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Thinking About Evolu0onary Change: Concepts, Contexts, and Cogni0ve Coherence Ross Nehm Associate Professor, Dept. of Ecology & Evolution Associate Director, Ph.D. Program in Science Education Thinking about biology Foundations

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Thinking About Evolu0onary Change: Concepts, Contexts, and Cogni0ve Coherence

Ross Nehm Associate Professor, Dept. of Ecology & Evolution Associate Director, Ph.D. Program in Science Education

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Thinking about biology

Foundations Scale/units Disciplines Diversity

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Thinking about biology

Foundations Heterogeneity of units Probabilis2c vs. determinis2c

  • utcomes

Many causes, weak effects (vs. few causes with strong effects) Historical con2ngency

  • f systems
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Thinking about biology

Epistemology Disciplines Botany Zoology Evolu2on Ecology Gene2cs Causal principles

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Thinking about biology

Epistemology Scale/units Disciplines Ecosystems Clades Genes Receptors

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Thinking about biology

Epistemology Scale/units Disciplines Diversity Organisms, features

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Unity & diversity of life

  • Unity—what is the same

and why?

  • Diversity—what is

different and why?

Source: Wikipedia Dobzhansky

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Unity & diversity of life

  • Unity—what is the same and why?
  • Diversity—what is different and

why?

  • How do humans at different

educational levels think about biological similarities and differences across scales and contexts?

  • What types of causal processes are

invoked to explain similarities and differences across biological scales and contexts?

Source: Wikipedia Dobzhansky

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Diversity & unity across scales

Between individuals in a popula2on Between popula2ons of the same taxon Differences between popula2ons of different taxa Same cell type different taxa Different cell types same taxa

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Unity Diversity

Thinking about biology

Spectacular phenotypic differences among lineages—and special similari2es. The phenomena experienced most directly during human ontogeny. Causal similarity: gene2c, developmental, and evolu2onary processes occur across the tree of life. Unobservable processes least directly experienced during ontogeny.

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Biology Educa2on Research

  • We have thousands of studies of student learning

difficul2es in biology (e.g., see Duit bibliography).

  • What robust, generalizable claims can be made

about student thinking about life?

  • An important goal of all disciples should be work

towards causal principles that have broad explanatory power.

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Thinking about life: diversity

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Contexts and evolu2onary reasoning: experiments

Trait gain or loss Coherence of ideas across lineages (as well as between popula2ons vs. species).

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ACORNS instrument (Nehm et al. 2012)

Experimental research design to try to establish generalizable findings about student reasoning.

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Wri>en (or oral) explana0ons

How would biologists explain how a species of cactus with spines evolved from a species of cactus without spines? Concepts (composition) Model (how concepts are arranged or structured) 12,000 + wriZen explana2ons, 100+ interviews

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Categorizing concepts students use to explain evolu2onary change

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From concepts to models

Nehm et al. (2012)

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Comparing ACORNS item: cogni0ve coherence?

coherence

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Comparing ACORNS item: cogni0ve coherence?

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Contexts and evolu2onary reasoning

Reasoning bias Cita0on

  • 1. Within-vs. between species (+ within)

Nehm & Ha, 2011, JRST, Ha & Nehm 2014

  • 2. Trait gain vs. trait loss (+gain)

Nehm & Ha, 2011, JRST

  • 3. Animal vs. plant (+animal)

Opfer, Nehm, Ha, 2012, JRST

  • 4. Familiar vs. unfamiliar (gender effects)

Opfer, Nehm, Ha, 2012, JRST, etc.

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Context: impact on concepts and models in novices

Nehm & Ha (2011)

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See also Nehm & Ridgway 2011

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What about experts (evolutionary biologists?)

Ph.D.

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Item a: cheetah Item b: bacteria Item c: rose

Nehm & Ridgway, 2011

  • Novices “frame”

problems using concrete surface features.

  • Experts solve

problems using domain principles (e.g., natural selection).

  • Significantly greater

coherence characterizes experts; multiple explanatory models characterize novices.

Explanatory model “A” Explanatory model “B” Explanatory model “C” Explanatory model “N”

Expert reasoning Novice reasoning

Contexts: novices and experts

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Expert-like (scien2fic) Novice-like (naïve) Mixed Nehm & Ha, 2011 Journal of Research in Science Teaching Contextual stability (coherence) Co-existence, rearrangements of knowledge elements, as exper2se grows

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Generalizability of model?

  • Studies of several

thousand students in Germany, Indonesia, China, and Korea.

  • Several years of

transla2on, back- transla2on, interviews, wriZen assessments.

  • Computer and human

scoring (Ha, Nehm et

  • al. 2013).
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Contexts: Trait gain and loss in animals and plants

Animal gain is easiest Plants hard: gain & loss Similar reasoning across nations

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Context effects: naïve (intuitive) ideas Trait gain and loss in animals and plants

> MIS loss all nations

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Coherence? Reasoning models across 4 contexts

50% scientific models 25% mixed models 25% naïve models

Explanatory tasks

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Thinking about biology

Student reasoning is strongly controlled by diversity Predictable reasoning difficulties

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Contextual learning paZerns through 2me

Norma2ve Mixed Naive None N = 856 students

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Unity & diversity of life

  • How do humans at different

educational levels think about biological similarities and differences across scales and contexts?

  • What types of causal

processes are invoked to explain similarities and differences across biological scales and contexts?

Source: Wikipedia Dobzhansky

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Conclusions

  • Biological thinking appears to be

conceptually constrained by the type of lineage that is evolving, along with biological scale, polarity, and familiarity;

  • Most learners across studies

display mixtures of intui2ve and scien2fic resources, not just right or wrong models, and lack cogni2ve coherence.

  • Experts display coherent reasoning

using norma2ve concepts—there is no “diversity effect.”

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Why should we care about these findings?

  • Researcher: in a general sense it is

important for biology educators to have robust models of how students reason about the diversity of life.

  • Curriculum: case studies of par2cular taxa

and traits do not appear to promote coherence; unique models are built for each case.

  • Pedagogy: certain contexts predictably elicit

naïve (or norma2ve) reasoning; knowing contexts can make teaching more efficient.

  • Assessment: without tes2ng students across

contexts we can over- or underes2mate competency.

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Organizers for invita2on US Na2onal Science Founda2on REESE, CAREER, TUES, EAR programs for funding. Graduate students and postdocs (Minsu Ha, Meghan Federer, Chad Campbell, Liz Beggrow, Robyn Tornabene) and data collaborators Irvin Schonfeld, Ute Harms, Jorg Grosschedl and John Opfer.

Thank you!