CoChat
Sign Up
Cited by 27Science Education and PedagogyEvolution and Science EducationInnovative Teaching Methods

Motivating and Shaping Scientific Argumentation in Lab Reports

Open access
Gouvea, Julia, Appleby, Lara, Fu, Liren, Wagh, Aditi·2022·CBE—Life Sciences Education
DOI 10.1187/cbe.21-11-0316PMC PMC9727606
Open this paper in CoChat — free account

Abstract

This study evaluates an iterative design experiment in an introductory lab course in which instruction was restructured and uncertainty in lab activities was increased. It is proposed that these changes to the rhetorical context helped motivate and shape more authentic engagement in scientific argumentation.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1187/cbe.21-11-0316

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 60

  1. Bazerman, C. (1988). Shaping written knowledge. Madison, WI: University of Wisconsin Press. 10.1126/science.248.4957.877 · DOI
  2. Bazerman, C. (2018). What does a model model? And for whom? Educational Psychologist, 53(4), 301– 318. 10.1080/00461520.2018.1496022 · DOI
  3. Berland, L. K. Hammer, D. (2012). Framing for scientific argumentation. Journal of Research in Science Teaching, 49(1), 68– 94. 10.1002/tea.20446 · DOI
  4. Berland, L. K. Reiser, B. J. (2011). Classroom communities’ adaptations of the practice of scientific argumentation. Science Education, 95(2), 191– 216. 10.1002/sce.20420 · DOI
  5. Biggs, J. Collis, K. F. (1982). Evaluating the quality of learning: The SOLO taxonomy (structure of the observed learning outcome). New York, NY: Academic Press.
  6. Bitzer, L. F. (1968). The rhetorical situation. Philosophy and Rhetoric, 1(1), 1– 15.
  7. Blikstein, P. Fuhrmann, T. Salehi, S. (2016). Using the bifocal modeling framework to resolve “Discrepant Events” between physical experiments and virtual models in biology. Journal of Science Education and Technology, 25(4), 513– 526. 10.1007/s10956-016-9623-7 · DOI
  8. Chen, Y. Benus, M. J. Hernandez, J. (2019). Managing uncertainty in scientific argumentation. Science Education, 103(5), 1235– 1276. 10.1002/sce.21527 · DOI
  9. Cobb, P. Confrey, J. DiSessa, A. A. Lehrer, R. Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9– 13. 10.3102/0013189X032001009 · DOI · Paper page
  10. Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213– 220. 19673146
  11. Engle, R. A. Conant, F. R. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20(4), 399– 483. 10.2307/3233901 · DOI
  12. Ford, M. J. (2005). The game, the pieces, and the players: Generative resources from two instructional portrayals of experimentation. Journal of the Learning Sciences, 14(4), 449– 487. 10.1207/s15327809jls1404_1 · DOI
  13. Ford, M. J. (2012). A dialogic account of sense-making in scientific argumentation and reasoning. Cognition and Instruction, 30(3), 207– 245. 10.1080/07370008.2012.689383 · DOI
  14. Ford, M. J. (2015). Educational implications of choosing “practice” to describe science in the Next Generation Science Standards. Science Education, 99(6), 1041– 1048. 10.1002/sce.21188 · DOI
  15. Goffman, E. (1986). Frame analysis. Northeastern University Press.
  16. Gouvea, J. Wagh, A. (2018). Exploring the unknown: Supporting students’ navigation of scientific uncertainty with coupled methodologies. In Proceedings of the 13th International Conference of the Learning Sciences (pp. 33– 40).
  17. Gouvea, J. S. Wagh, A. Hayes, R. Simon, M. R. (2022). Hybrid labs: How students use computer models to motivate and make meaning from experiments. In Pelaez N. J. Gardner S. M. Anderson T. (Eds.), Trends in teaching experimentation in the life sciences. Contributions from biology education research (pp. 395– 413). Switzerland: Springer Nature. 10.1007/978-3-030-98592-9_18 · DOI
  18. Hammer, D. Elby, A. Scherr, R. E. Redish, E. F. (2005). Resources, framing, and transfer. In Mestre, J. P. (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 89– 120). Greenwich, CT: Information Age.
  19. Hester, S. D. Nadler, M. Katcher, J. Elfring, L. K. Dykstra, E. Rezende, L. Bolger, M. S. (2018). Authentic Inquiry through Modeling in Biology (AIM-Bio): An introductory laboratory curriculum that increases undergraduates’ scientific agency and skills. CBE—Life Sciences Education, 17(4), ar63. 10.1187/cbe.18-06-0090 30496030 PMC6755888 · DOI
  20. Holmes, N. G. Bonn, D. A. (2014). Doing science or doing a lab? Engaging students with scientific reasoning during physics lab experiments. In 2013 Physics Education Research Conference Proceedings (pp. 185– 188). 10.1119/perc.2013.pr.034 · DOI
  21. Hu, D. Zwickl, B. M. Wilcox, B. R. Lewandowski, H. J. (2017). Qualitative investigation of students’ views about experimental physics. Physical Review Physics Education Research, 13(2), 1– 12. 10.1103/PhysRevPhysEducRes.13.020134 · DOI
  22. Jiménez-Aleixandre, M. P. Rodríguez, A. B. Duschl, R. A. (2000). “Doing the lesson” or “doing science”: Argument in high school genetics. Science Education, 84(6), 757– 792. 10.1002/1098-237X(200011)84:6<757::AID-SCE5>3.0.CO;2-F · DOI
  23. Kelly, G. J. Bazerman, C. (2003). How students argue scientific claims: A rhetorical-semantic analysis. Applied Linguistics, 24(1), 28– 55. 10.1093/applin/24.1.28 · DOI
  24. Kelly, G. J. Bazerman, C. Skukauskaite, A. Prothero, W. (2010). Rhetorical features of student science writing in introductory university oceanography. In Bazerman C. Krut R. Lunsford K. McLeod S. Null S. Rogers P. Stansell A. (Eds.), Traditions of writing research (pp. 265– 282). New York, NY: Routledge.
  25. Keys, C. W. (1999). Revitalizing instruction in scientific genres: Connecting knowledge production with writing to learn in science. Science Education, 83(2), 115– 130.
  26. Kuhn, D. Pease, M. (2008). What needs to develop in the development of inquiry skills? Cognition and Instruction, 26(4), 512– 559. 10.1080/07370000802391745 · DOI
  27. Lawson, A. E. (2002). Sound and faulty arguments generated by preservice biology teachers when testing hypotheses involving unobservable entities. Journal of Research in Science Teaching, 39(3), 237– 252. 10.1002/tea.10019 · DOI
  28. Lee, H.-S. Liu, O. L. Pallant, A. Roohr, K. C. Pryputniewicz, S. Buck, Z. E. (2014). Assessment of uncertainty-infused scientific argumentation. Journal of Research in Science Teaching, 51(5), 581– 605. 10.1002/tea.21147 · DOI
  29. Lee, H. S. Pallant, A. Pryputniewicz, S. Lord, T. Liu, O. L. (2017). Articulating uncertainty attribution as part of critical epistemic practice of scientific argumentation. In Smith B. K. Borge M. Emma Mercier E. Lim K. Y. (Eds.), Proceedings of the 12th International Conference on Computer Supported Collaborative Learning. Philadelphia, PA: International Society of the Learning Sciences.
  30. Libarkin, J. Ording, G. (2012). The utility of writing assignments in undergraduate bioscience. CBE—Life Sciences Education, 11(1), 39– 46. 10.1187/cbe.11-07-0058 22383616 PMC3292072 · DOI
  31. Lynch, M. (2010). Evolution of the mutation rate. Trends in Genetics, 26(8), 345– 352. 10.1016/j.tig.2010.05.003 20594608 PMC2910838 · DOI
  32. Manz, E. (2012). Understanding the codevelopment of modeling practice and ecological knowledge. Science Education, 96(6), 1071– 1105. 10.1002/sce.21030 · DOI
  33. Manz, E. (2015a). Representing student argumentation as functionally emergent from scientific activity. Review of Educational Research, 85(4), 553– 590. 10.3102/0034654314558490 · DOI
  34. Manz, E. (2015b). Resistance and the development of scientific practice: Designing the mangle into science instruction. Cognition and Instruction, 33(2), 89– 124. 10.1080/07370008.2014.1000490 · DOI
  35. Manz, E. Lehrer, R. Schauble, L. (2020). Rethinking the classroom science investigation. Journal of Research in Science Teaching, 57(7), 1148– 1174. 10.1002/tea.21625 · DOI
  36. McNeill, K. L. Lizotte, D. J. Krajcik, J. S. Marx, R. W. (2006). Supporting students’ construction of scientific explanations by fading scaffolds in instructional materials. Journal of the Learning Sciences, 15(2), 153– 191. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.2734
  37. Metz, K. E. (2004). Children’s understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction, 22(2), 219– 290. 10.1207/s1532690xci2202 · DOI
  38. Moon, A. Stanford, C. Cole, R. Towns, M. (2017). Analysis of inquiry materials to explain complexity of chemical reasoning in physical chemistry students’ argumentation. Journal of Research in Science Teaching, 54(10), 1322– 1346. 10.1002/tea.21407 · DOI
  39. Moskovitz, C. Kellogg, D. (2011). Inquiry-based writing in the laboratory course. Science, 332(6032), 919– 920. 21596978
  40. O'Neill, D. K. (2001). Knowing when you've brought them in: Scientific genre knowledge and communities of practice. Journal of the Learning Sciences, 10(3), 223– 264. 10.1207/S15327809JLS1003 · DOI
  41. Osborne, J. Erduran, S. Simon, S. (2004). Enhancing the quality of argumentation in school science. Journal of Research in Science Teaching, 41(10), 994– 1020. 10.1002/tea.20035 · DOI · Paper page
  42. Osborne, J. Henderson, J. B. MacPherson, A. Szu, E. Wild, A. Yao, S. Y. (2016). The development and validation of a learning progression for argumentation in science. Journal of Research in Science Teaching, 53(6), 821– 846. 10.1002/tea.21316 · DOI
  43. Petraglia, J. (1995). Spinning like a kite: A closer look at the pseudotransactional function of writing. JAC, 15(1), 19– 33.
  44. Petritis, S. J. Kelley, C. Talanquer, V. (2021). Exploring the impact of the framing of a laboratory experiment on the nature of student argumentation. Chemistry Education Research and Practice, 22(1), 122– 135. 10.1039/d0rp00268b · DOI
  45. Quitadamo, I. J. Kurtz, M. J. (2007). Learning to improve: Using writing to increase critical thinking performance in general education biology. CBE—Life Sciences Education, 6, 140– 154. 10.1187/cbe.06 17548876 PMC1885902 · DOI
  46. Russ, R. S. Berland, L. K. (2019). Invented science: A framework for discussing a persistent problem of practice. Journal of the Learning Sciences, 28(3), 279– 301. 10.1080/10508406.2018.1517354 · DOI
  47. Ryu, S. Sandoval, W. a. (2012). Improvements to elementary children’s epistemic understanding from sustained argumentation. Science Education, 96(3), 488– 526. 10.1002/sce.21006 · DOI · Paper page
  48. Sandoval, W. A. (2014). Conjecture mapping: An approach to systematic educational design research. Journal of the Learning Sciences, 23(1), 18– 36. 10.1080/10508406.2013.778204 · DOI
  49. Sandoval, W. A. Millwood, K. A. (2005). The quality of students’ use of evidence in written scientific explanations. Cognition and Instruction, 23(1), 23– 55. 10.1207/s1532690xci2301_2 · DOI
  50. Schen, M. (2017). A comparison of biology majors’ written arguments across the curriculum. Journal of Biological Education, 47(4), 224– 231. 10.1080/00219266.2013.788542 · DOI
  51. Scherr, R. E. Hammer, D. (2009). Student behavior and epistemological framing: Examples from collaborative active-learning activities in physics. Cognition and Instruction, 27(2), 147– 174. 10.1080/07370000902797379 · DOI
  52. Schwarz, B. B. Neuman, Y. Gil, J. Ilya, M. (2003). Construction of collective and individual knowledge in argumentative activity. Journal of the Learning Sciences, 12(2), 219– 256. 10.1207/S15327809JLS1202 · DOI
  53. Smith, E. M. Stein, M. M. Holmes, N. G. (2020). How expectations of confirmation influence students’ experimentation decisions in introductory labs. Physical Review Physics Education Research, 16(1), 10113. 10.1103/PhysRevPhysEducRes.16.010113 · DOI
  54. Stein, M. M. Smith, E. M. Holmes, N. G. (2018). Confirming what we know: Understanding questionable research practices in intro physics labs. Physics Education Research Conference Proceedings, 2018, 1– 4. 10.1119/perc.2018.pr.stein · DOI
  55. Tang, X. Coffey, J. E. Levin, D. M. (2015). Reconsidering the use of scoring rubrics in biology instruction. American Biology Teacher, 77(9), 669– 675. 10.1525/abt.2015.77.9.4.THE · DOI
  56. Tannen, D. (1979). What’s in a frame? Surface evidence for underlying expectations. In Freedle, R. (Ed.), New directions in discourse processing (pp. 137– 181).. Norwood, NJ: Ablex.
  57. Tannen, D. Wallat, C. (1987). Interactive frames and knowledge schemas in interaction: Examples from a medical examination/interview. Social Psychology Quarterly, 50(2), 205. 10.2307/2786752 · DOI
  58. Walker, J. P. Sampson, V. D. (2013). Argument-driven inquiry: Using the laboratory to improve undergraduates’ science writing skills through meaningful science writing, peer-review, and revision. Journal of Chemical Education, 90, 1269– 1274. 10.1021/ed300656p · DOI · Paper page
  59. Xu, H. Talanquer, V. (2013). Effect of the level of inquiry of lab experiments on general chemistry students’ written reflections. Journal of Chemical Education, 90(1), 21– 28. 10.1021/ed3002368 · DOI
  60. Zagallo, P. Meddleton, S. Bolger, M. S. (2016). Teaching Real Data Interpretation with Models (TRIM): Analysis of student dialogue in a large-enrollment cell and developmental biology course. CBE—Life Sciences Education, 15(2), ar17. 10.1187/cbe.15-11-0239 27193288 PMC4909339 · DOI

Cited by

Sorted by citations

Authors
Gouvea, J., Appleby, L., Fu, L., & Wagh, A.
Affiliations
  • Tufts University (US)
  • Ministry of Education (SG)
  • Massachusetts Institute of Technology (US)
Corresponding author
Gouvea, Julia
ORCID
Venue
CBE—Life Sciences Education
Publisher
American Society for Cell Biology (ASCB)
Published
2022-12
Volume
21
Issue
4
Pages
ar71-ar71
ISSN
1931-7913 (ISSN-L)
DOI
10.1187/cbe.21-11-0316
PMC
PMC9727606
OpenAlex
W4297156824
Cited by
27
References
1
Citations by year
2026: 13 · 2025: 5 · 2024: 5 · 2023: 4
FWCI
7.55
Type
journal article
Language
en
Open access
Licence
cc-by-nc-sa
Locations
  • CBE—Life Sciences Education (publishedVersion, cc-by-nc-sa)
  • PubMed Central (publishedVersion, cc-by-nc-sa)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Education › Science Education and Pedagogy
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Evolution and Science Education
  • Social Sciences › Education › Innovative Teaching Methods
Keywords
scientific argumentation, laboratory reports, student motivation, scaffolding, learning environment, scientific practice, authentic learning, argumentation instruction
MeSH terms
Humans, Laboratories, Learning, Students, Writing, Knowledge

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review