1887

Tracking the Resolution of Student Misconceptions about the Central Dogma of Molecular Biology

    Authors: Amy G. Briggs1,*, Stephanie K. Morgan1, Seth K. Sanderson1, Molly C. Schulting1, Laramie J. Wieseman1
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    Affiliations: 1: Department of Biology, Beloit College, Beloit, WI 53511
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    Source: J. Microbiol. Biol. Educ. December 2016 vol. 17 no. 3 339-350. doi:10.1128/jmbe.v17i3.1165
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    Abstract:

    The goal of our study was to track changes in student understanding of the central dogma of molecular biology before and after taking a genetics course. Concept maps require the ability to synthesize new information into existing knowledge frameworks, and so the hypothesis guiding this study was that student performance on concept maps reveals specific central dogma misconceptions gained, lost, and retained by students. Students in a genetics course completed pre- and posttest concept mapping tasks using terms related to the central dogma. Student maps increased in complexity and validity, indicating learning gains in both content and complexity of understanding. Changes in each of the 351 possible connections in the mapping task were tracked for each student. Our students did not retain much about the central dogma from their introductory biology courses, but they did move to more advanced levels of understanding by the end of the genetics course. The information they retained from their introductory courses focused on structural components (e.g., protein is made of amino acids) and not on overall mechanistic components (e.g., DNA comes before RNA, the ribosome makes protein). Students made the greatest gains in connections related to transcription, and they resolved the most prior misconceptions about translation. These concept-mapping tasks revealed that students are able to correct prior misconceptions about the central dogma during an intermediate-level genetics course. From these results, educators can design new classroom interventions to target those aspects of this foundational principle with which students have the most trouble.

Key Concept Ranking

Gene Expression
0.8652685
Genetic Code
0.8051804
RNA Polymerase
0.6333333
DNA
0.59685415
0.8652685

References & Citations

1. Adamczyk P, Willson M 1996 Using concept maps with trainee physics teachers Phys Educ 31 374 10.1088/0031-9120/31/6/018 http://dx.doi.org/10.1088/0031-9120/31/6/018
2. American Association for the Advancement of Science 2011 Vision and change in undergraduate biology education: a call to action: a summary of recommendations made at a national conference organized by the American Association for the Advancement of Science July 15–17, 2009 Washington, DC
3. Ausubel DP, Novak JD, Hanesian H 1978 Educational psychology: a cognitive view Holt, Rinehart, and Winston New York, NY
4. Baxter GP, Glaser R 1998 Investigating the cognitive complexity of science assessments Educ Meas Issues Pract 17 37 45 10.1111/j.1745-3992.1998.tb00627.x http://dx.doi.org/10.1111/j.1745-3992.1998.tb00627.x
5. BouJaoude S, Attieh M 2008 The effect of using concept maps as study tools on achievement in chemistry Eurasia J Math Sci Technol Educ 4 233 246
6. Brewer CA 2004 Near real-time assessment of student learning and understanding in biology courses BioScience 54 1034 1039 10.1641/0006-3568(2004)054[1034:NRAOSL]2.0.CO;2 http://dx.doi.org/10.1641/0006-3568(2004)054[1034:NRAOSL]2.0.CO;2
7. Chi MT, Glaser R, Farr MJ 1988 The nature of expertise 448 451 Lawrence Erlbaum Associates Hillsdale, NJ
8. Clement J 1982 Algebra word problem solutions: thought processes underlying a common misconception J Res Math Educ 13 16 30 10.2307/748434 http://dx.doi.org/10.2307/748434
9. Cohen D 1987 The use of concept maps to represent unique thought processes: toward more meaningful learning J Curric Superv 2 285 289
10. Crick F, et al 1970 Central dogma of molecular biology Nature 227 561 563 10.1038/227561a0 4913914 http://dx.doi.org/10.1038/227561a0
11. Cross KP, Angelo TA 1988 Classroom assessment techniques A handbook for faculty National Center for Research to Improve Postsecondary Teaching and Learning Ann Arbor, MI
12. Crowe A, Dirks C, Wenderoth MP 2008 Biology in bloom: implementing Bloom’s taxonomy to enhance student learning in biology CBE Life Sci Educ 7 368 381 10.1187/cbe.08-05-0024 19047424 2592046 http://dx.doi.org/10.1187/cbe.08-05-0024
13. Ebert-May D, Hodder J, Williams K, Luckie D 2004 Pathways to scientific teaching Front Ecol Environ 2 323 323 10.1890/1540-9295(2004)002[0323:PTST]2.0.CO;2 http://dx.doi.org/10.1890/1540-9295(2004)002[0323:PTST]2.0.CO;2
14. Edwards J, Fraser K 1983 Concept maps as reflectors of conceptual understanding Res Sci Educ 13 19 26 10.1007/BF02356689 http://dx.doi.org/10.1007/BF02356689
15. Fisher M 1985 A misconception in biology: amino acids and translation J Res Sci Teach 22 53 62 10.1002/tea.3660220105 http://dx.doi.org/10.1002/tea.3660220105
16. González FM 1997 Diagnosis of Spanish primary school students’ common alternative science conceptions Sch Sci Math 97 68 74 10.1111/j.1949-8594.1997.tb17345.x http://dx.doi.org/10.1111/j.1949-8594.1997.tb17345.x
17. Hake RR 1998 Interactive engagement versus traditional methods in mechanics instruction Am J Phys 66 64 74 10.1119/1.18809 http://dx.doi.org/10.1119/1.18809
18. Hay D, Kinchin I, Lygo-Baker S 2008 Making learning visible: the role of concept mapping in higher education Stud High Educ 33 295 311 10.1080/03075070802049251 http://dx.doi.org/10.1080/03075070802049251
19. Hay DB, Tan PL, Whaites E 2010 Non-traditional learners in higher education: comparison of a traditional MCQ examination with concept mapping to assess learning in a dental radiological science course Assess Eval High Educ 35 577 595 10.1080/02602931003782525 http://dx.doi.org/10.1080/02602931003782525
20. Herl HE, O’Neil HJ Jr, Chung G, Schacter J 1999 Reliability and validity of a computer-based knowledge mapping system to measure content understanding Comput Hum Behav 15 315 333 10.1016/S0747-5632(99)00026-6 http://dx.doi.org/10.1016/S0747-5632(99)00026-6
21. Hoz R, Bowman D, Kozminsky E 2001 The differential effects of prior knowledge on learning: a study of two consecutive courses in earth sciences Instr Sci 29 187 211 10.1023/A:1017528513130 http://dx.doi.org/10.1023/A:1017528513130
22. Huang G, Taddese N, Walter E 2000 Entry and persistence of women and minorities in college science and engineering education Educ Stat Q 2 59 60
23. Kinchin IM, Hay DB, Adams A 2000 How a qualitative approach to concept map analysis can be used to aid learning by illustrating patterns of conceptual development Educ Res 42 43 57 10.1080/001318800363908 http://dx.doi.org/10.1080/001318800363908
24. Knight JK, Wood WB 2005 Teaching more by lecturing less Cell Biol Educ 4 298 310 10.1187/05-06-0082 16341257 1305892 http://dx.doi.org/10.1187/05-06-0082
25. Krathwohl DR 2002 A revision of Bloom’s taxonomy: an overview Theory Pract 41 212 218 10.1207/s15430421tip4104_2 http://dx.doi.org/10.1207/s15430421tip4104_2
26. Lewis J, Leach J, Wood-Robinson C 2000 What’s in a cell?—Young people’s understanding of the genetic relationship between cells, within an individual J Biol Educ 34 129 132 10.1080/00219266.2000.9655702 http://dx.doi.org/10.1080/00219266.2000.9655702
27. Marbach-Ad G, et al 2010 A model for using a concept inventory as a tool for students’ assessment and faculty professional development CBE Life Sci Educ 9 408 416 10.1187/cbe.10-05-0069 21123686 2995757 http://dx.doi.org/10.1187/cbe.10-05-0069
28. Marbach-Ad G 2001 Attempting to break the code in student comprehension of genetic concepts J Biol Educ 35 183 189 10.1080/00219266.2001.9655775 http://dx.doi.org/10.1080/00219266.2001.9655775
29. Markham KM, Mintzes JJ, Jones MG 1994 The concept map as a research and evaluation tool: further evidence of validity J Res Sci Teach 31 91 101 10.1002/tea.3660310109 http://dx.doi.org/10.1002/tea.3660310109
30. Martin BL, Mintzes JJ, Clavijo IE 2000 Restructuring knowledge in biology: cognitive processes and metacognitive reflections Int J Sci Educ 22 303 323 10.1080/095006900289895 http://dx.doi.org/10.1080/095006900289895
31. McClure JR, Sonak B, Suen HK 1999 Concept map assessment of classroom learning: reliability, validity, and logistical practicality J Res Sci Teach 36 475 492 10.1002/(SICI)1098-2736(199904)36:4<475::AID-TEA5>3.0.CO;2-O http://dx.doi.org/10.1002/(SICI)1098-2736(199904)36:4<475::AID-TEA5>3.0.CO;2-O
32. McDonald K, Gomes J 2013 Evaluating student preparedness and conceptual change in introductory biology students studying gene expression J Transform Leadersh Policy Stud 3 21
33. Mintzes JJ 2006 Concept mapping in college science 67 75 Mintzes JJ, Leonard WH Handbook of College Science Teaching NSTA press Arlington, VA
34. Momsen JL, Long TM, Wyse SA, Ebert-May D 2010 Just the facts? Introductory undergraduate biology courses focus on low-level cognitive skills CBE Life Sci Educ 9 435 440 10.1187/cbe.10-01-0001 21123690 2995761 http://dx.doi.org/10.1187/cbe.10-01-0001
35. Nesher P 1987 Towards an instructional theory: the role of student’s misconceptions Learn Math 33 40
36. Novak JD 1998 Learning, creating, and using knowledge: concept maps as facilitative tools in schools and corporations Earlbaum Associates Mahwah, NJ
37. Pashley M 1994 A chromosome model J Biol Educ 28 157 161 10.1080/00219266.1994.9655385 http://dx.doi.org/10.1080/00219266.1994.9655385
38. Pearsall NR, Skipper JEJ, Mintzes JJ 1997 Knowledge restructuring in the life sciences: a longitudinal study of conceptual change in biology Sci Educ 81 193 215 10.1002/(SICI)1098-237X(199704)81:2<193::AID-SCE5>3.0.CO;2-A http://dx.doi.org/10.1002/(SICI)1098-237X(199704)81:2<193::AID-SCE5>3.0.CO;2-A
39. Piaget J 1968 Le structuralisme Presses Universitaires de France Paris, France
40. Rebich S, Gautier C 2005 Concept mapping to reveal prior knowledge and conceptual change in a mock summit course on global climate change J Geosci Educ 53 355 10.5408/1089-9995-53.4.355 http://dx.doi.org/10.5408/1089-9995-53.4.355
41. Redfield RJ 2012 “Why do we have to learn this stuff?”—a new genetics for 21 st century students PLoS Biol 10 e10001356 10.1371/journal.pbio.1001356 http://dx.doi.org/10.1371/journal.pbio.1001356
42. Regis A, Albertazzi PG, Roletto E 1996 Concept maps in chemistry education J Chem Educ 73 1084 10.1021/ed073p1084 http://dx.doi.org/10.1021/ed073p1084
43. Ruiz-Primo MA, Schultz SE, Li M, Shavelson RJ 2001 Comparison of the reliability and validity of scores from two concept-mapping techniques J Res Sci Teach 38 260 278 10.1002/1098-2736(200102)38:2<260::AID-TEA1005>3.0.CO;2-F http://dx.doi.org/10.1002/1098-2736(200102)38:2<260::AID-TEA1005>3.0.CO;2-F
44. Salmon D, Kelly M 2008 Exploring what concept maps reveal about knowledge integration in teacher learning Int J Learn 15 13 22
45. Shaka FL, Bitner BL 1996 Construction and validation of a rubric for scoring concept maps Paper presented at the annual meeting of the Association for the Education of Teachers of Science Seattle, WA
46. Smith JP III, diSessa AA, Roschelle J 1993 Misconceptions reconceived: a constructivist analysis of knowledge in transition J Learn Sci 3 2 115 163 10.1207/s15327809jls0302_1 http://dx.doi.org/10.1207/s15327809jls0302_1
47. Southard K, Wince T, Meddleton S, Bolger MS 2016 Features of knowledge building in biology: understanding undergraduate students’ ideas about molecular mechanisms CBE Life Sci Educ 15 1016
48. Venville G, Gribble SJ, Donovan J 2005 An exploration of young children’s understandings of genetics concepts from ontological and epistemological perspectives Sci Educ 89 614 633 10.1002/sce.20061 http://dx.doi.org/10.1002/sce.20061
49. Walker JM, King PH 2003 Concept mapping as a form of student assessment and instruction in the domain of bioengineering J Eng Educ 92 167 178 10.1002/j.2168-9830.2003.tb00755.x http://dx.doi.org/10.1002/j.2168-9830.2003.tb00755.x
50. West DC, Park JK, Pomeroy JR, Sandoval J 2002 Concept mapping assessment in medical education: a comparison of two scoring systems Med Educ 36 820 826 10.1046/j.1365-2923.2002.01292.x 12354244 http://dx.doi.org/10.1046/j.1365-2923.2002.01292.x
51. West DC, Pomeroy JR, Park JK, Gerstenberger EA, Sandoval J 2000 Critical thinking in graduate medical education: a role for concept mapping assessment? JAMA 284 1105 1110 10.1001/jama.284.9.1105 10974689 http://dx.doi.org/10.1001/jama.284.9.1105
52. Williams M 2004 Concept mapping—a strategy for assessment Nurs Stand 19 33 38 10.7748/ns2004.11.19.9.33.c3754 15574052 http://dx.doi.org/10.7748/ns2004.11.19.9.33.c3754
53. Wright LK, Fisk JN, Newman DL 2014 DNA → RNA: what do students think the arrow means? CBE Life Sci Educ 13 338 348 4041510
54. Yin Y, Vanides J, Ruiz-Primo MA, Ayala CC, Shavelson RJ 2005 Comparison of two concept-mapping techniques: implications for scoring, interpretation, and use J Res Sci Teach 42 166 184 10.1002/tea.20049 http://dx.doi.org/10.1002/tea.20049

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2016-12-02
2019-06-25

Abstract:

The goal of our study was to track changes in student understanding of the central dogma of molecular biology before and after taking a genetics course. Concept maps require the ability to synthesize new information into existing knowledge frameworks, and so the hypothesis guiding this study was that student performance on concept maps reveals specific central dogma misconceptions gained, lost, and retained by students. Students in a genetics course completed pre- and posttest concept mapping tasks using terms related to the central dogma. Student maps increased in complexity and validity, indicating learning gains in both content and complexity of understanding. Changes in each of the 351 possible connections in the mapping task were tracked for each student. Our students did not retain much about the central dogma from their introductory biology courses, but they did move to more advanced levels of understanding by the end of the genetics course. The information they retained from their introductory courses focused on structural components (e.g., protein is made of amino acids) and not on overall mechanistic components (e.g., DNA comes before RNA, the ribosome makes protein). Students made the greatest gains in connections related to transcription, and they resolved the most prior misconceptions about translation. These concept-mapping tasks revealed that students are able to correct prior misconceptions about the central dogma during an intermediate-level genetics course. From these results, educators can design new classroom interventions to target those aspects of this foundational principle with which students have the most trouble.

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FIGURE 1

Concept map assessment tool and implementation. Concept maps were used to assess student understanding of the central dogma of molecular biology (A). Concept maps are made of interconnected propositions, in which each proposition consists of two terms connected by a linking verb (B). Students were provided with a list of 27 terms (C) and were instructed to use those terms to create a concept map explaining the central dogma. These assessments were given before the first day of molecular biology content and again on the last day of class (D). DNA = deoxyribonucleic acid; RNA = ribonucleic acid.

Source: J. Microbiol. Biol. Educ. December 2016 vol. 17 no. 3 339-350. doi:10.1128/jmbe.v17i3.1165
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FIGURE 2

Example student concept maps. Representative examples of high- (A) and low- (B) complexity student-generated concept maps created at the beginning of the course (upper panels) and at the end of the course (lower panels) from two students enrolled in Section 2 of BIOL289: Genetics. Validity (number of valid propositions/total number of propositions) and complexity (number of propositions) scores are provided for comparison.

Source: J. Microbiol. Biol. Educ. December 2016 vol. 17 no. 3 339-350. doi:10.1128/jmbe.v17i3.1165
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FIGURE 3

Matrix scoring method. To track changes in propositions from pre- to post- maps, each proposition on each individual student map was given a code (1 = absent, 3 = valid, 7 = invalid) and then the formula [(post + pre) × pre] (A) was used to generate nine unique values indicating the nine possible types of changes. Each student’s separate pre- and post- map (B) was then converted into a single 27 × 27 matrix containing these change codes (C). DNA = deoxyribonucleic acid; RNA = ribonucleic acid.

Source: J. Microbiol. Biol. Educ. December 2016 vol. 17 no. 3 339-350. doi:10.1128/jmbe.v17i3.1165
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FIGURE 4

Overall complexity and validity gains ( = 38). Concept maps were graded using two methods: (A) validity (percent valid propositions) and (B) complexity (total number of propositions made). * < 0.05 one-way ANOVA versus pretest.

Source: J. Microbiol. Biol. Educ. December 2016 vol. 17 no. 3 339-350. doi:10.1128/jmbe.v17i3.1165
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FIGURE 5

Tests of correlation between concept map validity and complexity. Multivariate analysis was used to determine correlations between concept map complexity (number of connections made) and validity on pretests (A; Spearman correlation coefficient R = 0.38, 0.0342) and posttests (B; R = 0.3234, 0.0751), and between complexity gains and normalized validity gains, (E; R = −0.210, 0.2567). Correlations were also determined for pretest and posttest scores for validity (C; R = 0.62, 0.0002) and complexity (D; R = 0.55, 0.0014).

Source: J. Microbiol. Biol. Educ. December 2016 vol. 17 no. 3 339-350. doi:10.1128/jmbe.v17i3.1165
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FIGURE 6

Analysis of most common propositional changes. The frequency with which each proposition changed between each student’s pre- and post- maps in each pre/post change category (see Table 1 ) was quantified (A, B; = 1,415 total proposition changes). The most common propositions within each change category are shown.

Source: J. Microbiol. Biol. Educ. December 2016 vol. 17 no. 3 339-350. doi:10.1128/jmbe.v17i3.1165
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