Definitions, Underpinnings, Benefits
Underpinnings – The Cognitive Basis of Graphing:
Friel, S. N., Curcio, F. R., & Bright, G. W. (2001). Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in Mathematics Education, 124-158.
Freedman E.G., Shah P. (2002) Toward a Model of Knowledge-Based Graph Comprehension. In: Hegarty M., Meyer B., Narayanan N.H. (eds) Diagrammatic Representation and Inference. Diagrams 2002. Lecture Notes in Computer Science, vol 2317. Springer, Berlin, Heidelberg.
Hegarty, M. (2011). The Cognitive Science of Visual-Spatial Displays: Implications for Design Topics in Cognitive Science 3 (2011) 446–474 DOI: 10.1111/j.1756-8765.2011.01150.x.
Lehrer, R., & Schauble, L. (2000). Developing model-based reasoning in mathematics and science. Journal of Applied Developmental Psychology, 21(1), 39-48.
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.
Padilla, L. M., Creem-Regehr, S. H., Hegarty, M., & Stefanucci, J. K. (2018). Decision making with visualizations: a cognitive framework across disciplines. Cognitive research: principles and implications, 3(1), 1-25
Shah, P., & Freedman, E.G. (2011). Bar and line graph comprehension: An interaction of top-down and bottom-up processes. Topics in Cognitive Science, 3(3), 560–578.
Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Cheshire, CT: Graphics press.
Wang, Z. H., Wei, S., Ding, W., Chen, X., Wang, X., & Hu, K. (2012). Students’ cognitive reasoning of graphs: Characteristics and progression. International Journal of Science Education, 34(13), 2015-2041.
Wilson, K. J., Long, T. M., Momsen, J. L., & Bray Speth, E. (2020). Modeling in the classroom: making relationships and systems visible. CBE—Life Sciences Education, 19(1), fe1.
Additional papers not cited but influential:
Bryant, P. E., & Somerville, S. C. (1986). The spatial demands of graphs. British Journal of Psychology, 77(2), 187-197
diSessa, A. A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991). Inventing graphing: Meta-representational expertise in children. Journal of Mathematical Behavior, 10, 117-160.
Mathewson, J. H. 1999. Visual-spatial thinking: an aspect of science overlooked by educators. Science Education 83:33–54.
Factors Affecting the Development of Graph Competence
Common Challenges Students Encounter in Graphing:
Angra, A., & Gardner, S. M. (2017). Reflecting on graphs: Attributes of graph choice and construction practices in biology. CBE—Life Sciences Education, 16(3), ar53.
Glazer, N. (2011). Challenges with graph interpretation: A review of the literature. Studies in science education, 47(2), 183-210.
Harsh, J. A., Campillo, M., Murray, C., Myers, C., Nguyen, J., & Maltese, A. V. (2019). “Seeing” data like an expert: An eye-tracking study using graphical data representations. CBE—Life Sciences Education, 18(3), ar32.
Maltese, A. V., Harsh, J. A., & Svetina, D. (2015). Data visualization literacy: Investigating data interpretation along the novice—expert continuum. Journal of College Science Teaching, 45(1), 84-90.
Barriers in Graph Teaching and Learning:
Bowen, G. M., & Roth, W. M. (2005). Data and graph interpretation practices among preservice science teachers. Journal of Research in Science Teaching, 42(10), 1063-1088.
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.
Gardner SM, Suazo-Flores E, Maruca S, Abraham JK, Karippadath A, and Meir E (2021) Biology Undergraduate Students’ Graphing Practice in Digital versus Pen and Paper Graphing Environments. Journal of Science Education and Technology
Roth, W. M., Bowen, G. M., & McGinn, M. K. (1999). Differences in graph‐related practices between high school biology textbooks and scientific ecology journals. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 36(9), 977-1019.
Rybarczyk, B. (2011). Visual literacy in biology: A comparison of visual representations in textbooks and journal articles. Journal of College Science Teaching, 41(1), 106.
Weissgerber, T. L., Winham, S. J., Heinzen, E. P., Milin-Lazovic, J. S., Garcia-Valencia, O., Bukumiric, Z., … & Milic, N. M. (2019). Reveal, don’t conceal: transforming data visualization to improve transparency. Circulation, 140(18), 1506-1518.
Additional papers not cited but influential:
Jescovitch, L. N., Scott, E. E., Cerchiara, J. A., Merrill, J., Urban-Lurain, M., Doherty, J. H., & Haudek, K. C. (2021). Comparison of machine learning performance using analytic and holistic coding approaches across constructed response assessments aligned to a science learning progression. Journal of Science Education and Technology, 30(2), 150-167.
Kastellec, J. P., & Leoni, E. L. (2007). Using graphs instead of tables in political science. Perspectives on politics, 5(4), 755-771
Shulman, L. (1987). Knowledge and teaching: Foundations of the new reform. Harvard educational review, 57(1), 1-23.
Inclusive Teaching Practices:
Braille Authority of North America (2010). Guidelines and Standards for Tactile Graphics.
CAST (2022). Universal Design for Learning Guidelines version 2.2. Retrieved from http://udlguidelines.cast.org
Dewsbury B and Brame, CJ (2019) Inclusive Teaching. CBE-Life Sciences Education 18(2)
Jones, J. L., Jones, K. A., & Vermette, P. J. (2011). Planning Learning Experiences in the Inclusive Classroom: Implementing the Three Core UDL Principles to Motivate, Challenge and Engage All Learners. Electronic Journal for Inclusive Education, 2(7), 6.
Levine, A. (2019) True Colors: Optimizing Charts for Readers with Color Vision Deficiencies
Stone, B. W., Kay, D., & Reynolds, A. (2019). Teaching Visually Impaired College Students in Introductory Statistics. Journal of Statistics Education, 27(3), 225-237.
Tanner K. (2013) Structure Matters: Twenty-One Teaching Strategies to Promote Student Engagement and Cultivate Classroom Equity. CBE-Life Sciences Education 12(3).
Additional papers not cited but influential:
Meeks, L. M., Jain, N. R., & Herzer, K. R. (2016). Universal Design: Supporting Students with Color Vision Deficiency (CVD) in Medical Education. Journal of Postsecondary Education and Disability, 29(3), 303-309.
Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. Association for Supervision and Curriculum Development. Alexandria, VA
Measuring Graph Competence
Learning Outcomes for Graph Instruction:
Aikens, M. L., & Dolan, E. L. (2014). Teaching quantitative biology: Goals, assessments, and resources. Molecular Biology of the Cell, 25(22), 3478-3481.
Clemmons, A. W., Timbrook, J., Herron, J. C., & Crowe, A. J. (2020). BioSkills guide: Development and national validation of a tool for interpreting the Vision and Change core competencies. CBE—Life Sciences Education, 19(4), ar53.
College Board (2020). AP Biology Course and Exam Description. Retrieved from https://apcentral.collegeboard.org/pdf/ap-biology-course-and-exam-description-0.pdf
Pelaez N, Gardner SM, & Anderson T (2022). The problem with teaching experimentation: Development and use of a framework to define fundamental competencies for biological experimentation. Trends in Teaching Experimentation in the Life Sciences, Editors, Nancy Pelaez, Trevor Anderson, and Stephanie M. Gardner, Springer publishing.
Additional papers not cited but influential:
American Association for the Advancement of Science. (2011). Vision and change in undergraduate biology education: A call to action. Washington, DC.
AAMC-HHMI Committee. (2009). Scientific foundations for future physicians. Washington, DC: Association of American Medical Colleges, 26-29.
George, M., S. Bragg, A. G. de los Santos Jr, D. D. Denton, P. Gerber, M. M. Lindquist, J. M. Rosser, D. A. Sanchez, and C. Meyer (1996). “Shaping the Future: New Expectations for Undergraduate Education in Science.” Mathematics, Engineering and Technology: Arlington, VA, National Science Foundation
Pelaez, N. J., Gardner, S. M., & Anderson, T. R. (2022). Trends in Teaching Experimentation in the Life Sciences. Putting Research into Practice to Drive Institutional Change. Springer Nature Switzerland.
Identifying Students’ Strengths and Areas of Improvement:
Angra, A., & Gardner, S. M. (2018). The graph rubric: development of a teaching, learning, and research tool. CBE—Life Sciences Education, 17(4), ar65.
Berg, C., & Boote, S. (2017). Format effects of empirically derived multiple-choice versus free-response instruments when assessing graphing abilities. International Journal of Science and Mathematics Education, 15(1), 19-38.
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.
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.
McKenzie, D. L., & Padilla, M. J. (1986). The construction and validation of the test of graphing in science (TOGS). Journal of research in science teaching, 23(7), 571-579.
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.
Wiggins, G. (1990) “The Case for Authentic Assessment,” Practical Assessment, Research, and Evaluation: Vol. 2 , Article 2.
Additional papers not cited but influential:
Allen, D., & Tanner, K. (2007). Putting the horse back in front of the cart: using visions and decisions about high-quality learning experiences to drive course design. CBE—Life Sciences Education, 6(2), 85-89.
Martone, A., & Sireci, S. G. (2009). Evaluating alignment between curriculum, assessment, and instruction. Review of educational research, 79(4), 1332-1361.
National Research Council. (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academy Press.
Design Principles for Graph Teaching
Teaching in the Discipline:
Åberg‐Bengtsson, L., & Ottosson, T. (2006). What lies behind graphicacy? Relating students’ results on a test of graphically represented quantitative information to formal academic achievement. Journal of Research in Science Teaching, 43(1), 43-62.
Bowen, G. M., Roth, W. M., & McGinn, M. K. (1999). Interpretations of graphs by university biology students and practicing scientists: Toward a social practice view of scientific representation practices. Journal of Research in Science Teaching, 36(9), 1020-1043.
Konold C, Higgins T, Russell SJ, Khalil K (2014) Data seen through different lenses. Educational Studies in Mathematics 88 (3), 305-325
Xiong, C., Van Weelden, L., & Franconeri, S. (2019). The curse of knowledge in visual data communication. IEEE transactions on Visualization and Computer Graphics, 26(10), 3051-3062.
Additional papers not cited but influential:
Curcio (1987) Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education. 18, 382-393.
Gardner, S. M., Angra, A., & Harsh, J. A. (2022). A Framework for Teaching and Learning Graphing in Undergraduate Biology. Trends in Teaching Experimentation in the Life Sciences, 143-170.
Roth, W. M. (2012). Undoing decontextualization or how scientists come to understand their own data/graphs. Science Education, 97(1), 80-112.
Explicit Instruction:
Dennen, V. P. (2004). Cognitive apprenticeship in educational practice: Research on scaffolding, modeling, mentoring, and coaching as instructional strategies. Handbook of research on educational communications and technology, 2(2004), 813-828.
Patterson, T. F., & Leonard, J. G. (2005). Turning spreadsheets into graphs: An information technology lesson in whole brain thinking. Journal of Computing in Higher Education, 17(1), 95-115
Schultheis, E. H., & Kjelvik, M. K. (2015). Data nuggets: bringing real data into the classroom to unearth students’ quantitative & inquiry skills. The American Biology Teacher, 77(1), 19-29.related information processing, and practice. Theory Into Practice, 1-12
Shah, P., & Hoeffner, J. (2002). Review of graph comprehension research: Implications for instruction. Educational psychology review, 14(1), 47-69.of-Class Graphing Activities Increases Student Engagement and Learning Outcomes. Journal of microbiology & biology education, 18(3)
Additional papers not cited but influential:
Barsoum, M. J., Sellers, P. J., Campbell, A. M., Heyer, L. J., & Paradise, C. J. (2013). Implementing recommendations for introductory biology by writing a new textbook. CBE—Life Sciences Education, 12(1), 106-116.
Wiggins, G., & McTighe, J. (1998). Understanding by design. Association for Supervision and Curriculum Development (ACSD): Alexandria, Virginia.
Use of Meaningful Data:
DeBoy, C. A. (2017). Student Use of Self-Data for Out-of-Class Graphing Activities Increases Student Engagement and Learning Outcomes. Journal of Microbiology & Biology Education, 18(3).
Hug, B., & McNeill, K. L. (2008). Use of First‐hand and Second‐hand Data in Science: Does data type influence classroom conversations?. International Journal of Science Education, 30(13), 1725-1751.
Renninger, K. A., & Hidi, S. E. (2021). Interest development, self-related information processing, and practice. Theory Into Practice, 1-12.
Additional papers not cited but influential:
Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational researcher, 18(1), 32-42.
Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (pp. 416–436). Sage Publications Ltd.
Use Real-World Messy Data:
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), es2.77(1), 19-29.related information processing, and practice. Theory Into Practice, 1-12
Kastens, K., Krumhansl, R., & Baker, I. (2015). THINKING BIG: Transitioning your students from working with small, student-collected data sets toward “big data”. The Science Teacher, 82(5), 25-31.
Rosenberg, J., Edwards, A., & Chen, B. (2020). Getting Messy with Data. The Science Teacher, 87(5), 30-34.
Schultheis, E. H., & Kjelvik, M. K. (2020). Using Messy, Authentic Data to Promote Data Literacy & Reveal the Nature of Science. The American Biology Teacher, 82(7), 439-446
Science Education Resource Center (SERC) at Carleton College (2021). Higher Education Resources. Retrieved from https://serc.carleton.edu/highered/index.html#teaching
Utilize Collaborative Work:
Roth, W. M., & McGinn, M. K. (1997). Graphing: Cognitive ability or practice?. Science Education, 81(1), 91-106.
Shofner, M. A., & Marbach-Ad, G. (2017). Group Activity to Enhance Student Collaboration, Graph Interpretation, and Peer Evaluation of Ecological Concepts in a Large-Enrollment Class. Journal of Microbiology & Biology Education, 18(3), 18-3.
Tanner, K., Chatman, L. S., & Allen, D. (2003). Approaches to cell biology teaching: cooperative learning in the science classroom—beyond students working in groups. CBE-Life Sciences Education, 2(1), 1-5.
Wilson, K.J., Brickman, P., & Brame, C.J. (2018). Group Work. CBE-Life Science Education, 17 (1)
Incorporate Intentional Reflection:
Angra, A. & Gardner S.M. (2016).Development of a Framework for Graph Choice and Construction. Advances in Physiology Education 40: 123–128.
diSessa AA (2004) Metarepresentation: native competence and targets for instruction. Cognition and Instruction 22: 293-331.
Matuk, C., Zhang, J., Uk, I., & Linn, M. C. (2019). Qualitative graphing in an authentic inquiry context: How construction and critique help middle school students to reason about cancer. Journal of Research in Science Teaching, 56(7), 905-936.
McFarland, J. (2010). Teaching and assessing graphing using active learning. MathAMATYC Educator, 1(2), 32-39.
Stanton, J. D., Sebesta, A. J., & Dunlosky, J. (2021). Fostering Metacognition to Support Student Learning and Performance. CBE—Life Sciences Education, 20(2), fe3.
Tanner, K. D. (2012). Promoting student metacognition. CBE—Life Sciences Education, 11(2), 113-120.
Additional papers not cited but influential:
Hogan, K., & Maglienti, M. (2001). Comparing the epistemological underpinnings of students’ and scientists’ reasoning about conclusions. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 38(6), 663-687.
Designs in Action
Lecture Space:
Crowther, G. J. (2017). Which way do the ions go? A graph-drawing exercise for understanding electrochemical gradients. Advances in Physiology Education, 41: 556–559.
Bray Speth E., Momsen, J.L., Moyerbrailean, G.A., Ebert-May, D., Long, T.M., Wyse, S.A. & Linton, D. (2010). 1, 2, 3, 4: Infusing quantitative literacy into introductory biology. CBE-Life Sciences Education 9: 323-332.
Picone, C., Rhode, J., Hyatt, L., & Parshall, T. (2007). Assessing gains in undergraduate students’ abilities to analyze graphical data. Teaching Issues and Experiments in Ecology, 5(July), 1-54
Taylor, M. F. (2010). Making Biology Teaching More ““Graphic””. The American Biology Teacher, 72(9), 568-571.
Weigel, E., & Angra, A. (accepted) Teaching in Tandem: Using graphs in an active-learning classroom to shape students’ understanding of biology concepts. Journal of College Science Teaching.
Additional papers not cited but influential:
Feser, J., Vasaly, H., & Herrera, J. (2013). On the edge of mathematics and biology integration: improving quantitative skills in undergraduate biology education. CBE—Life Sciences Education, 12(2), 124-128.
Laboratory:
Angra, A., Dalgleish, H. J., Chambers, S. M., Pita, D., & Emery, N. C. (2020). Data, distributions, and hypotheses: Exploring diversity and disturbance in the tallgrass prairie. CourseSource.
Gray, C. E., & Contreras-Shannon, V. E. (2017). Using Models From the Literature and Iterative Feedback to Teach Students to Construct Effective Data Figures for Poster Presentations. Journal of College Science Teaching, 46(3), 74.
Hammett, A., & Dorsey, C. (2020). Messy Data, Real Science. The Science Teacher, 87(8).
Harsh J.A. and Schmitt-Harsh M.L. (2016) Instructional Strategies to Develop Graphing Skills in the College Science Classroom. The American Biology Teacher 78(1): 49-56.
Kirby, C. K., Fleming-Davies, A., & White, P. J. (2019). The Figure of the Day: A Classroom Activity to Improve Students’ Figure Creation Skills in Biology. The American Biology Teacher, 81(5), 317-325.
Violin, C. R., & Forster, B. M. (2019). An Introductory Module and Experiments To Improve the Graphing Skills of Non-Science Majors. Journal of Microbiology & Biology Education, 20(3).
