Fostering Deep Understandings of Emergent Science Concepts

Autor/in	Su, Man
Titel	Fostering Deep Understandings of Emergent Science Concepts
Quelle	(2023), (250 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Arizona State University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	979-8-3795-2740-2
Schlagwörter	Hochschulschrift; Dissertation; Scientific Concepts; Concept Formation; Genetics; Teaching Methods; Instructional Effectiveness; Pretests Posttests; Misconceptions + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Concept learning; Begriffsbildung; Humangenetik; Teaching method; Lehrmethode; Unterrichtsmethode; Unterrichtserfolg; Missverständnis
Abstract	Integrating agent-based models (ABMs) has been a popular approach for teaching emergent science concepts. However, students continue to find it difficult to explain the emergent process of natural selection. This study adopted an ontological framework--the Pattern, Agents, Interactions, Relations, and Causality (PAIR-C)--to guide the design of learning modules. This pre-posttest experimental study examines the effects of the PAIRC module versus the Regular module on fostering students' deep understanding of natural selection. Results show that students in the PAIR-C intervention group performed better in answering deep questions assessing the understanding of inter-level causal relationships than those in the Regular control group. Although students in both groups did not show significantly improved abilities in explaining the natural selection process for other contexts or significant differences in their abilities to explain other emergent phenomena, students in the intervention group demonstrated system-thinking perspectives and fewer misconceptions in their expressions compared to the control group. A close analysis of student misconceptions consolidates that the intervention group demonstrated drastically fewer categories and numbers of misconceptions while those in the control group did not show such drastic changes before and after the study. To precisely address misconceptions and further improve students' learning outcomes, Epistemic Network Analysis was adopted to capture students' misconception characteristics by examining the co-occurrences of different misconception categories as well as the relationship between misconceptions and PAIR-C features. The results of student learning outcomes and misconception characteristics collectively provide directions for improving the instructional design of the PAIR-C module. Furthermore, findings on student engagement levels during learning can also inform future design efforts. Overall, this project sheds light on applying an innovative framework to designing effective learning modules to teach emergent science concepts. [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).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01