitative reasoning cycles in linguistically diverse middle school students’ problem solving on a rate task. Paper presented at the Research Conference of the National Council of Teachers of Mathematics, San Diego, CA.
   Google Scholar
• Zentgraf, K., and Prediger, S., 2024. Demands and scaffolds for explaining the connection of multiple representations: revisiting the bottle-filling task. The journal of mathematical behavior, 73, 101118. doi:10.1016/j.jmathb.2023.101118.
   Web of Science ®Google Scholar