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Metacognition and Peer Learning Strategies as Predictors in Problem-Solving Performance in Microbiology

    Author: Josephine Itota Ebomoyi1
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    Affiliations: 1: Medical Laboratory Science Program, School of Health Studies, College of Health and Human Sciences, Northern Illinois University, Dekalb, IL 60115
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    • Received 07 December 2018 Accepted 20 November 2019 Published 28 February 2020
    • ©2020 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/ and 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: Medical Laboratory Science Program, School of Health Studies, College of Health and Human Sciences, Northern Illinois University, 1425 W Lincoln Highway, Dusable Hall 157, DeKalb, IL 60115-2828. Phone: 815-753-6325. Fax: 815-753-1653. E-mail: [email protected].
    Source: J. Microbiol. Biol. Educ. February 2020 vol. 21 no. 1 doi:10.1128/jmbe.v21i1.1715
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    Abstract:

    The objective of this study was to determine the relationship between learning strategies (LS) and problem solving (PS) in microbiology. Microbiology problems utilized for the study were from educational software known as “Interactive Multimedia Exercises” (IMMEX). Problem-solving performances measured included: the ability to solve, scores obtained and elapsed time. It was hypothesized that there would be a good correlation between students’ LS and PS. Since many factors besides learning strategies predict performance, alpha was set at 0.10. Participants ( = 65) solved two sets of microbiology problems “Microquest” (Mq), which focuses on microbial cellular processes and mode of action of antibiotics, and “Creeping crud” (Cc), which focuses on the cause, origin, and transmission of diseases. Participants also responded to the adapted Motivated Strategy Learning Questionnaire (MSLQ) using a five-point Likert scale. Scores for LS were determined by averaging the item responses of participants. Regression analysis was used to determine significance, with Grade Point Average (GPA) as a control. Of the 65 participants 48 (73.8%) successfully solved Mq while 52 (80%) solved Cc. Metacognitive self-regulated strategy was significantly ( < 0.10) related to ability to solve Cc. Peer learning strategy showed a significant ( < 0.10) relationship with Cc scores. Time spent solving Cc was significantly more than time spent on Mq ( < 0.001). These findings emphasize the fact that metacognition and peer learning are positive predictors for problem solving and could potentially improve learning outcomes in microbiology. The implications for curriculum development are discussed.

References & Citations

1. Ebomoyi JI 2009 Chapter 1 8 50 Problem solving skills and learning strategies of students: theories, research, technological applications and attributes for success as a learner Lambert Academic Publishing
2. Ebomoyi JI, Jurin R 2012 Understanding students’ decision-making strategies in problem-solving in microbiology using IMMEX educational software Electr J Sci Educ 16 1 1
3. Someran MW, Barnard YF, Sandberg JAC 1994 The think aloud method: a practical guide to modeling cognitive processes Academic Press
4. Pintrich PR, Smith DAF, Garcia T, MacKeachie WJ 1993 Reliability and predictive validity of the motivated strategy for learning questionnaire (MSLQ) Educ Psychol Measure 53 801 813 10.1177/0013164493053003024 http://dx.doi.org/10.1177/0013164493053003024
5. Martinez EM 1998 What is problem solving? Phi Kappan 79 8 605 610
6. Zhao N 2009 Metacognitive strategy training and vocabulary learning of Chinese college students Eng Lang Teach 2 4 123 129
7. Yu KC, Fan SC, Lin KY 2014 Enhancing students’ problem-solving skills through context-based learning Int J Sci Math Educ 13 6 1377 1401 10.1007/s10763-014-9567-4 http://dx.doi.org/10.1007/s10763-014-9567-4
8. Brown AL, Brarisford JD, Campione JC, Ferrera RA 1983 Learning, remembering and understanding 77 166 Flavell J, Markman E Handbook of child psychology 3, Cognitive Development Wiley New York
9. Bandura A 1986 Social foundations of thought and action Prentice Hall Englewood Cliffs, NJ
10. Zimmerman B, Pons M 1986 Development of structured interview for assessing student use of self-regulated learning strategies Am Educ Res J 23 4 614 628 10.3102/00028312023004614 http://dx.doi.org/10.3102/00028312023004614
11. Hunt EB 1999 Intelligence and human resources: past, present and future 3 30 Ackerman PL, Kyllonen PC, Roberts RD Learning and individual differences: process, trait, and content determinants American Psychology Association Washington DC
12. Barkley EF 2010 From theory to practice: teachers talk about student engagement 45 81 Student engagement techniques: a handbook for college faculty Jossy Bass San Francisco, CA
13. Quelmarz JW, Pelegrino JW 2009 Perspectives and integration of technology and assessment JRTE 43 2 119 134
14. Stevens RH, McCoy JM, Kwark AR 1991 Solving the problem of how medical students solve problems MD Comput 8 1 13 20 2011052
15. Pintrich PR, Smith DAF, Garcia T, MacKeachie WJ 1991 Chapter 3 37 43 Motivated strategies for learning questionnaire manual National Center for Research to Improve Postsecondary Teaching and Learning
16. Ebomoyi JI 2004 PhD thesis Problem solving performance and learning strategy of undergraduate students who solved microbiology problems using IMMEX educational software University of Northern Colorado Greeley CO
17. Pedhazur EJ 1999 Multiple regression in behavioral research: explanation and prediction 3rd ed. Harcourt Brace College Publishers
18. Grimm LG, Yamold PR 1994 Reading and understanding multivariate statistics American Psychological Association UK and Europe
19. Ryan AM, Pintrich PR 1997 Should I ask for help? The role of motivation and attitudes in adolescents’ help-seeking in math class J Educ Psychol 89 329 341 10.1037/0022-0663.89.2.329 http://dx.doi.org/10.1037/0022-0663.89.2.329
20. Karabenick SA, Knapp JR 1991 Relationship of academic help seeking to the use of learning strategies and other instrumental achievement behaviors in college students J Educ Psychol 83 221 230 10.1037/0022-0663.83.2.221 http://dx.doi.org/10.1037/0022-0663.83.2.221
21. Hoffman EA 2001 Successful application of active learning techniques in introductory microbiology Microbiol Educ 2 5 11 10.1128/154288101X14285805983179 23653538 3633112 http://dx.doi.org/10.1128/154288101X14285805983179
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2020-02-28
2020-04-10

Abstract:

The objective of this study was to determine the relationship between learning strategies (LS) and problem solving (PS) in microbiology. Microbiology problems utilized for the study were from educational software known as “Interactive Multimedia Exercises” (IMMEX). Problem-solving performances measured included: the ability to solve, scores obtained and elapsed time. It was hypothesized that there would be a good correlation between students’ LS and PS. Since many factors besides learning strategies predict performance, alpha was set at 0.10. Participants ( = 65) solved two sets of microbiology problems “Microquest” (Mq), which focuses on microbial cellular processes and mode of action of antibiotics, and “Creeping crud” (Cc), which focuses on the cause, origin, and transmission of diseases. Participants also responded to the adapted Motivated Strategy Learning Questionnaire (MSLQ) using a five-point Likert scale. Scores for LS were determined by averaging the item responses of participants. Regression analysis was used to determine significance, with Grade Point Average (GPA) as a control. Of the 65 participants 48 (73.8%) successfully solved Mq while 52 (80%) solved Cc. Metacognitive self-regulated strategy was significantly ( < 0.10) related to ability to solve Cc. Peer learning strategy showed a significant ( < 0.10) relationship with Cc scores. Time spent solving Cc was significantly more than time spent on Mq ( < 0.001). These findings emphasize the fact that metacognition and peer learning are positive predictors for problem solving and could potentially improve learning outcomes in microbiology. The implications for curriculum development are discussed.

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FIGURE 2

Example of the experience of a Creeping crud problem solver. Available optional resources include Library, Experts, and Maps.

Source: J. Microbiol. Biol. Educ. February 2020 vol. 21 no. 1 doi:10.1128/jmbe.v21i1.1715
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Image of FIGURE 1

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FIGURE 1

Schematic representation of study design.

Source: J. Microbiol. Biol. Educ. February 2020 vol. 21 no. 1 doi:10.1128/jmbe.v21i1.1715
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