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While evolutionary theory follows from observable facts and logical inferences (Mayr, 1985), historically, the origin of novel inheritable variations was a major obstacle to acceptance of natural selection (Bowler, 1992; Bowler, 2005). While molecular mechanisms address this issue (Jablonka and Lamb, 2005), analysis of responses to the Biological Concept Inventory (BCI) (Klymkowsky et al., 2010), revealed that molecular biology majors rarely use molecular level ideas in their discourse, implying that they do not have an accessible framework within which to place evolutionary variation. We developed a "Socratic tutorial" focused on Muller's categorization of mutations' phenotypic effects (Muller, 1932). Using a novel vector-based method to analyzed students' essay responses, we found that a single interaction with this tutorial led to significant changes in thinking toward a clearer articulation of the effects of mutational change. We suggest that Muller's morphs provides an effective framework for facilitating student learning about mutational effects and evolutionary mechanisms.
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23213431 ???displayArticle.pmcLink???PMC3509460 ???displayArticle.link???Biol Open
Fig. 1. BCI based insights into student “genetic” thinking.The table (top) illustrates the overall correct responses (in bold) to four questions from the BCI. The graphs (below) and the right two columns of the table reflect responses to these same questions by a group of fourteen introductory college level biology classes (a total of 2197 students) at seven different colleges and universities, administered over the period from 2006 to 2011, together with the responses from a group of 85 middle and high school teachers who answered the BCI online in response to an email to the National Science Teachers Association biology listserv. The graphs display the responses to the questions (a,b,c,d and unanswered “un”) together with the standard deviation for each response. The correct choices are indicated by red circles.
Fig. 2. Vector analysis of student responses.This illustrates our scheme for visualizing the clarity, correctness, and confusion present in student responses. The widths of arrows (and the diameter of the circle centered around 0,0) reflect the number/percentage of students in that group. Arrows that fall off the X axis contain aspects of correctness and either cant or mistakes.
Fig. 3. Changes in student thinking.Students were asked to work through either the Graphical Thinking (top panels) or the Mendel/Muller (bottom panels) activities in groups. Student responses to the “How might a mutation be creative?” question pre- (left panels) and post- (right panels) treatment were analyzed.
Aharoni,
The 'evolvability' of promiscuous protein functions.
2005, Pubmed
Aharoni,
The 'evolvability' of promiscuous protein functions.
2005,
Pubmed Bergthorsson,
Ohno's dilemma: evolution of new genes under continuous selection.
2007,
Pubmed Berkman,
Science education. Defeating creationism in the courtroom, but not in the classroom.
2011,
Pubmed Blount,
Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli.
2008,
Pubmed Bowler,
Revisiting the eclipse of Darwinism.
2005,
Pubmed Bowling,
Development and evaluation of a genetics literacy assessment instrument for undergraduates.
2008,
Pubmed Burga,
Predicting mutation outcome from early stochastic variation in genetic interaction partners.
2011,
Pubmed Casanueva,
Fitness trade-offs and environmentally induced mutation buffering in isogenic C. elegans.
2012,
Pubmed Copley,
Enzymes with extra talents: moonlighting functions and catalytic promiscuity.
2003,
Pubmed Garvin-Doxas,
Understanding randomness and its impact on student learning: lessons learned from building the Biology Concept Inventory (BCI).
2008,
Pubmed Klymkowsky,
Recognizing student misconceptions through Ed's Tools and the Biology Concept Inventory.
2008,
Pubmed Lindquist,
Protein folding sculpting evolutionary change.
2009,
Pubmed Lynch,
The frailty of adaptive hypotheses for the origins of organismal complexity.
2007,
Pubmed Muller,
BAR DUPLICATION.
1936,
Pubmed Powell,
Science education: spare me the lecture.
2003,
Pubmed Smith,
The problem of revealing how students think: Concept inventories and beyond.
2010,
Pubmed Smith,
The Genetics Concept Assessment: a new concept inventory for gauging student understanding of genetics.
2008,
Pubmed Tokuriki,
Protein dynamism and evolvability.
2009,
Pubmed Wagner,
Robustness, evolvability, and neutrality.
2005,
Pubmed