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LEARNING BIOLOGICAL EVOLUTION THROUGH COMPUTATIONAL THINKING

Computational thinking is a contemporary mathematical and engineering concept that has been introduced to US science classrooms due to its emphasis within the Next Generation Science Standards (NGSS Lead States, 2013), yet it stands with no clear definition nor explicit methods for inclusion. Because biological evolution, an essential theory within biology, spans across temporal and organizational scales (Aho, 2012), computational thinking may facilitate evolution learning (Wilensky & Reisman, 2006), specifically by overcoming misconceptions, reinforcing the nature of science (NOS), and allowing student embodiment (as students become emerged in their models, i.e., personification; Weinthrop et. al. 2016). The complex nature of both teaching computational thinking and biological evolution lends toward the need for a learning progression that identifies the instructional context, computational product and computational process and spans from simple to complex (as modified from Berland & McNeill, 2010). I developed and present an appropriate learning progression that outlines biological evolution learning coupled with computational thinking. The defined components of computational thinking (input, integration, output and feedback) are coupled with biology student roles. Two major themes of biological evolution, unity and diversity have each been paired with both computational thinking and specific corresponding NGSS standards at levels of increasing complexity. To investigate the effectiveness of the learning progression, I developed and conducted a quasi-experimental research design study. I designed two learning experiences (interventions) which merged computation and biological evolution content based on AP biology laboratory lessons (College Board, 2009). I also developed two instruments for use in the study, one to assess computational knowledge and the other to assess biological evolution knowledge across scales. I measured knowledge gains in both biological evolution and computational thinking quantitatively and explored participant use of biological levels of organization and computational complexity through qualitative analysis of participant artifacts. The quantitative and qualitative results of the study support the argument to include computational thinking into biological evolution knowledge instruction. Knowledge gains differed between the two interventions indicating that one intervention was significantly more successful in learning both biological evolution and computational thinking. Students who made biological level connections across scales (spanning from the micro to the macro levels) also had significantly greater gains in biological knowledge. Considering the results collectively, computational thinking deserves a much greater emphasis within biology classrooms. There are virtually no previous studies which relate computation and evolution across scales and the present study paved the way for questions of importance, support, benefits and overall student achievement in relation to the advancement of science in education. / Teaching & Learning

Identiferoai:union.ndltd.org:TEMPLE/oai:scholarshare.temple.edu:20.500.12613/302
Date January 2020
CreatorsChristensen, Dana, 0000-0002-2448-3794
ContributorsNewton, Kristie Jones, 1973-, Lombardi, Doug, 1965-, Bailey, Janelle M., Han, Insook
PublisherTemple University. Libraries
Source SetsTemple University
LanguageEnglish
Detected LanguageEnglish
TypeThesis/Dissertation, Text
Format297 pages
RightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available., http://rightsstatements.org/vocab/InC/1.0/
Relationhttp://dx.doi.org/10.34944/dspace/286, Theses and Dissertations

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