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| Autor/in | Bennett, Steven Carl |
|---|---|
| Titel | Adapting a Framework for Assessing Students' Approaches to Modeling |
| Quelle | (2017), (127 Seiten)
PDF als Volltext Ph.D. Dissertation, Michigan State University |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| ISBN | 978-0-3552-1940-1 |
| Schlagwörter | Hochschulschrift; Dissertation; Models; Learning Theories; Scientific Concepts; Prior Learning; Undergraduate Students; College Science; Majors (Students); Introductory Courses; Biology; Protocol Analysis; Science Education; Cues; Course Content; Learner Engagement; Metacognition |
| Abstract | We used an "approach to learning" theoretical framework to explicate the ways students engage in scientific modeling. Approach to learning theory suggests that when students approach learning deeply, they link science concepts with prior knowledge and experiences. Conversely, when students engage in a surface approach to learning, they memorize without understanding and therefore do not connect new content to prior knowledge and experiences. In this study we modified an approach to learning framework in order to investigate the extent to which undergraduate science majors use a deep or surface approach when modeling. Twenty students enrolled in an introductory biology course participated in two think-aloud interviews, one year apart. The students engaged in a total of five modeling tasks, with each task having different contextual and conceptual familiarity based on course content. We determined student approaches to modeling by observing their use of metacognition, generative thinking, and causal reasoning. We observed that within-student and between-student approach-to-modeling scores differed as the modeling tasks changed, and we attribute the differences to prompt construction; specifically, the presence of cueing words, the type and amount of background information provided, and familiarity with the contexts and concepts addressed in the prompts. Similarly, prompt construction also influences the depth of engagement needed to construct a correct model; some students were able to construct correct models without using deep modeling approaches, while other students were unable to generate correct models despite using deeper approaches. We concluded this study with recommendations for prompt construction that may encourage a diverse group of learners to engage more deeply in modeling tasks. The findings of this study support a theme common in the science education--the importance of helping students connect new information to prior knowledge and experiences, which not only improves learning, but also modeling. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided). |
| Anmerkungen | ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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