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Analyzing interesting images motivates mathematics and statistics learning

Author(s): Jeremy M Wojdak

Radford University

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Summary:
Presentation by Jeremy Wojdak made as part of the "Bringing Research Data to the Ecology Classroom: Opportunities, Barriers, and Next Steps” Session at the Ecological Society of America annual meeting, August 8th, 2017, Portland Oregon

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 15 Aug 2017 doi:10.25334/Q4Z66C - cite this

Description

Background/Question/Methods

Data-intensive lessons can fall flat if students don't see the connections between their core passion (biology) and the quantitative analysis. Many of the best pedagogical reforms that do successfully bridge biology and math/stats focus on already quantitatively strong students (e.g., biocalculus or modeling courses), or require intense per-capita investment of resources (e.g., summer research experiences in biomathematics). Instructional approaches that can better put data and analyses in context, and be used across the spectrum of student aptitude/preparedness and at institutions with fewer resources, are desperately needed.

A promising, scalable, open-inquiry approach to infuse biology instruction with more data is to have students measure real biological phenomena via image analysis. Students are introduced to a current research frontier (i.e. via video interview with the researcher), and then a set of images or videos that the researcher used in their own work. The context might range from the bioenergetics of leaf-cutter ant foraging, to phenotypic plasticity of tadpoles in response to predators, or tree growth responses to climatic variation. Open-inquiry lab activities lead the students to develop testable hypotheses, define the variables they should measure, and collect data themselves, using image analysis software.

Results/Conclusions

Because the images and research contexts are interesting and the inquiry is open, students care about the data analysis as the means to answer their own questions. Moreover, students understand data they collect themselves in more meaningful ways than when using pre-collected data from textbooks or problem sets; during image analysis, students literally see the variation among individual measurements. Students have intuitive expectations for the magnitude of a sample mean and variance, treatment differences, or the results of hypothesis tests or regression analyses. Comparing their visual expectations (i.e. “this treatment seemed higher than the control”) with the results of the analyses provides a deeper understanding of the techniques and how the model/ empirical study/ data/ model cycle works. By adding contextual understanding and choosing fascinating topics, mundane data exercises (e.g., “conduct an ANOVA with the data at the end of the text chapter”) become answers to questions posed by the students themselves (e.g., “Do all predators induce similar morphological change in their tadpole prey?”). Open-inquiry, course-based activities that provide realistic research opportunities hold promise as a powerful tool to deliver our best pedagogy (e.g., undergraduate research) to all students. Image analysis provides a fast, free, scalable mechanism to have students generate their own data, and motivation, for subsequent analysis.

https://qubeshub.org/groups/aims

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