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How Does Peer Instruction Contribute to Learning Advancement in Solving Problems Involving Newton's Second Law?
New Phys.: Sae Mulli 2017; 67: 1327~1341
Published online November 30, 2017;
© 2017 New Physics: Sae Mulli.

Il LEE1, Junehee YOO*2

1 Gwangmyeong High School, Gwangmyeong 14234, Korea
2 Departmenet of Physics Education, Seoul National University, Seoul 08826, Korea
Correspondence to:
Received June 20, 2017; Revised September 28, 2017; Accepted October 10, 2017.
cc This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Peer instruction has proven its effectiveness in various studies. However, a qualitative analysis of which interaction between students contributes to learning has been lacking. This research aimed to investigate the learning progression and advancement with peer instruction in terms of modeling. We analyzed 17 high-school students' discourse and worksheets during peer instruction about Newton's second law. The learning progress was classified into six levels based on the facets construction, validity, and coherence for modeling schema. We identified a relatively more valid and coherent model structure from the upper level students. The learning advancement was categorized into two types. The first type was one in which the lower level students could learn how to construct facets representing the concept by sharing the model structure of the upper level students. The second type was one in which learning advancements occurred due to model co-construction, and the students were able to recognize the incoherencies in the initial model by comparison with another students' model and attempt to revise their model. Peer instruction can contribute to the learning progression for theoretical explanatory modeling.
PACS numbers: 01.40.Ha
Keywords: Peer instruction, Modeling, Learning progression, Learning advancement

November 2017, 67 (11)
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