1887

Increasing Student Metacognition and Learning through Classroom-Based Learning Communities and Self-Assessment

    Author: Amy Siegesmund1
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    Affiliations: 1: Department of Biology, Pacific Lutheran University, Tacoma, WA 98447
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    • Published 04 May 2016
    • ©2016 Author(s). Published by the American Society for Microbiology.
    • [open-access] This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.

    • Supplemental materials available at http://asmscience.org/jmbe
    • Corresponding author. Mailing address: Department of Biology, Pacific Lutheran University, Tacoma, WA 98447. Phone: 253-535-8310. Fax: 253-536-5055. E-mail: [email protected].
    Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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    Abstract:

    Student overconfidence challenges success in introductory biology. This study examined the impact of classroom learning communities and self-assessment on student metacognition and subsequent impact on student epistemological beliefs, behaviors, and learning. Students wrote weekly self-assessments reflecting on the process of learning and received individual feedback. Students completed a learning strategies inventory focused on metacognition and study behaviors at the beginning and end of the semester and a Student Assessment of their Learning Gains (SALG) at the end of the semester. Results indicated significant changes in both metacognition and study behaviors over the course of the semester, with a positive impact on learning as determined by broad and singular measures. Self-assessments and SALG data demonstrated a change in student beliefs and behaviors. Taken together, these findings argue that classroom learning communities and self-assessment can increase student metacognition and change student epistemological beliefs and behaviors.

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References & Citations

1. Ambrose SA, Kirst MW, Lovett MC, Norman MK 2010 How learning works: seven research-based principles for smart teaching Jossey-Bass San Francisco, CA
2. Andrade MS 2007 Learning communities: examining positive outcomes J Coll Stud Retent 9 1 20 10.2190/E132-5X73-681Q-K188 http://dx.doi.org/10.2190/E132-5X73-681Q-K188
3. Angelo TA, Cross KP 1993 Diagnostic learning logs 311 315 Classroom assessment techniques: a handbook for college teachers Jossey-Bass San Francisco, CA
4. Arum R, Roksa J, Cho E 2011 Improving undergraduate learning: findings and policy recommendations from the SSRC-CLA longitudinal project Social Science Research Council New York, NY
5. Billett S 2009 Personal epistemologies, work and learning Educ Res Rev 4 210 219 10.1016/j.edurev.2009.06.001 http://dx.doi.org/10.1016/j.edurev.2009.06.001
6. Black P, William D 1998 Assessment and classroom learning Assess Educ 5 7 74 10.1080/0969595980050102 http://dx.doi.org/10.1080/0969595980050102
7. Black P, William D 2009 Developing the theory of formative assessment Educ Assess Eval Account 21 5 31 10.1007/s11092-008-9068-5 http://dx.doi.org/10.1007/s11092-008-9068-5
8. Blackwell LS, Tresniewski KH, Dweck CS 2007 Implicit theories of intelligence predict achievement across an adolescent transition: a longitudinal study and an intervention Child Dev 78 246 263 10.1111/j.1467-8624.2007.00995.x 17328703 http://dx.doi.org/10.1111/j.1467-8624.2007.00995.x
9. Bose J, Rengel Z 2009 A model formative assessment strategy to promote student-centered self-regulated learning in higher education US-China Educ Rev 6 29 35
10. Buch K, Spaulding S 2008 Using program assessment to “prove and improve” a discipline-based learning community J Learn Communit Res 3 35 46
11. Butler DL, Winne PH 1999 Feedback and self-regulated learning: a theoretical synthesis Rev Educ Res 65 245 281 10.3102/00346543065003245 http://dx.doi.org/10.3102/00346543065003245
12. Chew SL 2008 Study more! Study harder! Students’ and teachers’ faulty beliefs about how people learn 22 25 Meyers S, Stowell J Essays from E-Xcellence in teaching Society for the Teaching of Psychology [Online] http://teachpsych.org/ebooks/eit2008/index.php
13. Chew SL 2014 Helping students to get the most out of studying Applying science of learning in education: infusing psychological science into the curriculum, society for the teaching of psychology [Online.] http://teachpsych.org/ebooks/asle2014/index.php accessed 15 January 2015
14. Coutinho SA 2007 The relationship between goals, metacognition, and academic success Educate 7 39 47
15. 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
16. Dewey J 1933 How we think D. C. Heath & Co. Boston, MA
17. Dunning D, Heath C, Suls JM 2004 Flawed self-assessment Psychol Sci Public Interest 5 69 106 10.1111/j.1529-1006.2004.00018.x 26158995 http://dx.doi.org/10.1111/j.1529-1006.2004.00018.x
18. Dunning D, Johnson K, Ehrlinger J, Kruger J 2003 Why people fail to recognize their own incompetence Curr Dir Psychol Sci 12 83 87 10.1111/1467-8721.01235 http://dx.doi.org/10.1111/1467-8721.01235
19. Elwood J, Klenowski V 2002 Creating communities of shared practice: the challenges of assessment use in learning and teaching Assess Eval High Educ 27 243 256 10.1080/02602930220138606 http://dx.doi.org/10.1080/02602930220138606
20. Flavell JH 1979 Metacognition and cognitive monitoring: a new area of cognitive–developmental inquiry Am Psychol 34 906 10.1037/0003-066X.34.10.906 http://dx.doi.org/10.1037/0003-066X.34.10.906
21. Goodsell Love A 2012 The growth and current state of learning communities in higher education New Dir Teach Learn 2012 5 18 10.1002/tl.20032 http://dx.doi.org/10.1002/tl.20032
22. Hake RR 1998 Interactive-engagement versus traditional methods: a six-thousand-student survey of mechanics test data for introductory physics courses Am J Phys 66 64 74 10.1119/1.18809 http://dx.doi.org/10.1119/1.18809
23. Handelsman J, Miller S, Pfund C 2007 Scientific Teaching W. H. Freeman and Company New York, NY
24. Hill KM, Brözel VS, Heiberger GA 2014 Examining the delivery modes of metacognitive awareness and active reading lessons in a college nonmajors introductory biology course J Microbiol Biol Educ 15 5 12 10.1128/jmbe.v15i1.629 24839509 4004747 http://dx.doi.org/10.1128/jmbe.v15i1.629
25. Kienhues D, Bromme R, Stahl E 2008 Changing epistemological beliefs: the unexpected impact of a short-term intervention Br J Educ Psychol 78 545 565 10.1348/000709907X268589 18166142 http://dx.doi.org/10.1348/000709907X268589
26. Kruger J, 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 1121 1134 10.1037/0022-3514.77.6.1121 http://dx.doi.org/10.1037/0022-3514.77.6.1121
27. Lenning O, Ebbers L 1999 The powerful potential of learning communities: improving education for the future (No. 6), ASHE-ERIC Higher Education Report Graduate School of Education and Human Development, George Washington University Washington, DC
28. Mair C 2012 Using technology for enhancing reflective writing, metacognition and learning J Furth High Educ 36 147 167 10.1080/0309877X.2011.590583 http://dx.doi.org/10.1080/0309877X.2011.590583
29. Markulis PM, Murff E, Strang DR 2011 Should college instructors change their teaching styles to meet the millennial student? Dev Bus Simul Exp Learn 38 189 200
30. McMillan JH, Hearn J 2008 Student self-assessment: the key to stronger student motivation and higher achievement Educ Horiz 87 40 49
31. Mieklejohn A 1932 The experimental college Harper Collins New York, NY
32. Nicol DJ, Macfarlane-Dick D 2006 Formative assessment and self-regulated learning: a model and seven principles of good feedback practice Stud High Educ 31 199 218 10.1080/03075070600572090 http://dx.doi.org/10.1080/03075070600572090
33. Psycharis S 2013 Examining the effect of the computational models on learning performance, scientific reasoning, epistemic beliefs and argumentation: an implication for the STEM agenda Comput Educ 68 253 265 10.1016/j.compedu.2013.05.015 http://dx.doi.org/10.1016/j.compedu.2013.05.015
34. Ridley DS, Schutz PA, Glanz RS, Weinstein CE 1992 Self-regulated learning: the interactive influence of metacognitive awareness and goal-setting J Exp Educ 60 293 306 10.1080/00220973.1992.9943867 http://dx.doi.org/10.1080/00220973.1992.9943867
35. Rocconi LM 2011 The impact of learning communities on first year students’ growth and development in college Res High Educ 52 178 193 10.1007/s11162-010-9190-3 http://dx.doi.org/10.1007/s11162-010-9190-3
36. Sanbonmatsu DM, Strayer DL, Medeiros-Ward N, Watson JM 2013 Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking PLoS ONE 8 e54402 10.1371/journal.pone.0054402 23372720 3553130 http://dx.doi.org/10.1371/journal.pone.0054402
37. Saville BK, Lawrence NK, Jakobsen KV 2012 Creating learning communities in the classroom New Dir Teach Learn 2012 57 69 10.1002/tl.20036 http://dx.doi.org/10.1002/tl.20036
38. Schommer-Aikins M, Easter M 2006 Ways of knowing and epistemological beliefs: combined effect on academic performance Educ Psychol 26 411 423 10.1080/01443410500341304 http://dx.doi.org/10.1080/01443410500341304
39. Schraw G, Dennison RS 1994 Assessing metacognitive awareness Educ Psychol 19 460 475
40. Shepard LA 2000 The role of assessment in a learning culture Educ Res 29 4 14 10.3102/0013189X029007004 http://dx.doi.org/10.3102/0013189X029007004
41. Sperling R, Howard BC, Staley R, DuBois N 2004 Metacognition and self-regulated learning constructs Educ Res Eval 10 117 139 10.1076/edre.10.2.117.27905 http://dx.doi.org/10.1076/edre.10.2.117.27905
42. Sweeney R 2006 Millennial behaviors and demographics Newark NJ Institute of Technology [Online.] http://unbtls.ca/teachingtips/pdfs/sew/Millennial-Behaviors.pdf accessed on 24 November 2014
43. Tan C 1992 An evaluation of the use of continuous assessment in the teaching of physiology Higher Educ 23 255 272 10.1007/BF00145016 http://dx.doi.org/10.1007/BF00145016
44. Tanner KD 2012 Promoting student metacognition CBE Life Sci Educ 11 113 120 10.1187/cbe.12-03-0033 22665584 3366894 http://dx.doi.org/10.1187/cbe.12-03-0033
45. Taraban R, Rynearson K, Kerr MS 2000 Metacognition and freshman academic performance J Dev Educ 24 12 14 16 18 20
46. Taylor K, Moore WS, MacGregor J, Lindblad J 2003 Learning community research and assessment: what we know now National Learning Communities Project Monograph Series The Evergreen State College Olympia, WA
47. Tinto V 2003 Learning better together: the impact of learning communities on student success (No. 8) Higher Education Monograph Series Syracuse University, Higher Education Program, School of Education Syracuse, NY
48. Tomanek D, Montplaisir L 2004 Students’ studying and approaches to learning in introductory biology CBE Life Sci Educ 3 253 262 10.1187/cbe.04-06-0041 http://dx.doi.org/10.1187/cbe.04-06-0041
49. Young A, Fry J 2012 Metacognitive awareness and academic achievement in college students J Scholar Teach Learn 8 1 10
50. Zhao N, Wardeska JG, McGuire SY, Cook E 2014 Metacognition: an effective tool to promote success in college science learning J Coll Sci Teach 43 48 54 10.2505/4/jcst14_043_04_48 http://dx.doi.org/10.2505/4/jcst14_043_04_48

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2016-05-04
2019-07-18

Abstract:

Student overconfidence challenges success in introductory biology. This study examined the impact of classroom learning communities and self-assessment on student metacognition and subsequent impact on student epistemological beliefs, behaviors, and learning. Students wrote weekly self-assessments reflecting on the process of learning and received individual feedback. Students completed a learning strategies inventory focused on metacognition and study behaviors at the beginning and end of the semester and a Student Assessment of their Learning Gains (SALG) at the end of the semester. Results indicated significant changes in both metacognition and study behaviors over the course of the semester, with a positive impact on learning as determined by broad and singular measures. Self-assessments and SALG data demonstrated a change in student beliefs and behaviors. Taken together, these findings argue that classroom learning communities and self-assessment can increase student metacognition and change student epistemological beliefs and behaviors.

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Figures

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FIGURE 1

Student predictions versus actual grade distribution in BIOL 225. During the 1 week of the semester, students ( 145) anonymously predicted the grade they would receive in the course (dark bars). The actual end-of-the-semester grade distribution for BIOL 225-1 is shown for comparison (light bars); a similar distribution of final grades was observed in BIOL 225-2. A = 100–90%; B = 89–80%; C = 79–65%; D = 64–56%; E = 55–0%.

Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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FIGURE 2

Student-predicted exam scores vs. actual exam scores. Students were asked to predict their score on the 1 exam. This was the final question on the exam; students had therefore completed the exam before making prediction. Predicted data are plotted against the actual score for each student who made a prediction. Data shown are for a single exam in BIOL 225-1 ( = 0.6); the same trend was observed in BIOL 225-2. Boxes highlight two students who had large discrepancies in predicted and actual exam scores.

Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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Image of FIGURE 3

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FIGURE 3

Impact of classroom community on student learning. At the end of the semester, students completed the Student Assessment of Learning Gains (SALG) online survey. Data shown are selected responses reporting gains in understanding class content and skills and how the class activities impacted learning and attitudes. Questions were scored on a 5-point scale ranging from 1 (no gain) to 5 (great gain). Data shown are the combined percentages of BIOL 225-1 and BIOL 225-2 students ( 73).

Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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FIGURE 4

Metacognitive awareness (MA) scores pre- and post-ELP. Students completed electronic learning portfolio (ELP) entries from week 5 through the end of the semester. Metacognitive awareness was assessed as part of the learning strategies inventory (items 1–52) given at the beginning (pre-ELP) and end (post-ELP) of the semester. There were significant increases in MA scores in both BIOL 225-1 (dark bars, 0.01) and BIOL 225-2 (light bars, 0.02). Error bars depict the standard error of the mean.

Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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FIGURE 5

Study skills (SS) scores pre- and post-ELP. Students completed electronic learning portfolio (ELP) entries from week 5 through the end of the semester. Study skills were assessed as part of the learning strategies inventory (items 53–68) given at the beginning (pre-ELP) and end (post-ELP) of the semester. In both BIOL 225-1 (dark bars) and BIOL 225-2 (light bars), there was a significant ( 0.0001) decrease in SS scores. Error bars depict the standard error of the mean.

Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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FIGURE 6

Impact of ELP on student behaviors. At the end of the semester, students completed the Student Assessment of Learning Gains (SALG; www.salgsite.org) online survey. Data shown are selected responses related to changes in student behaviors targeted in the ELP including the class impact on attitudes; how class activities helped learning; and gains made in particular skills. Students were asked how much an activity helped their learning (e.g. writing in my ELP and reading the feedback) and what gains they made in particular skills (all other behaviors). Questions were scored on a five-point scale ranging from 1 (no gain) to 5 (great gain). Data shown are the combined percentages of BIOL 225-1 and BIOL 225-2 students ( 73).

Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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FIGURE 7

DFW and DE rates pre- and post-ELP. BIOL 225-1 and BIOL 225-2 students ( 92) completed electronic learning portfolio (ELP) entries from week 5 through the end of the semester. DFW rates represent students who dropped, failed, or withdrew from the course. DE rate represents students who received a failing grade (D or E) in the course. No ELP refers to a cohort of BIOL 225 taught by the same instructor in which the ELP was not employed.

Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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FIGURE 8

Metacognitive gains (MG) and exam scores in BIOL 225-1 and BIOL 225-2. Metacognitive gains (MG) were calculated as described in Methods. Exam 1 was taken pre-ELP and exams 2, 3, and 4 were taken post-ELP. A) Positive impact of MG on exam performance. Exam scores are shown for three representative students with increased MG and exam scores over the course of the semester. One student (MG = 0.35) had an exam score above 100% due to an extra credit question. B) Lower MG and relatively unchanged exam performance. Exam scores are shown for three representative students with little change in MG and exam scores over the course of the semester.

Source: J. Microbiol. Biol. Educ. May 2016 vol. 17 no. 2 204-214. doi:10.1128/jmbe.v17i2.954
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