BioSkills Guide: Development and National Validation of a Tool for Interpreting the Vision and Change Core Competencies
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Abstract
This article presents the BioSkills Guide, a resource enumerating program- and course-level learning outcomes aligned with the six core competencies of Vision and Change. The learning outcomes were developed and then nationally validated using input from more than 600 college biology educators from a range of biology fields and institution types.
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The BioSkills Guide was developed through an iterative process that included a diversity of instructors and other content experts, and includes both program learning outcomes and specific learning outcomes for undergraduate biology education, including multiple learning outcomes related to the reading of PSL.
“Developed through an iterative process that included a diversity of instructors and other content experts, the guide includes both program learning outcomes and specific learning outcomes for undergraduate biology education, including multiple learning outcomes related to the reading of PSL (Clemmons et al., 2020).”
From Reading Primary Scientific Literature: Approaches for Teaching Students in the Undergraduate STEM Classroom (2023) · cites this paper as Clemmons et al., 2020
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Experimental — accuracy not yet measuredThe BioSkills Guide is a resource for biology instructors that is aligned with the Vision and Change report.
“the BioSkills Guide (Clemmons et al., 2020) is a resource for biology instructors that is aligned with the Vision and Change report (AAAS, 2011).”
From Reading Primary Scientific Literature: Approaches for Teaching Students in the Undergraduate STEM Classroom (2023) · cites this paper as Clemmons et al., 2020
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References 94 · Cited by 122
Research works printed but not linked: 71
- Agarkar, S. Brock, R. (2017). Learning theories in science education. In Taber, K. Akpan, B. (Eds.), Science education (pp. 93– 103). Rotterdam, The Netherlands: Sense Publishers. 10.1007/978-94-6300-749-8_7 · DOI
- Angra, A. Gardner, S. (2016). Development of a framework for graph choice and construction. Advances in Physiology Education, 40(1), 123– 128. 10.1152/advan.00152.2015 26873901 · DOI
- Auchincloss, L. C. Laursen, S. L. Branchaw, J. L. Eagan, K. Graham, M. Hanauer, D. I.,... & Dolan, E. J. (2014). Assessment of course-based undergraduate research experiences: A meeting report. CBE—Life Sciences Education, 13(1), 29– 40. 10.1187/cbe.14-01-0004 24591501 PMC3940459 · DOI · Paper page
- Bayer Corporation. (2014). The Bayer facts of science education XVI: US STEM workforce shortage-myth or reality? Fortune 1000 talent recruiters on the debate. Journal of Science Education and Technology, 23(5), 617– 623. 10.1007/s10956-014-9501-0 · DOI
- Bergan-Roller, H. E. Galt, N. J. Chizinski, C. J. Helikar, T. Dauer, J. T. (2018). Simulated computational model lesson improves foundational systems thinking skills and conceptual knowledge in biology students. BioScience, 68(8), 612– 621. 10.1093/biosci/biy054 · DOI
- Brancaccio-Taras, L. Pape-Lindstrom, P. Peteroy-Kelly, M. Aguirre, K. Awong-Taylor, J. Balser, T.,... & Zhao, J. (2016). The PULSE Vision & Change rubrics, version 1.0: A valid and equitable tool to measure transformation of life sciences departments at all institution types. CBE—Life Sciences Education, 15(4), ar60. 10.1187/cbe.15-12-0260 27856548 PMC5132357 · DOI
- Branchaw, J. L. Pape-Lindstrom, P. A. Tanner, K. D. Bissonnette, S. A. Cary, T. L. Couch, B. A.,... & Brownell, S. E. (2020). Resources for teaching and assessing the VisionandChange biology core concepts. CBE—Life Sciences Education, 19(2), es1. 10.1187/cbe.19-11-0243 32357095 PMC8697664 · DOI
- Brown, D. E. Sadler, T. D. (2018). Conceptual framing and instructional enactment of the Next Generation Science Standards: A synthesis of the contributions to the special issue. Journal of Research in Science Teaching, 55(7), 1101– 1108. 10.1002/tea.21509 · DOI
- Brownell, S. E. Freeman, S. Wenderoth, M. P. Crowe, A. J. (2014a). BioCore Guide: A tool for interpreting the core concepts of VisionandChange for biology majors. CBE—Life Sciences Education, 13(2), 200– 211. 10.1187/cbe.13-12-0233 26086653 PMC4041499 · DOI
- Brownell, S. E. Kloser, M. (2015). Toward a conceptual framework for measuring the effectiveness of course-based undergraduate research experiences in undergraduate biology. Studies in Higher Education, 40(3), 525– 544. 10.1080/03075079.2015.1004234 · DOI
- Brownell, S. E. Price, J. V. Steinman, L. (2013). Science communication to the general public: Why we need to teach undergraduate and graduate students this skill as part of their formal scientific training. Journal of Undergraduate Neuroscience Education, 12(1), E6– E10. Retrieved September 14, 2017, from www.ncbi.nlm.nih.gov/pubmed/24319399 24319399 PMC3852879
- Brownell, S. E. Wenderoth, M. P. Theobald, R. Okoroafor, N. Koval, M. Freeman, S.,... & Crowe, A. J. (2014b). How students think about experimental design: Novel conceptions revealed by in-class activities. BioScience, 64(2), 125– 137. 10.1093/biosci/bit016 · DOI
- Camilli, G. Hira, R. (2019). Introduction to special issue—STEM workforce: STEM education and the post-scientific society. Journal of Science Education and Technology, 28(1), 1– 8. 10.1007/s10956-018-9759-8 · DOI
- Cappelli, P. H. (2015). Skill gaps, skill shortages, and skill mismatches. ILR Review, 68(2), 251– 290. 10.1177/0019793914564961 · DOI
- Cary, T. Branchaw, J. (2017). Conceptual elements: A detailed framework to support and assess student learning of biology core concepts. CBE—Life Sciences Education, 16(2), 1– 10. 10.1187/cbe.16-10-0300 PMC5459242 28450444 · DOI
- Cole, R. Lantz, J. M. Ruder, S. Reynders, G. J. Stanford, C. (2018, June 23). Board 25: Enhancing learning by assessing more than content knowledge. Paper presented at: 2018 ASEE Annual Conference & Exposition. Retrieved August 27, 2019, from https://peer.asee.org/29991
- College Board. (2015). AP Biology: Course and Exam Description (rev. ed., Fall 2015, pp. 145– 149).
- Cooper, K. M. Soneral, P. A. G. Brownell, S. E. (2017). Define your goals before you design a CURE: A call to use backward design in planning course-based undergraduate research experiences. Journal of Microbiology & Biology Education, 18(2). 10.1128/jmbe.v18i2.1287 PMC5440170 28656069 · DOI
- Corwin, L. A. Kiser, S. LoRe, S. M. Miller, J. M. Aikens, M. L. (2019). Community college instructors’ perceptions of constraints and affordances related to teaching quantitative biology skills and concepts. CBE—Life Sciences Education, 18(4), ar64. 10.1187/cbe.19-01-0003 31782692 PMC6889844 · DOI
- Dasgupta, A. P. Anderson, T. R. Pelaez, N. (2014). Development and validation of a rubric for diagnosing students’ experimental design knowledge and difficulties. CBE—Life Sciences Education, 13(2), 265– 284. 10.1187/cbe.13-09-0192 26086658 PMC4041504 · DOI
- Dauer, J. T. Bergan-Roller, H. E. King, G. P. Kjose, M. K. Galt, N. J. Helikar, T. (2019). Changes in students’ mental models from computational modeling of gene regulatory networks. International Journal of STEM Education, 6(1), 38. 10.1186/s40594-019-0193-0 · DOI
- Dauer, J. T. Momsen, J. L. Speth, E. B. Makohon-Moore, S. C. Long, T. M. (2013). Analyzing change in students’ gene-to-evolution models in college-level introductory biology. Journal of Research in Science Teaching, 50(6), 639– 659. 10.1002/tea.21094 · DOI
- Deane, T. Nomme, K. Jeffery, E. Pollock, C. Birol, G. (2016). Development of the Statistical Reasoning in Biology Concept Inventory (SRBCI). CBE—Life Sciences Education, 15(1), ar5. 10.1187/cbe.15-06-0131 26903497 PMC4803094 · DOI
- Diaz Eaton, C. Highlander, H. C. Dahlquist, K. D. Ledder, G. LaMar, M. D. Schugart, R. C. (2019). A “rule-of-five” framework for models and modeling to unify mathematicians and biologists and improve student learning. PRIMUS, 29(8), 799- 829. 10.1080/10511970.2018.1489318 · DOI
- Diaz-Martinez, L. A. Fisher, G. R. Esparza, D. Bhatt, J. M. D’Arcy, C. E. Apodaca, J.,... & Olimpo, J. T. (2019). Recommendations for effective integration of ethics and responsible conduct of research (E/RCR) education into course-based undergraduate research experiences: A meeting report. CBE—Life Sciences Education, 18(2), mr2. 10.1187/cbe.18-10-0203 31120396 PMC6755229 · DOI
- Dillman, D. A. Smyth, J. D. Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.). Hoboken NJ: Wiley.
- Dirks, C. Knight, J. K. (2016). Measuring college learning in biology. In Arum, R. Roksa, J. Cook, A. (Eds.), Improving quality in American higher education: Learning outcomes and assessments for the 21st century (pp. 225– 260). San Francisco, CA: Jossey-Bass. Retrieved January 14, 2018, from http://highered.ssrc.org/wp-content/uploads/MCL-in-Biology.pdf
- Dolan, E. L. (2017). Within and beyond biology education research: Steps toward cross-disciplinary collaboration. CBE—Life Sciences Education, 16(4), ed2. 10.1187/cbe.17-10-0224 29196424 PMC5749971 · DOI · Paper page
- Durán, P. A. Marshall, J. A. (2018). Mathematics for biological sciences undergraduates: A needs assessment. International Journal of Mathematical Education in Science and Technology, 50(6), 807– 824. 10.1080/0020739X.2018.1537451 · DOI
- Friedrichsen, P. J. Barnett, E. (2018). Negotiating the meaning of Next Generation Science Standards in a secondary biology teacher professional learning community. Journal of Research in Science Teaching, 55(7), 999– 1025. 10.1002/tea.21472 · DOI
- Gormally, C. Brickman, P. Lutz, M. (2012). Developing a Test of Scientific Literacy Skills (TOSLS): Measuring undergraduates’ evaluation of scientific information and arguments. CBE—Life Sciences Education, 11(4), 364– 377. 10.1187/cbe.12-03-0026 23222832 PMC3516792 · DOI · Paper page
- Gouvea, J. Passmore, C. (2017). “Models of” versus “Models for.” Science & Education, 26(1–2), 49– 63. 10.1007/s11191-017-9884-4 · DOI
- Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60(1), 549– 576. 10.1146/annurev.psych.58.110405.085530 18652544 · DOI · Paper page
- Grunspan, D. Z. Kline, M. A. Brownell, S. E. (2018). The lecture machine: A cultural evolutionary model of pedagogy in higher education. CBE—Life Sciences Education, 17(3), es6. 10.1187/cbe.17-12-0287 29953324 PMC6234828 · DOI
- Henderson, C., Finkelstein, N. Beach, A. (2010). Beyond dissemination in college science teaching: An introduction to four core change strategies. Journal of College Science Teaching, 39(5), 18– 25. Retrieved September 12, 2020, from http://www.jstor.org/stable/42993814
- Hester, S. D. Nadler, M. Katcher, J. Elfring, L. K. Dykstra, E. Rezende, L. F. Bolger, M. S. (2018). Authentic inquiry through modeling in biology (AIM-Bio): An introductory laboratory curriculum that increases undergraduates’ scientific agency and skills. CBE—Life Sciences Education, 17(4), ar63. 10.1187/cbe.18-06-0090 30496030 PMC6755888 · DOI
- Kahle, D. Wickham, H. (2013). ggmap: Spatial visualization with ggplot2. The R Journal, 5(1), 144– 161. Retrieved September 12, 2020, from http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf
- Kjelvik, M. K. Schultheis, E. H. (2019). Getting messy with authentic data: Exploring the potential of using data from scientific research to support student data literacy. CBE—Life Sciences Education, 18(2), es2. 10.1187/cbe.18-02-0023 31074698 PMC6755219 · DOI
- Kruchten, A. Baumgartner, E. Beadles-Bohling, A. Brown, J. Duncan, J. Kayes, L.,... & Tillberg, C. (2018). A network approach to vertical transfer and articulation for student success in biology: A fourth workshop hosted by the Northwest Biosciences Consortium RCN-UBE. FASEB Journal, 32(S1), 535.11– 535.11. Retrieved September 12, 2020, from www.fasebj.org/doi/abs/10.1096/fasebj.2018.32.1_supplement.535.11 · DOI
- Laverty, J. T. Underwood, S. M. Matz, R. L. Posey, L. A. Carmel, J. H. Caballero, M. D.,... & Cooper, M. M. (2016). Characterizing college science assessments: The three-dimensional learning assessment protocol. PLoS ONE, 11(9), e0162333. 10.1371/journal.pone.0162333 27606671 PMC5015839 · DOI · Paper page
- Long, J. S. Freese, J. (2014). Regression models for categorical dependent variables using Stata (3rd ed.). College Station, TX: Stata Press. Retrieved September 12, 2020, from www.stata.com/bookstore/regression-models -categorical-dependent-variables /
- Marbach-Ad, G. Hunt, C. Thompson, K. V. (2019). Exploring the values undergraduate students attribute to cross-disciplinary skills needed for the workplace: An analysis of five STEM disciplines. Journal of Science Education and Technology, 28(5), 452– 469. 10.1007/s10956-019-09778-8 · DOI
- McComas, W. F. (1998). The principal elements of the nature of science: Dispelling the myths. In McComas, W. F. (Ed.), The Nature of Science in Science Education. Science & Technology Education Library, vol 5. Dordrecht, The Netherlands: Springer (pp. 53– 70). Kluwer Academic. 10.1007/0-306-47215-5_3 · DOI
- Mercer-Mapstone, L. Kuchel, L. (2017). Core skills for effective science communication: A teaching resource for undergraduate science education. International Journal of Science Education, Part B, 7(2), 181– 201. 10.1080/21548455.2015.1113573 · DOI
- Mustillo, S. A. Lizardo, O. A. McVeigh, R. M. (2018). Editors’ comment: A few guidelines for quantitative submissions. American Sociological Review, 83(6), 1281– 1283. 10.1177/0003122418806282 · DOI
- National Academies of Sciences, Engineering, and Medicine (N ASEM). (2016). Developing a national STEM workforce strategy: A workshop summary. Washington, DC: National Academies Press. 10.17226/21900 · DOI
- NASEM. (2017). Undergraduate research experiences for STEM students: Successes, challenges, and opportunities. Washington, DC: National Academies Press. 10.17226/24622 · DOI
- National Academy of Engineering and National Research Council. (2014). STEM integration in K–12 education: Status, prospects, and an agenda for research. Washington, DC: National Academies Press. 10.17226/18612 · DOI
- Next Generation Science Standards Lead States. (2013). In Next Generation Science Standards: For States, By States. Washington, DC: National Academies Press. 10.17226/18290 · DOI
- National Research Council. (2003). BIO2010: Transforming undergraduate education for future research biologists. Washington, DC: National Academies Press. 10.17226/10497 20669482 · DOI
- NRC. (2012a). A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press. 10.17226/13165 · DOI
- Olson, K. Smyth, J. D. (2015). The effect of CATI questions, respondents, and interviewers on response time. Journal of Survey Statistics and Methodology, 3(3), 361– 396. 10.1093/jssam/smv021 · DOI
- Passmore, C. Stewart, J. Cartier, J. (2009). Model-based inquiry and school science: Creating connections. School Science and Mathematics, 109(7), 394– 402. 10.1111/j.1949-8594.2009.tb17870.x · DOI
- Project Kaleidoscope. (2011). What works in facilitating interdisciplinary learning in science and mathematics. Washington, DC: Association of American Colleges and Universities. 10.2307/3192150 · DOI
- Quillin, K. Thomas, S. (2015). Drawing-To-Learn: A framework for using drawings to promote model-based reasoning in biology. CBE—Life Sciences Education, 14(1), es2. 10.1187/cbe.14-08-0128 25713094 PMC4353088 · DOI
- Schwarz, C. V. Reiser, B. J. Davis, E. A. Kenyon, L. Achér, A. Fortus, D.,... & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632– 654. 10.1002/tea.20311 · DOI
- Scott, E. E. Wenderoth, M. P. Doherty, J. H. (2019). Learning progressions: An empirically grounded, learner-centered framework to guide biology instruction. CBE—Life Sciences Education, 18(4), es5. 10.1187/cbe.19-03-0059 31603729 PMC6812577 · DOI
- Shortlidge, E. E. Brownell, S. E. (2016). How to assess your CURE: A practical guide for instructors of course-based undergraduate research experiences. Journal of Microbiology & Biology Education, 17(3), 399– 408. 10.1128/jmbe.v17i3.1103 28101266 PMC5134943 · DOI
- Sirum, K. Humburg, J. (2011). The Experimental Design Ability Test (EDAT). Bioscene: Journal of College Biology Teaching, 8(371), 8– 16. Retrieved July 25, 2017, from http://files.eric.ed.gov/fulltext/EJ943887.pdf
- Smith, M. K. Brownell, S. E. Crowe, A. J. Holmes, N. G. Knight, J. K. Semsar, K.,... & Couch, B. A. (2019). Tools for change: Measuring student conceptual understanding across undergraduate biology programs using Bio-MAPS assessments. Journal of Microbiology & Biology Education, 20(2). 10.1128/jmbe.v20i2.1787 PMC6713482 31501687 · DOI
- Speth, E. B. Shaw, N. Momsen, J. Reinagel, A. Le, P. Taqieddin, R. Long, T. (2014). Introductory biology students’ conceptual models and explanations of the origin of variation. CBE—Life Sciences Education, 13(3), 529– 539. 10.1187/cbe.14-02-0020 25185235 PMC4152213 · DOI
- Stanhope, L. Ziegler, L. Haque, T. Le, L. Vinces, M. Davis, G. K.,... & Overvoorde, P. J. (2017). Development of a biological science quantitative reasoning exam (BioSQuaRE). CBE—Life Sciences Education, 16(4), ar66. 10.1187/cbe.16-10-0301 29196427 PMC5749968 · DOI
- Svoboda, J. Passmore, C. (2013). The strategies of modeling in biology education. Science & Education, 22(1), 119– 142. 10.1007/s11191-011-9425-5 · DOI
- Timmerman, B. E. C. Strickland, D. C. Johnson, R. L. Payne, J. R. (2011). Development of a “universal” rubric for assessing undergraduates’ scientific reasoning skills using scientific writing. Assessment & Evaluation in Higher Education, 36(5), 509– 547. 10.1080/02602930903540991 · DOI
- Tripp, B. Shortlidge, E. E. (2019). A framework to guide undergraduate education in interdisciplinary science. CBE—Life Sciences Education, 18(2), es3. 10.1187/cbe.18-11-0226 31120394 PMC6755209 · DOI
- Twenge, J. M. Donnelly, K. (2016). Generational differences in American students’ reasons for going to college, 1971–2014: The rise of extrinsic motives. Journal of Social Psychology, 156(6), 620– 629. 10.1080/00224545.2016.1152214 26886402 · DOI
- West, B. T. Welch, K. B. Galecki, A. T. (2014). Linear mixed models: A practical guide using statistical software. Boca Raton, FL: CRC Press.
- Wiggins, G. McTighe, J. (1998). What is backward design? In Understanding by Design (pp. 7– 19). Alexandria, VA: Association for Supervision and Curriculum Development. Retrieved September 12, 2020, from https://educationaltechnology.net/wp-content/uploads/2016/01/backward -design.pdf
- Wilson Sayres, M. A. Hauser, C. Sierk, M. Robic, S. Rosenwald, A. G. Smith, T. M.,... & Pauley, M. A. (2018). Bioinformatics core competencies for undergraduate life sciences education. PLoS ONE, 13(6), e0196878. 10.1371/journal.pone.0196878 29870542 PMC5988330 · DOI
- Yan, T. Tourangeau, R. (2008). Fast times and easy questions: The effects of age, experience and question complexity on Web survey response times. Applied Cognitive Psychology, 22(1), 51– 68. 10.1002/acp.1331 · DOI
- Zagallo, P. Meddleton, S. Bolger, M. S. (2016). Teaching Real Data Interpretation with Models (TRIM): Analysis of student dialogue in a large-enrollment cell and developmental biology course. CBE—Life Sciences Education, 15(2), ar17. 10.1187/cbe.15-11-0239 27193288 PMC4909339 · DOI
- Authors
- Clemmons, A. W., Timbrook, J., Herron, J. C., & Crowe, A. J.
- Affiliations
- University of Nebraska–Lincoln (US)
- University of Washington (US)
- Corresponding author
- Clemmons, Alexa W.
- Venue
- CBE—Life Sciences Education
- Published
- 2020-12
- Volume
- 19
- Issue
- 4
- Pages
- ar53
- ISSN
- 1931-7913 (ISSN-L)
- PMC
- PMC8693931
- OpenAlex
- W3089833548
- Cited by
- 122
- References
- 94
- Citations by year
- 2026: 16 · 2025: 26 · 2024: 25 · 2023: 13 · 2022: 19 · 2021: 21 · 2020: 2
- FWCI
- 20.87
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- CBE—Life Sciences Education (publishedVersion, cc-by-nc-sa)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- Lincoln (University of Nebraska) (submittedVersion)
- Funders
- National Science Foundation (1710772)
- University of Washington
- Topics
- Social Sciences › Education › Science Education and Pedagogy
- Social Sciences › Education › Educational Assessment and Pedagogy
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Keywords
- Vision and Change, core competencies, undergraduate biology education, competency framework, content validity, learning outcomes, backward design, competency assessment, curriculum mapping, community colleges, faculty survey
- MeSH terms
- Curriculum, Educational Measurement, Faculty, Humans, Learning, Students, Universities
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