Knowledge integration in student learning of Newton’s third law: Addressing the action-reaction language and the implied causality

• Main Authors: Lei Bao, Joseph C. Fritchman
• Format: Article
• Language: English
• Published: American Physical Society 2021-09-01
• Series: Physical Review Physics Education Research
• Online Access: http://doi.org/10.1103/PhysRevPhysEducRes.17.020116

Newton’s third law is one of the most important concepts learned early in introductory mechanics courses; however, ample studies have documented a wide range of students’ misconceptions and fragmented understandings of this concept that are difficult to change through traditional instruction. This research develops a conceptual framework model to investigate students’ understanding of Newton’s third law through the knowledge integration perspective. The conceptual framework is established with a central idea emphasizing forces as quantitative measures of physical interactions instead of using the common action-reaction language. Guided by the conceptual framework, assessment and interview results reveal that students’ concepts of Newton’s third law are fragmented without deep understanding. Specifically, three main issues within students’ understanding have been identified: (i) students have a disconnect between time order of events and causal reasoning, (ii) students rely on a memorized equal-and-opposite rule to identify interaction forces, and (iii) students directly link action-reaction language to the belief in a causal relation between the interaction forces. The framework is then applied to develop a new instruction intervention that explicitly targets the central idea of Newton’s third law. Results from pre- and post-testing show that the intervention is effective in helping students develop more integrated understandings of Newton’s third law. Overall, this study shows the potential benefits of applying the conceptual framework method to model student knowledge structures and guide assessment and instruction for promoting knowledge integration and deep learning.