There has been a call to reform science education to integrate scientific thinking practices, such as data interpretation and modeling, with learning content in science classrooms. This call to reform has taken place in both K-12 science education through Next Generation Science Standards and undergraduate education through AAAS initiative Vision and Change in Undergraduate Biology Education. This dissertation work examines undergraduate students' learning of multiple scientific thinking skills in a curricular format called Teaching Real data Interpretation with Models (TRIM) applied to a large-enrollment course in Cellular and Developmental Biology. In TRIM, students are provided worksheets in groups and tasked to interpret authentic biological data. Importantly, groups are tasked to relate their data interpretations to a 2D visual model representation of the relevant biological process. This dissertation work consists of two studies with the overarching question: How do students use model representations to interpret data interpretations? In the first study, we primarily describe how students learn to navigate and interpret discipline-based data representations. We found the majority of groups could construct quality written data interpretations. Qualitative coding analysis on group discourse found students relied on strategies such as decoding the data representation and noticing data patterns together to construct claims. Claims were refined through spontaneous collaborative argumentation. We also found groups used the provided model to connect their data inferences to a biological context. In the second study, we primarily target our analysis on how individual students relate their data interpretations to different modeling tasks, including student-generation of their own model drawing. I interviewed students one-on-one as they worked through TRIM-style worksheets. From iterative qualitative analysis of transcripts and collected video on hand movements, I characterize the forms of reasoning at play at the interface of data and model representations. I propose a model at the end of Study 2 describing three modes of reasoning in data abstraction into models. I found when relating between data and models, students needed to link signs in both representations to a common referent in the real-world phenomenon. Establishing this sign-referent relationship seemed to depend on bringing in outside mechanistic information about the phenomenon. Once a mechanism was established, students could fluidly move between data and model representations through mechanistic reasoning. Thus data abstraction seems to rely on mechanistic reasoning with models. The findings from this dissertation work support the feasibility of student development of multiple scientific thinking skills within a large lecture course, and provide targets for curriculum and assignment designs centered on teaching higher order reasoning skills.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/625586 |
| Date | January 2017 |
| Creators | Zagallo, Patricia, Zagallo, Patricia |
| Contributors | Bolger, Molly S., Bolger, Molly S., Elfring, Lisa K., Nagy, Lisa M., Talanquer, Vicente A., Weinert, Ted |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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