Biology Education Research

A central goal of my Biology Education Research is to advance conceptual understanding of biological reasoning about phenomena in order to design more effective educational environments. I work to better understand this complex system through altering it; I make evidence-based changes to learning environments and measure the impact. Although my work focuses on changing educational systems, it does so to illuminate the causal features central to its functioning. My research program is strongly interdisciplinary. Throughout my career I have sought out, learned from, and collaborated with experts in social psychology (Bonita London), psychometrics (Bill Boone), chemistry (Greg Rushton), science education (Ross Nehm), applied mathematics (Steven Finch), and institutional research (Nora Galambos). Theoretical frameworks from other disciplines is an important but often missing feature of BER work. These successful collaborations continue to enrich the theoretical foundations of my work and advance the methodological rigor of my studies of biology learning. I describe my three main research foci below.​

Cognitive Coherence

The cognitive models used to frame biology education research are built from conceptual understandings of biological reasoning, and are central to informing evidence-based teaching. Research shows that biological reasoning lacks cognitive coherence in novice learners. For example, evolution knowledge and acceptance have been found to be dependent upon the scale of evolutionary change (e.g., macroevolution vs. microevolution) and the lineages that are changing (e.g., plants vs. humans) (Sbeglia and Nehm, 2019). My work is exploring cognitive coherence as a construct, especially in reasoning related to evolution acceptance, genetic determinism, and matter and energy. This work investigates the links between the development of abstract reasoning in biology and cognitive coherence using advanced statistical and psychometric techniques (e.g., Item Response Theory, latent variable structural equation modeling). Specifically, my paper in Science Education utilizes Delta Dimensional Alignment of Rasch measures to study cognitive coherence in evolution acceptance across taxonomic and biological scales (Sbeglia and Nehm, 2019). My paper currently in review in Science & Education utilizes these methodologies to investigate cognitive coherence in genetic determinism across plants and animals (Tornabene et al., in revision). In some of my newer work, I investigate cognitive coherence in conceptions of matter and energy across biological scales (molecular vs. organismal).

Impact of Student Background, Demographic, and Psychosocial Variables on STEM Persistence

The classroom environment is a complex system. I have extensive experience designing, teaching, and studying this system, especially active learning classrooms. Although evidence supports the benefit of active learning approaches, particularly for underrepresented minority (URM) learners (Freeman et al., 2014), there is not enough precision in data collection to test existing models about why and under what conditions these benefits exist. In fact, Freeman et al. (2014) raise important questions about how the type, intensity, and delivery of active learning may interact with psychosocial elements. Feelings of isolation and lack of belonging are pervasive challenges to URMs in STEM (Cohen and Garcia, 2008), which can negatively impact self-efficacy and performance by rooting perceptions of one’s abilities in stereotypes. However, the classroom conditions in which feelings of belonging will increase and feelings of isolation will decrease are not well understood, but of central importance to inclusive and equitable teaching. 

Given this challenge, I use theory from psychology and education to design and implement curricular innovations that uncover how students’ mental models and psychosocial backgrounds interact with learning environments to generate cognitive, behavioral, and social change. My goal is to disentangle the relationships among race, gender, psychosocial variables (e.g., sense of belonging, STEM identity), and evolution acceptance and investigate how these variables impact biological learning and persistence in the biological discipline. This work is funded by the American Association of University Women (AAUW) and the Howard Hughes (HHMI) Inclusive Excellence Fund.

 

Low persistence in STEM disciplines such as biology is a chronic problem that the BER community is uniquely capable of addressing. At present, fewer than 40% of students who enter college intending to major in a STEM field complete a STEM degree (NSF, 2017). Evolution acceptance is an intuitive but uninvestigated variable that may contribute to race- and gender-based attrition in STEM and in the Evolutionary and Ecological Sciences (EES) in particular. Evolution is a central conceptual pillar in the biological sciences (Brewer & Smith, 2011) and an unavoidable dimension of EES. Surprisingly, only in the past few years have investigations begun to explore the roles that race, gender, and religiosity play in evolution acceptance and EES career interest (Mead et al., 2015, Sbeglia and Nehm 2018a, b). In my work, I have found that all three instruments designed to measure acceptance of evolution showed significantly lower measures for URMs and females than for Whites and males (Sbeglia & Nehm 2018a, b). The interactions among race, gender, religiosity, belonging, identity, evolution acceptance, and STEM persistence are complex and poorly studied despite their importance to diversifying the scientific workforce. There is currently no work within biology education research that has evaluated the causal relationships among these variables. I use latent variable structural equation modeling to achieve this goal. This work is situated in a large introductory biology course at Stony Brook University and I am currently building collaborations to expand this project to minority serving institutions nationwide.

Psychometrics

An overarching aspect of all of the work described above is the development and evaluation of measurement models and tools in order to advance theory and improve the quality of measurement. The rigorous measurement of variables of central importance to biology education is essential in the effort to understand learning. However, many challenges exist that limit our ability to gather and utilize high quality measures including the lack of: suitable instruments for measuring constructs, consensus regarding the meaning and quality of the measures derived from these instruments, and replication using large samples and longitudinal study designs. If any consensus exists, it is that robust measures of latent traits are crucial but remarkably challenging to develop and validate (cf. NRC, 2001). Measurement instruments must undergo psychometric validation analyses in order to establish robust values of the latent traits they propose to measure. However, at present, the field suffers from a lack of sophisticated psychometric foundations, which limits the quality of our measurement and the validity of our inferences. My work is at the for-front of bringing more rigorous psychometric methods to the measurement of learning and affect. For example, my work uses Rasch analysis to psychometrically evaluate the functioning of instruments that measure evolution acceptance (Sbeglia & Nehm 2018, 2019), and student understanding of randomness (Fiedler et al., 2018), genetic determinism (Tornabene et al. in revision), and transformations of matter and energy.

Literature Cited:

Carver, R. B., Castéra, J., Gericke, N., Evangelista, N. A. M., & El-Hani, C. N. (2017). Young adults’ belief in genetic determinism, and knowledge and attitudes towards modern genetics and genomics: the PUGGS questionnaire. PloS one, 12(1): e0169808.

Ecological Society of America (2017). Annual Reports to the ESA Council. The Bulletin of the Ecological Society of America 98 (S1).

Fiedler, D., Sbeglia, G. C., Nehm, R. H., Harms, U. (2019). How strongly does statistical reasoning influence knowledge and acceptance of evolution? Journal of Research in Science Teaching (JRST). DOI: 10.1002/tea.21547.

Freeman, S, Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences 111(23): 8410-8415.

Ha, M., Haury, D. L., Nehm, R. H. (2012). Feeling of certainty: uncovering a missing link between knowledge and acceptance of evolution. Journal of Research in Science Teaching 49(1): 95–121.

Mead L. S., Clarke J. B., Forcino F., & Graves J. (2015). Factors influencing minority student decisions to consider a career in evolutionary biology. Evolution: Education and Outreach 8(1): 1–11. https://doi.org/10.1186/s12052-015-0034-7

Nadelson, L. S., Southerland, S. (2012). A more fine-grained measure of student’s acceptance of evolution: Development of the Inventory of Student Evolution Acceptance – I-SEA. International Journal of Science Education 34(11): 1637–1666.

National Science Foundation (2017). Women, minorities, and persons with disabilities in science and engineering. Arlington, VA. Retrieved February 20, 2018, from https://www.nsf.gov/statistics/2017/nsf17310/static/ downloads/nsf17310-digest.pdf.

National Research Council (2001). Knowing What Students Know. Washington, D.C: National Academies Press.

National Research Council (2012). Thinking evolutionarily: Evolution education across the life sciences: Summary of a convocation. Washington, D.C: National Academies Press.

National Research Council (2014). Developing Assessments for the Next Generation Science Standards. Washington, D.C: National Academies Press.

President’s Council of Advisors on Science and Technology (2012). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering and mathematics. Retrieved February 20, 2018, from https://obamawhitehouse.archives. gov/sites/default/files/microsites/ostp/pcast-engage-to-excel-final_2-25-12.pdf.

Sbeglia, G. C., Nehm, R. H. (2018a). Measuring evolute using the GAENE: Influences of gender, race, degree-plan, and instruction. Evolution: Education and Outreach 12:1.

Sbeglia, G., Nehm, R.H. (2018b). Disparities in URM evolution acceptance: implications for diversifying the biological sciences. Paper presented at SABER (Society for the Advancement of Biology Education Research) National Meeting. Minneapolis, MN. 

Sbeglia, G. C., Nehm, R. H. (2019). Do you see what I-SEA? A Rasch analysis of the psychometric properties of the Inventory of Student Evolution Acceptance. Science Education. 103(2): 287-316.

 Sbeglia, G. C., Tchesnokova, V., Wright, P. C., Dykhuizen, D. E. (August 2016). “One-locus method for the differentiation of socially transmitted E. coli strains in ringtailed lemurs in south western Madagascar (Lemur catta).” Poster presented at the Joint Meeting of the International Primatological Society (IPS) and the American Primatological Society (APS), Chicago, IL.

Smith, M. K., Jones, F. H. M., Gilbert, S. L., Wieman, C. E. (2013). The classroom observation protocol for undergraduate STEM (COPUS): a new instrument to characterize university STEM classroom practices. CBE—Life Sciences Education 12(4), 618-627.

Smith, M. U., Snyder, S. W., Devereaux, R. (2016). The GAENE—Generalized acceptance of evolution evaluation: Development of a new measure of evolution acceptance. Journal of Research in Science Teaching, 53(9), 1289–1315.99.

Stains, M. et al  (2018). Anatomy of STEM Teaching in American Universities: A Snapshot from a Large Scale Observation Study. Science, 359(6383): 1468-1470.

Tornabene, R. E., Sbeglia, G. C., Nehm, R. H. (in revision) Measuring belief in genetic determinism: A Rasch analysis of the PUGGS instrument. 

© 2019 by Gena Sbeglia

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