Measuring meta-ignorance through the lens of confidence: Examining students' redox misconceptions about oxidation numbers, charge, and electron transfer

Chemistry Education Research and Practice

Volumn 15, Issue 4, 2014, Pages 729-746

  Brandriet, Alexandra R ^a   Bretz, Stacey Lowery ^a  

^a MIAMI UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84908666231     PISSN: None     EISSN: 11094028     Source Type: Journal    
DOI: 10.1039/c4rp00129j     Document Type: Article

References (85)

1. Adams W. K. and Wieman C. E., (2011), Development and validation of instruments to measure learning of expert-like thinking, Int. J. Sci. Educ., 33 (9), 1289-1312
2. Allsop R. T. and George N. H., (1982), Redox in Nuffield advanced chemistry, Educ. Chem., 19, 57-59
3. American Chemical Society, (2008), ACS Guidelines & Supplements, from http://www.acs.org/content/acs/en/about/governance/committees/training/acs-guidelines-supplements.html
4. Bell P. and Volckmann D., (2011), Knowledge surveys in general chemistry: confidence, overconfidence, and performance, J. Chem. Educ., 88 (11), 1469-1476
5. Bodner G. M., (1986), Constructivism: a theory of knowledge, J. Chem. Educ., 63 (10), 873-878
6. Brandriet A. R. and Bretz S. L., (2014), The development of the Redox Concept Inventory as a measure of students' symbolic and particulate redox understanding and confidence, J. Chem. Educ., 91 (8), 1132-1144
7. Bretz S. L. and Linenberger K. J., (2012), Development of the enzyme-substrate interactions concept inventory, Biochem. Mol. Biol. Educ., 40 (4), 229-233
8. Burton R. F., (2001), Quantifying the effects of chance in multiple choice and true/false tests: question selection and guessing of answers, Asess. Eval. High. Educ., 26 (1), 41-50
9. Butts B. and Smith R., (1987), What do students perceive as difficult in H.S.C. chemistry? Aust. Sci. Teach. J., 32 (4), 45-51
10. Caleon I. and Subramaniam R., (2010a), Development and application of a three-tier diagnostic test to assess secondary students' understanding of waves, Int. J. Sci. Educ., 32 (7), 939-961
11. Caleon I. and Subramaniam R., (2010b), 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., 40 (3), 313-337
12. Carter T. J. and Dunning D., (2008), Faulty self-assessment: why evaluating one's own competence is an intrinsically difficult task, Soc. Personal. Psychol. Compass, 2 (1), 346-360
13. Chandrasegaran A. L., Treagust D. F. and Mocerino M., (2007), 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., 8 (3), 293-307
14. Chi M. T. H. and Roscoe R. D., (2002), The processes and challenges of conceptual change, in Limón M. and Mason L. (ed.), Reconsidering conceptual change: issues in theory and practice, New York, NY: Kluwer Academic Publishers, pp. 3-27
15. Chi M. T. H., Slotta J. D. and De Leeuw N., (1994), From things to processes: a theory of conceptual change for learning science concepts, Learn. Instr., 4 (1), 27-43
16. Chiu T., Fang D., Chen J., Wang Y. and Jeris C., (2001), A robust and scalable clustering algorithm for mixed type attributes in large database environment, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA
17. Clement J., Brown D. E. and Zietsman A., (1989), Not all preconceptions are misconceptions: finding 'anchoring conceptions' for grounding instruction on students' intuitions, Int. J. Sci. Educ., 11 (5), 554-565
18. Cohen J., (1988), Statistical power analysis for the behavioral sciences, 2nd edn, Hillsdale, NJ: Lawrence Erlbaum Associates, Inc
19. Cronbach L. J., (1951), Coefficient alpha and the internal structure of tests, Psychometrika, 16 (3), 297-334
20. Cruz-Ramirez de Arellano D. and Towns M., (2014), Students' understanding of alkyl halide reactions in undergraduate organic chemistry, Chem. Educ. Res. Pract., DOI: 10.1039/C3RP00089C
21. Davidowitz B., Chittleborough G. and Murray E., (2010), Student-generated submicro diagrams: a useful tool for teaching and learning chemical equations and stoichiometry, Chem. Educ. Res. Pract., 11 (3), 154-164
22. De Jong O., Acampo J. and Verdonk A., (1995), Problems in teaching the topic of redox reactions: actions and conceptions of chemistry teachers, J. Res. Sci. Teach., 32 (10), 1097-1110
23. Ding L. and Beichner R., (2009), Approaches to data analysis of multiple-choice questions, Phys. Rev. ST Phys. Educ. Res., 5, 1-17
24. diSessa A. A., (1993), Toward an epistemology of physics, Cognition Instruct., 10 (2-3), 105-225
25. Dunning D., (2011), The Dunning-Kruger effect: on being ignorant of one's own ignorance, in Olson J. M. and Zanna M. P. (ed.), Advances in Experimental Social Psychology, San Diego, CA: Elsevier Academic Press, 44, pp. 247-296
26. Everitt B. S., Landau S., Leese M. and Stahl D., (2011), Cluster analysis, 5th edn, Chichester, UK: John Wiley & Sons, Ltd
27. Ferguson G. A., (1949), On the theory of test discrimination, Psychometrika, 14 (1), 61-68
28. Ferguson C. J., (2009), An effect size primer: a guide for clinicians and researchers, Prof. Psychol. Res. Pract., 40 (5), 532-538
29. Garnett P. J. and Treagust D. F., (1992), Conceptual difficulties experienced by senior high school students of electrochemistry: electric circuits and oxidation-reduction equations, J. Res. Sci. Teach., 29 (2), 121-142
30. Hasan S., Bagayoko D. and Kelley E. L., (1999), Misconceptions and the certainty of response index (CRI), Phys. Educ., 34 (5), 294-299
31. IBM SPSS Statistics, (2011), TwoStep Cluster Analysis, retrieved from Statistics Base Options website: http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Fidh-twostep-main.htm
32. IBM SPSS Statistics, (2012a), IBM SPSS Statistics 21 Algorithms, retrieved from IBM SPSS Statistics 21 Documentation website: ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/21.0/en/client/Manuals/IBM-SPSS-Statistics-Algorithms.pdf
33. IBM SPSS Statistics, (2012b), IBM SPSS Statistics Base 21, retrieved from IBM SPSS Statistics 21 Documentation website: ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/21.0/en/client/Manuals/IBM-SPSS-Statistics-Base.pdf
34. Jaber L. Z. and BouJaoude S., (2012), A macro-micro-symbolic teaching to promote relational understanding of chemical reactions, Int. J. Sci. Educ., 34 (7), 973-998
35. Johnstone A. H., (1982), Macro-and micro-chemistry, Sch. Sci. Rev., 64 (227), 377-379
36. Johnstone A. H., (1991), Why is science difficult to learn? Things are seldom what they seem, J. Comput. Assist. Learn., 7 (2), 75-83
37. Johnstone A. H., (1993), The development of chemistry teaching: a changing response to changing demand, J. Chem. Educ., 70 (9), 701-705
38. Johnstone A. H., (2006), Chemical education research in Glasgow in perspective, Chem. Educ. Res. Pract., 7 (2), 49-63
39. Johnstone A. H., (2010), You can't get there from here, J. Chem. Educ., 87 (1), 22-29
40. Johnstone A. H., Morrison T. I. and Sharp D. W. A., (1971), Topic difficulties in chemistry, Educ. Chem., 8, 212, 213, 218
41. Karatjas A. G., (2013), Comparing college students' self-assessment of knowledge in organic chemistry to their actual performance, J. Chem. Educ., 90 (8), 1096-1099
42. Kaufman L. and Rousseeuw P. J., (1990), Finding groups in data: an introduction to cluster analysis, New York, NY: Wiley
43. Kelly R. M., Barrera J. H. and Mohamed S. C., (2010), An analysis of undergraduate general chemistry students' misconceptions of the submicroscopic level of precipitation reactions, J. Chem. Educ., 87 (1), 113-118
44. Kern A. L., Wood N. B., Roehrig G. H. and Nyachwaya J., (2010), A qualitative report of the ways high school chemistry students attempt to represent a chemical reaction at the atomic/molecular level, Chem. Educ. Res. Pract., 11 (3), 165-172
45. Kline P., (1986), A handbook of test construction: introduction to psychometric design, New York, NY: Methuen
46. Knapp T. R., (1990), Treating ordinal scales as interval scales: an attempt to resolve the controversy, Nurs. Res., 39 (2), 121-123
47. Kruger J. and Dunning D., (1999), Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments, J. Pers. Soc. Psychol., 77 (6), 1121-1134
48. Linenberger K. J. and Bretz S. L., (2012), Generating cognitive dissonance in student interviews through multiple representations, Chem. Educ. Res. Pract., 13 (3), 172-178
49. Linenberger K. J. and Bretz S. L., (2014), Biochemistry students' ideas about shape and charge in enzyme-substrate interactions, Biochem. Mol. Biol. Educ., 42 (3), 203-212
50. Luxford C. J. and Bretz S. L., (2013), Moving beyond definitions: what student-generated models reveal about their understanding of covalent bonding and ionic bonding, Chem. Educ. Res. Pract., 14 (2), 214-222
51. Luxford C. J. and Bretz S. L., (2014), Development of the Bonding Representations Inventory to identify student misconceptions about covalent and ionic bonding representations, J. Chem. Educ., 91 (3), 312-320
52. McClary L. M. and Bretz S. L., (2012), Development and assessment of a diagnostic tool to identify organic chemistry students' alternative conceptions related to acid strength, Int. J. Sci. Educ., 34 (15), 2317-2341
53. Mirkin B. G., (2005), Clustering for data mining: a data recovery approach, Boca Raton, FL: Taylor and Francis Group
54. Mooi E. and Sarstedt M., (2011), Cluster Analysis, in A concise guide to market research: the process, data, and methods using IBM SPSS statistics, New York, NY: Springer, pp. 237-284
55. Mulford D. R. and Robinson W. R., (2002), An inventory for alternate conceptions among first-semester general chemistry students, J. Chem. Educ., 79 (6), 739-744
56. Naah B. M. and Sanger M. J., (2012), Student misconceptions in writing balanced equations for dissolving ionic compounds in water, Chem. Educ. Res. Pract., 13 (3), 186-194
57. Nakhleh M. B., (1992), Why some students don't learn chemistry: chemical misconceptions, J. Chem. Educ., 69 (3), 191-196
58. National Research Council, (2012), Discipline-based education research: understanding and improving learning in undergraduate science and engineering, in Singer S. R., Nielsen N. R. and Schweingruber H. A. (ed.), Committee on the Status, Contributions, and Future Directions of Discipline-Based Education Research. Board on Science Education, Division of Behavioral and Social Sciences and Education, Washington, DC: The National Academies Press
59. NoruŠis M. J., (2012), Cluster Analysis, in IBM SPSS Statistics 19 statistical procedures companion, Upper Saddle River, NJ: Prentice Hall, pp. 375-404
60. Novak J. D., (2010), Learning, creating, and using knowledge: concept maps as facilitative tools in schools and corporations, New York, NY: Routledge Taylor & Francis Group
61. Nyachwaya J. M., Warfa A. M., Roehrig G. H. and Schneider J. L., (2014), College chemistry students' use of memorized algorithms in chemical reactions, Chem. Educ. Res. Pract., 15 (1), 81-93
62. Österlund L. and Ekborg M., (2009), Students' understanding of redox reactions in three situations, Nordic Stud. Sci. Educ., 5 (2), 115-127
63. Österlund L., Berg A. and Ekborg M., (2010), Redox models in chemistry textbooks for the upper secondary school: friend or foe? Chem. Educ. Res. Pract., 11 (3), 182-192
64. Øyehaug A. B. and Holt A., (2013), Students' understanding of the nature of matter and chemical reactions-a longitudinal study of conceptual restructuring, Chem. Educ. Res. Pract., 14 (4), 450-467
65. Pazicni S. and Bauer C. F., (2014), Characterizing illusions of competence in introductory chemistry students, Chem. Educ. Res. Pract., 15 (1), 24-34
66. Peterson R. F. and Treagust D. F., (1989), Grade-12 students' misconceptions of covalent bonding and structure, J. Chem. Educ., 66 (6), 459-460
67. Roediger H. L. and Marsh E. J., (2005), The positive and negative consequences of multiple-choice testing, J. Exp. Psychol. Learn., 31 (5), 1155-1159
68. Rosenthal D. P. and Sanger M. J., (2012), Student misinterpretations and misconceptions based on their explanations of two computer animations of varying complexity depicting the same oxidation-reduction reaction, Chem. Educ. Res. Pract., 13, 471-483
69. Rust J. and Golombok S., (2009), Modern psychometrics: the science of psychological assessment, Hove, East Sussex: Routledge
70. Sanger M. J. and Greenbowe T. J., (2000), Addressing student misconceptions concerning electron flow in aqueous solutions with instruction including computer animations and conceptual change strategies, Int. J. Sci. Educ., 22 (5), 521-537
71. Schmidt H. J. and Volke D., (2003), Shift of meaning and students' alternative concepts, Int. J. Sci. Educ., 25 (11), 1409-1424
72. Schwarz G., (1978), Estimating the dimension of a model, Ann. Stat., 6 (2), 461-464
73. Smith K. C. and Nakhleh M. B., (2011), University students' conceptions of bonding in melting and dissolving phenomena, Chem. Educ. Res. Pract., 12 (4), 398-408
74. SPSS Inc., (2001), The SPSS TwoStep cluster component: A scalable component to segment your customers more effectively, White Paper-Technical Report, Chicago, IL, pp. 1-9
75. Sreenivasulu B. and Subramaniam R., (2014), Exploring undergraduates' understanding of transition metals chemistry with the use of cognitive and confidence measures, Res. Sci. Educ., DOI: 10.1007/s11165-014-9400-7
76. Stains M. and Talanquer V., (2008), Classification of chemical reactions: stages of expertise, J. Res. Sci. Teach., 45 (7), 771-793
77. Stevens S. S., (1946), On the theory of scales of measurement, Science, 103, 677-680
78. Strike K. A. and Posner G. J., (1992), A revisionist theory of conceptual change, in Duschl R. A. and Hamilton R. J., (ed.), Philosophy of Science Cognitive Psychology, and Educational Theory and Practice, Albany, NY: State University of New York Press, pp. 147-176
79. Taber K. S., (2002), Conceptualizing quanta: illuminating the ground state of student understanding of atomic orbitals, Chem. Educ. Res. Pract., 3 (2), 145-158
80. Treagust D. F., (1988), Development and use of diagnostic tests to evaluate students' misconceptions in science, Int. J. Sci. Educ., 10 (2), 159-169
81. Villafañe S. M., Bailey C. P., Loertscher J., Minderhout V. and Lewis J. E., (2011), Development and analysis of an instrument to assess student understanding of foundational concepts before biochemistry coursework, Biochem. Mol. Biol. Educ., 39 (2), 102-109
82. von Glasersfeld E., (1984), An introduction to radical constructivism, in Watzlawick P. (ed.) The Invented Reality: How Do We Know What We Believe We Know? New York: Norton & Company, Inc
83. von Glasersfeld E., (1995), A constructivist approach to teaching, in Steffe L. P. and Gale J. E. (ed.), Constructivism in Education, Hillsdale, NJ: Lawrence Erlbaum Associates
84. Voska K. W. and Heikkinen H. W., (2000), Identification and analysis of student conceptions used to solve chemical equilibrium problems, J. Res. Sci. Teach., 37 (2), 160-176
85. Wren D. and Barbera J., (2014), Psychometric analysis of the Thermochemistry Concept Inventory, Chem. Educ. Res. Pract., 15, 380-390