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Abstract In this study, we used Epistemic Network Analysis (ENA) to represent data generated by Natural Language Processing (NLP) analytics during an activity based on the Knowledge Integration (KI) framework. The activity features a web-based adaptive dialog about energy transfer in photosynthesis and cellular respiration. Students write an initial explanation, respond to two adaptive prompts in the dialog, and write a revised explanation. The NLP models score the KI level of the initial and revised explanations. They also detect the ideas in the explanations and the dialog responses. The dialog uses the detected ideas to prompt students to elaborate and refine their explanations. Participants were 196 8th-grade students at a public school in the Western United States. We used ENA to represent the idea networks at each KI score level for the revised explanations. We also used ENA to analyze the idea trajectories for the initial explanation, the two dialog responses, and the final explanation. Higher KI levels were associated with more links and increased frequency of mechanistic ideas in ENA representations. Representation of the trajectories suggests that the NLP adaptive dialog helped students who started with descriptive and macroscopic ideas to add more microscopic ideas. The dialog also helped students who started with partially linked ideas to keep linking the microscopic ideas to mechanistic ideas. We discuss implications for STEM teachers and researchers who are interested in how students build on their ideas to integrate their ideas.
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Integrative Analysis Using Big Ideas: Energy Transfer and Cellular Respiration
Big ideas in science education are meant to be interpretive frameworks that empower student learning. Unfortunately, outside of the broad conception of scientific evaluation, there are few theoretical explanations of how this might happen. Therefore, we contribute one such explanation, an instructional concept called integrative analysis wherein students use a big idea to interconnect isolated scenarios and enrich their meanings. We illustrate the characteristics and value of integrative analysis within an empirical study of student learning in 9th-grade biology. The study focused on using energy transfer as a big idea for teaching cellular respiration. Fifty-nine students were randomly assigned to one of two instructional conditions. In the “analysis” condition, students processed a set of three manipulatives representing cellular respiration molecules; then, they abstracted the deep energy transfer structure of these manipulatives as a big idea. In the “recognition” condition, students processed the same molecule-manipulatives, but without energy interpretations. Instead, they constructed additional manipulatives using novel materials. Then, students in both conditions received an identical lesson where they used their knowledge of the manipulatives to learn about one cellular respiration process, glycolysis. Specifically, students processed a sequence of three texts describing glycolysis, annotating the texts with either their deep energy transfer structure (analysis condition) or their contextualized knowledge of the manipulatives (recognition condition). A posttest showed that in the analysis condition, this process was significantly integrative as evidenced by analysis students’ advantage over recognition students in connecting glycolysis to novel phenomena and generating causal explanations about glycolysis.
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- PAR ID:
- 10418239
- Date Published:
- Journal Name:
- Journal of Science Education and Technology
- ISSN:
- 1059-0145
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10956-023-10040-5
