Beyond Correct Answers: Revealing Pseudo-Understanding through Correspondence Analysis of Conceptual Relations and Chemical Representations in Chemical Kinetics

Suandi Sidauruk, Ruli Meiliawati, Sisca Alvionita, Triliyansi Triliyansi

Abstract

Correct answers in chemistry assessments do not always reflect adequate conceptual understanding, particularly when students engage with different forms of chemical representation. This issue is important because students may successfully solve problems without constructing appropriate conceptual explanations. This study investigated the distribution of Conceptual Understanding (CU), Transitional Understanding (TU), Procedural Knowledge (PK), and Misconception (MC) across macroscopic, submicroscopic, and symbolic representations in chemical kinetics. It also examined the association between representation types and understanding categories and explored aggregate patterns related to pseudo-understanding. A descriptive and associative quantitative design was employed with 254 Grade 11 students. Data were collected using 14 three-tier diagnostic test items, generating 3,556 responses. Data analysis included descriptive statistics, 10,000 restricted within-student permutation tests, Cramer’s V, and Correspondence Analysis (CA). The results showed that PK was dominant in macroscopic representation (35.6%), TU was highest in submicroscopic representation (28.0%), and MC and PK occurred equally in symbolic representation (27.4% each). CU showed a relatively stable distribution across representations (20.9–23.1%). Although representation and understanding category were significantly associated (χ²(6) = 44.778, pperm < 0.001), the association was very weak (Cramer’s V = 0.079; inertia = 0.013). The findings indicate that correct answers may occur without adequate conceptual reasoning and support the need for diagnostic assessments that evaluate students’ reasoning and confidence alongside answer accuracy.

Keywords

chemical representations; chemical kinetics; correspondence analysis; diagnostic assessment; pseudo-understanding; three-tier test

Full Text:

PDF

References

[1] A. H. Johnstone, “Chemistry teaching—Science or alchemy?,” J. Chem. Educ., vol. 74, no. 3, pp. 262–268, 1997, doi: 10.1021/ed074p262.

[2] V. Talanquer, “Macro, submicro, and symbolic: The many faces of the chemistry ‘triplet’,” Int. J. Sci. Educ., vol. 33, no. 2, pp. 179–195, 2011, doi: 10.1080/09500690903386435.

[3] M. M. Cooper and R. L. Stowe, “Chemistry education research—From personal empiricism to evidence, theory, and informed practice,” Chem. Rev., vol. 118, no. 12, pp. 6053–6087, 2018, doi: 10.1021/acs.chemrev.8b00020.

[4] V. Talanquer, “The complexity of reasoning about and with chemical representations,” JACS Au, vol. 2, no. 12, pp. 2658–2669, 2022, doi: 10.1021/jacsau.2c00498.

[5] R. B. Kozma and J. Russell, “Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena,” J. Res. Sci. Teach., vol. 34, no. 9, pp. 949–968, 1997, doi: 10.1002/(SICI)1098-2736(199711)34:9<949::AID-TEA7>3.0.CO;2-U.

[6] D. F. Treagust, G. Chittleborough, and T. L. Mamiala, “The role of submicroscopic and symbolic representations in chemical explanations,” Int. J. Sci. Educ., vol. 25, no. 11, pp. 1353–1368, 2003, doi: 10.1080/0950069032000070306.

[7] V. Taskin and S. Bernholt, “Students’ understanding of chemical formulae: A review of empirical research,” Int. J. Sci. Educ., vol. 36, no. 1, pp. 157–185, 2014, doi: 10.1080/09500693.2012.744492.

[8] D. F. Treagust, R. Duit, and H. E. Fischer, Eds., Multiple Representations in Physics Education, vol. 10. Cham, Switzerland: Springer International Publishing, 2017, doi: 10.1007/978-3-319-58914-5.

[9] M. Stieff, M. Hegarty, and G. Deslongchamps, “Identifying representational competence with multi-representational displays,” Cogn. Instr., vol. 29, no. 1, pp. 123–145, 2011, doi: 10.1080/07370008.2010.507318.

[10] K. Bain and M. H. Towns, “A review of research on the teaching and learning of chemical kinetics,” Chem. Educ. Res. Pract., vol. 17, no. 2, pp. 246–262, 2016, doi: 10.1039/C5RP00176E.

[11] Y. K. Yan and R. Subramaniam, “Using a multi-tier diagnostic test to explore the nature of students’ alternative conceptions on reaction kinetics,” Chem. Educ. Res. Pract., vol. 19, no. 1, pp. 213–226, 2018, doi: 10.1039/C7RP00143F.

[12] Habiddin and E. M. Page, “Uncovering students’ genuine misconceptions: Evidence to inform the teaching of chemical kinetics,” Acta Chim. Slov., vol. 70, no. 2, pp. 184–195, 2023, doi: 10.17344/acsi.2022.7880.

[13] D. K. Gurel, A. Eryilmaz, and L. C. McDermott, “A review and comparison of diagnostic instruments to identify students’ misconceptions in science,” EURASIA J. Math. Sci. Technol. Educ., vol. 11, no. 5, pp. 989–1008, 2015, doi: 10.12973/eurasia.2015.1369a.

[14] I. Caleon and R. Subramaniam, “Development and application of a three-tier diagnostic test to assess secondary students’ understanding of waves,” Int. J. Sci. Educ., vol. 32, no. 7, pp. 939–961, 2010, doi: 10.1080/09500690902890130.

[15] I. S. Caleon and R. Subramaniam, “Do students know what they know and what they don’t know? Using a four-tier diagnostic test to assess the nature of students’ alternative conceptions,” Res. Sci. Educ., vol. 40, no. 3, pp. 313–337, 2010, doi: 10.1007/s11165-009-9122-4.

[16] A. Cetin-Dindar and O. Geban, “Development of a three-tier test to assess high school students’ understanding of acids and bases,” Procedia Soc. Behav. Sci., vol. 15, pp. 600–604, 2011, doi: 10.1016/j.sbspro.2011.03.147.

[17] A. A. diSessa, “A history of conceptual change research: Threads and fault lines,” in The Cambridge Handbook of the Learning Sciences, 2nd ed., R. K. Sawyer, Ed. Cambridge, U.K.: Cambridge University Press, 2014, pp. 88–108, doi: 10.1017/CBO9781139519526.007.

[18] S. Vosniadou, Ed., International Handbook of Research on Conceptual Change, 2nd ed. New York, NY, USA: Routledge, 2013, doi: 10.4324/9780203154472.

[19] T. Phajan, K. Cojorn, and T. N. N. Dien, “Exploring the representational competence of eleventh-grade students on the rates of chemical reactions,” J. Pendidik. IPA Indones., vol. 14, no. 2, pp. 351–363, 2025, doi: 10.15294/jpii.v14i2.24509.

[20] M. Greenacre, Correspondence Analysis in Practice, 3rd ed. Boca Raton, FL, USA: CRC Press, 2017, doi: 10.1201/9781315369983.

[21] E. J. Beh and R. Lombardo, “Correspondence analysis using the Cressie–Read family of divergence statistics,” Int. Stat. Rev., vol. 92, no. 1, pp. 17–42, 2024, doi: 10.1111/insr.12541.

[22] D. F. Treagust, “Development and use of diagnostic tests to evaluate students’ misconceptions in science,” Int. J. Sci. Educ., vol. 10, no. 2, pp. 159–169, 1988, doi: 10.1080/0950069880100204.

[23] K. S. Taber, Chemical Misconceptions: Prevention, Diagnosis and Cure: Theoretical Background, vol. 1. London, U.K.: Royal Society of Chemistry, 2002.

[24] A. L. Chandrasegaran, D. F. Treagust, and M. Mocerino, “The development of a two-tier multiple-choice diagnostic instrument for evaluating secondary school students’ ability to describe and explain chemical reactions using multiple levels of representation,” Chem. Educ. Res. Pract., vol. 8, no. 3, pp. 293–307, 2007, doi: 10.1039/B7RP90006F.

[25] S. Ainsworth, “DeFT: A conceptual framework for considering learning with multiple representations,” Learn. Instr., vol. 16, no. 3, pp. 183–198, 2006, doi: 10.1016/j.learninstruc.2006.03.001.

[26] L. R. Aiken, “Three coefficients for analyzing the reliability and validity of ratings,” Educ. Psychol. Meas., vol. 45, no. 1, pp. 131–142, 1985, doi: 10.1177/0013164485451012.

[27] A. Fink, J. D. Young, N. K. Vuppala, and R. F. Frey, “Mixed-methods exploration of students’ written belonging explanations from general chemistry at a selective institution,” Chem. Educ. Res. Pract., vol. 24, no. 1, pp. 327–352, 2023, doi: 10.1039/D2RP00166G.

[28] D. K. Walker, V. P. Bustamante, H. E. Blomgren, J. D. Edwards, and R. F. Frey, “A mixed methods study exploring students’ open-ended responses about course inclusivity in General Chemistry 1,” J. Chem. Educ., vol. 101, no. 4, pp. 1403–1415, 2024, doi: 10.1021/acs.jchemed.3c00781.

[29] A. M. Winkler, M. A. Webster, D. Vidaurre, T. E. Nichols, and S. M. Smith, “Multi-level block permutation,” NeuroImage, vol. 123, pp. 253–268, 2015, doi: 10.1016/j.neuroimage.2015.05.092.

[30] A. M. Winkler, O. Renaud, S. M. Smith, and T. E. Nichols, “Permutation inference for canonical correlation analysis,” NeuroImage, vol. 220, Art. no. 117065, 2020, doi: 10.1016/j.neuroimage.2020.117065.

[31] N. W. Koning, “More power by using fewer permutations,” Biometrika, vol. 111, no. 4, pp. 1405–1412, 2024, doi: 10.1093/biomet/asae031.

[32] J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ, USA: Lawrence Erlbaum Associates, 1988. doi: 10.4324/9780203771587.

DOI: https://doi.org/10.20961/jkpk.v11i2.118027

Refbacks

  • There are currently no refbacks.


Bookmark and Share