Absence or Entrenchment? Belief Revision, Diagnostic Error and the Design of a Trial on Misconception Interventions in Mathematics
DOI:
https://doi.org/10.5281/zenodo.22712852Keywords:
belief revision, conceptual change, misconceptions, certainty of response index, aptitude-by-treatment interaction, simulation studyAbstract
Not knowing a mathematical relation and firmly believing its negation are distinct epistemic states that, in a ranked belief-base model, require different belief-change operations; grading both as one kind of error hides that difference from intervention studies. We model instruction as semi-revision by input of given credibility. Absence is revised by any input that delivers the target; a misconception yields only to input more credible than its strongest minimal support. Operators whose acceptance ignores either the base or the credibility predict no state-by-intervention interaction; this one predicts an interaction with no effect in absence. Under non-differential diagnosis the interaction is scaled by PPV + NPV − 1 (0.70 at sensitivity and specificity 0.85 and prevalence 0.53, 0.39 at prevalence 0.89). In published certainty tables for 93 students, high-certainty answers made up 98 of 110, 48 of 91 and 40 of 115 wrong answers in three groups. No intervention data exist. An ADEMP simulation reports operating characteristics of a proposed, not yet preregistered, 2 × 2 trial with classrooms allocated within schools. Under stipulated parameters, 16 schools of four classrooms with 40 students tested per classroom give power 0.96 for a latent-state interaction of 0.30, attenuated in the diagnosed strata, if about half are analysed (0.86 if a third are); type I error of the primary test stayed within its declared tolerance in every null condition explored, and so did a secondary profile test with joint variance in the seven conditions examined. These are properties of a design under assumptions, not evidence about learning.
Received: 1 August 2026 | Peer review completed: 10 September 2026 | Accepted: 12 September 2026
References
[1] R. Stavy and D. Tirosh, "Intuitive rules in science and mathematics: The case of 'more of A - more of B'," International Journal of Science Education, vol. 18, no. 6, pp. 653-667, 1996, doi: 10.1080/0950069960180602.
[2] G. J. Posner, K. A. Strike, P. W. Hewson, and W. A. Gertzog, "Accommodation of a scientific conception: Toward a theory of conceptual change," Science Education, vol. 66, no. 2, pp. 211-227, 1982, doi: 10.1002/sce.3730660207.
[3] S. Vosniadou, "Capturing and modeling the process of conceptual change," Learning and Instruction, vol. 4, no. 1, pp. 45-69, 1994, doi: 10.1016/0959-4752(94)90018-3.
[4] S. Vosniadou and L. Verschaffel, "Extending the conceptual change approach to mathematics learning and teaching," Learning and Instruction, vol. 14, no. 5, pp. 445-451, 2004, doi: 10.1016/j.learninstruc.2004.06.014.
[5] C. von Aufschnaiter and C. Rogge, "Misconceptions or missing conceptions?," Eurasia Journal of Mathematics, Science and Technology Education, vol. 6, no. 1, 2010, doi: 10.12973/ejmste/75223.
[6] S. Hasan, D. Bagayoko, and E. L. Kelley, "Misconceptions and the Certainty of Response Index (CRI)," Physics Education, vol. 34, no. 5, pp. 294-299, 1999, doi: 10.1088/0031-9120/34/5/304.
[7] A. C. Leon and M. Heo, "Sample sizes required to detect interactions between two binary fixed-effects in a mixed-effects linear regression model," Computational Statistics & Data Analysis, vol. 53, no. 3, pp. 603-608, 2009, doi: 10.1016/j.csda.2008.06.010.
[8] K. J. Preacher and S. K. Sterba, "Aptitude-by-treatment interactions in research on educational interventions," Exceptional Children, vol. 85, no. 2, pp. 248-264, 2019, doi: 10.1177/0014402918802803.
[9] C. E. Alchourrón, P. Gärdenfors, and D. Makinson, "On the logic of theory change: Partial meet contraction and revision functions," Journal of Symbolic Logic, vol. 50, no. 2, pp. 510-530, 1985, doi: 10.2307/2274239.
[10] E. Fermé and S. O. Hansson, "AGM 25 years," Journal of Philosophical Logic, vol. 40, no. 2, pp. 295-331, 2011, doi: 10.1007/s10992-011-9171-9.
[11] M. Limón, "On the cognitive conflict as an instructional strategy for conceptual change: A critical appraisal," Learning and Instruction, vol. 11, no. 4-5, pp. 357-380, 2001, doi: 10.1016/S0959-4752(00)00037-2.
[12] C. A. Chinn and W. F. Brewer, "The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction," Review of Educational Research, vol. 63, no. 1, pp. 1-49, 1993, doi: 10.3102/00346543063001001.
[13] P. Potvin, "Response of science learners to contradicting information: A review of research," Studies in Science Education, vol. 59, no. 1, pp. 67-108, 2023, doi: 10.1080/03057267.2021.2004006.
[14] S. O. Hansson, "A survey of non-prioritized belief revision," Erkenntnis, vol. 50, no. 2-3, pp. 413-427, 1999, doi: 10.1023/A:1005534223776.
[15] S. O. Hansson, E. L. Fermé, J. Cantwell, and M. A. Falappa, "Credibility limited revision," Journal of Symbolic Logic, vol. 66, no. 4, pp. 1581-1596, 2001, doi: 10.2307/2694963.
[16] C. A. Berrío Pérez, M. Falk de Losada, and O. J. Rojas Velázquez, "Las pseudoparadojas geométricas para potencializar el concepto de área en el séptimo grado," Vidya, vol. 40, no. 1, pp. 5-27, 2020, doi: 10.37781/vidya.v40i1.3115.
[17] Á. M. Sánchez-Ossa, O. J. Rojas Velázquez, and C. A. Rodríguez-Nieto, "Advances in the characterization of university students' visual thinking through inspection heuristics and representation," Eurasia Journal of Mathematics, Science and Technology Education, vol. 21, no. 4, art. em2624, 2025, doi: 10.29333/ejmste/16223.
[18] S. O. Hansson, "Semi-revision," Journal of Applied Non-Classical Logics, vol. 7, no. 1-2, pp. 151-175, 1997, doi: 10.1080/11663081.1997.10510904.
[19] B. Nebel, "Syntax based approaches to belief revision," in Belief Revision, P. Gärdenfors, Ed. Cambridge, U.K.: Cambridge University Press, 1992, pp. 52-88, doi: 10.1017/CBO9780511526664.003.
[20] J. R. Busemeyer and L. E. Jones, "Analysis of multiplicative combination rules when the causal variables are measured with error," Psychological Bulletin, vol. 93, no. 3, pp. 549-562, 1983, doi: 10.1037/0033-2909.93.3.549.
[21] K. R. Blake and S. Gangestad, "On attenuated interactions, measurement error, and statistical power: Guidelines for social and personality psychologists," Personality and Social Psychology Bulletin, vol. 46, no. 12, pp. 1702-1711, 2020, doi: 10.1177/0146167220913363.
[22] I. Bross, "Misclassification in 2 × 2 tables," Biometrics, vol. 10, no. 4, p. 478, 1954, doi: 10.2307/3001619.
[23] Y. Wardat, A. M. Jarrah, and S. Gningue, "Misconception on addition and subtraction of fractions in seventh-grade middle school students," Eurasia Journal of Mathematics, Science and Technology Education, vol. 18, no. 6, art. em2115, 2022, doi: 10.29333/ejmste/12070.
[24] R. Hayati and W. Setyaningrum, "Identification of misconceptions in middle school mathematics utilizing Certainty of Response Index," Journal of Physics: Conference Series, vol. 1320, no. 1, art. 012041, 2019, doi: 10.1088/1742-6596/1320/1/012041.
[25] T. P. Morris, I. R. White, and M. J. Crowther, "Using simulation studies to evaluate statistical methods," Statistics in Medicine, vol. 38, no. 11, pp. 2074-2102, 2019, doi: 10.1002/sim.8086.
[26] N. L. Schroeder and A. C. Kucera, "Refutation text facilitates learning: A meta-analysis of between-subjects experiments," Educational Psychology Review, vol. 34, no. 2, pp. 957-987, 2022, doi: 10.1007/s10648-021-09656-z.
[27] Ç. Paçacı, U. Üstün, and Ö. F. Özdemir, "Effectiveness of conceptual change strategies in science education: A meta-analysis," Journal of Research in Science Teaching, vol. 61, no. 6, pp. 1263-1325, 2024, doi: 10.1002/tea.21887.
[28] J. Van Hoof, A.-S. Engelen, and W. Van Dooren, "How robust are learners' misconceptions of fraction magnitude? An intervention study comparing the use of refutation and expository text," Educational Psychology, vol. 41, no. 5, pp. 524-543, 2021, doi: 10.1080/01443410.2021.1908521.
[29] A.-S. Dersch, I. Thacker, and A. Eitel, "Should we tell teachers that they are (in)correct? How personalized refutation text promotes or hampers conceptual change," Educational Psychology Review, vol. 38, no. 1, art. 17, 2026, doi: 10.1007/s10648-026-10116-9.
[30] S. O. Hansson, "Kernel contraction," Journal of Symbolic Logic, vol. 59, no. 3, pp. 845-859, 1994, doi: 10.2307/2275912.
[31] D. Makinson, "Screened revision," Theoria, vol. 63, no. 1-2, pp. 14-23, 1997, doi: 10.1111/j.1755-2567.1997.tb00737.x.
[32] R. Elio and F. J. Pelletier, "Belief change as propositional update," Cognitive Science, vol. 21, no. 4, pp. 419-460, 1997, doi: 10.1207/s15516709cog2104_2.
[33] G. Priest, "Paraconsistent belief revision," Theoria, vol. 67, no. 3, pp. 214-228, 2001, doi: 10.1111/j.1755-2567.2001.tb00204.x.
[34] M. Carrara, D. Fazio, and M. Pra Baldi, "Paraconsistent belief revision: An algebraic investigation," Erkenntnis, vol. 89, no. 2, pp. 725-753, 2024, doi: 10.1007/s10670-022-00553-7.
[35] J. P. Smith III, A. A. diSessa, and J. Roschelle, "Misconceptions reconceived: A constructivist analysis of knowledge in transition," Journal of the Learning Sciences, vol. 3, no. 2, pp. 115-163, 1994, doi: 10.1207/s15327809jls0302_1.
[36] A. A. diSessa, "Toward an epistemology of physics," Cognition and Instruction, vol. 10, no. 2-3, pp. 105-225, 1993, doi: 10.1080/07370008.1985.9649008.
[37] R. Babai, H. Zilber, R. Stavy, and D. Tirosh, "The effect of intervention on accuracy of students' responses and reaction times to geometry problems," International Journal of Science and Mathematics Education, vol. 8, no. 1, pp. 185-201, 2010, doi: 10.1007/s10763-009-9169-8.
[38] N. M. McNeil and M. W. Alibali, "Why won't you change your mind? Knowledge of operational patterns hinders learning and performance on equations," Child Development, vol. 76, no. 4, pp. 883-899, 2005, doi: 10.1111/j.1467-8624.2005.00884.x.
[39] K. Loibl, I. Roll, and N. Rummel, "Towards a theory of when and how problem solving followed by instruction supports learning," Educational Psychology Review, vol. 29, no. 4, pp. 693-715, 2017, doi: 10.1007/s10648-016-9379-x.
[40] T. Sinha and M. Kapur, "When problem solving followed by instruction works: Evidence for productive failure," Review of Educational Research, vol. 91, no. 5, pp. 761-798, 2021, doi: 10.3102/00346543211019105.
[41] C. A. Barbieri and B. L. Devlin, "Targeting fraction misconceptions and reducing high confidence errors in an online tutor," Journal of Computer Assisted Learning, vol. 40, no. 1, pp. 254-268, 2024, doi: 10.1111/jcal.12877.
[42] K. Durkin and B. Rittle-Johnson, "Diagnosing misconceptions: Revealing changing decimal fraction knowledge," Learning and Instruction, vol. 37, pp. 21-29, 2015, doi: 10.1016/j.learninstruc.2014.08.003.
[43] B. Butterfield and J. Metcalfe, "Errors committed with high confidence are hypercorrected," Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 27, no. 6, pp. 1491-1494, 2001, doi: 10.1037/0278-7393.27.6.1491.
[44] J. Metcalfe and B. Finn, "Hypercorrection of high confidence errors in children," Learning and Instruction, vol. 22, no. 4, pp. 253-261, 2012, doi: 10.1016/j.learninstruc.2011.10.004.
[45] T. J. VanderWeele and M. J. Knol, "A tutorial on interaction," Epidemiologic Methods, vol. 3, no. 1, pp. 33-72, 2014, doi: 10.1515/em-2013-0005.
[46] L. V. Hedges and E. C. Hedberg, "Intraclass correlation values for planning group-randomized trials in education," Educational Evaluation and Policy Analysis, vol. 29, no. 1, pp. 60-87, 2007, doi: 10.3102/0162373707299706.
[47] L. Kumle, M. L.-H. Võ, and D. Draschkow, "Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R," Behavior Research Methods, vol. 53, no. 6, pp. 2528-2543, 2021, doi: 10.3758/s13428-021-01546-0.
[48] D. Bates, M. Mächler, B. Bolker, and S. Walker, "Fitting linear mixed-effects models using lme4," Journal of Statistical Software, vol. 67, no. 1, 2015, doi: 10.18637/jss.v067.i01.
[49] G. W. Imbens and M. Kolesár, "Robust standard errors in small samples: Some practical advice," Review of Economics and Statistics, vol. 98, no. 4, pp. 701-712, 2016, doi: 10.1162/REST_a_00552.
[50] J. E. Pustejovsky and E. Tipton, "Small-sample methods for cluster-robust variance estimation and hypothesis testing in fixed effects models," Journal of Business & Economic Statistics, vol. 36, no. 4, pp. 672-683, 2018, doi: 10.1080/07350015.2016.1247004.
[51] D. J. Torgerson, "Contamination in trials: Is cluster randomisation the answer?," BMJ, vol. 322, no. 7282, pp. 355-357, 2001, doi: 10.1136/bmj.322.7282.355.
[52] H. Gehlbach and C. D. Robinson, "Mitigating illusory results through preregistration in education," Journal of Research on Educational Effectiveness, vol. 11, no. 2, pp. 296-315, 2018, doi: 10.1080/19345747.2017.1387950.
[53] E. B. Wilson, "Probable inference, the law of succession, and statistical inference," Journal of the American Statistical Association, vol. 22, no. 158, pp. 209-212, 1927, doi: 10.1080/01621459.1927.10502953.
[54] I. R. White, P. Royston, and A. M. Wood, "Multiple imputation using chained equations: Issues and guidance for practice," Statistics in Medicine, vol. 30, no. 4, pp. 377-399, 2011, doi: 10.1002/sim.4067.
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