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Register a new userExploring student facility with goes like reasoning in introductory physics
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Added by
Charlotte Zimmerman
Publication date
2020
fields
Physics
and research's language is
English
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Covariational reasoning -- reasoning about how changes in one quantity relate to changes in another quantity -- has been examined extensively in mathematics education research. Little research has been done, however, on covariational reasoning in introductory physics contexts. We explore one aspect of covariational reasoning: ``goes like reasoning. ``Goes like reasoning refers to ways physicists relate two quantities through a simplified function. For example, physicists often say that ``the electric field goes like one over r squared. While this reasoning mode is used regularly by physicists and physics instructors, how students make sense of and use it remains unclear. We present evidence from reasoning inventory items which indicate that many students are sense making with tools from prior math instruction, that could be developed into expert ``goes like thinking with direct instruction. Recommendations for further work in characterizing student sense making as a foundation for future development of instruction are made.
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Proficiency with calculating, reporting, and understanding measurement uncertainty is a nationally recognized learning outcome for undergraduate physics lab courses. The Physics Measurement Questionnaire (PMQ) is a research-based assessment tool that measures such understanding. The PMQ was designed to characterize student reasoning into point or set paradigms, where the set paradigm is more aligned with expert reasoning. We analyzed over 500 student open-ended responses collected at the beginning and the end of a traditional introductory lab course at the University of Colorado Boulder. We discuss changes in students understanding over a semester by analyzing pre-post shifts in student responses regarding data collection, data analysis, and data comparison.
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One desired outcome of introductory physics instruction is that students will develop facility with reasoning quantitatively about physical phenomena. Little research has been done regarding how students develop the algebraic concepts and skills involved in reasoning productively about physics quantities, which is different from either understanding of physics concepts or problem-solving abilities. We introduce the Physics Inventory of Quantitative Literacy (PIQL) as a tool for measuring quantitative literacy, a foundation of mathematical reasoning, in the context of introductory physics. We present the development of the PIQL and evidence of its validity for use in calculus-based introductory physics courses. Unlike concept inventories, the PIQL is a reasoning inventory, and can be used to assess reasoning over the span of students instruction in introductory physics. Although mathematical reasoning associated with the PIQL is taught in prior mathematics courses, pre/post test scores reveal that this reasoning isnt readily used by most students in physics, nor does it develop as part of physics instruction--even in courses that use high-quality, research-based curricular materials. As has been the case with many inventories in physics education, we expect use of the PIQL to support the development of instructional strategies and materials--in this case, designed to meet the course objective that all students become quantitatively literate in introductory physics.
The Physics Inventory of Quantitative Literacy (PIQL), a reasoning inventory under development, aims to assess students physics quantitative literacy at the introductory level. The PIQLs design presents the challenge of isolating types of mathematical reasoning that are independent of each other in physics questions. In its current form, the PIQL spans three principle reasoning subdomains previously identified in mathematics and physics education research: ratios and proportions, covariation, and signed (negative) quantities. An important psychometric objective is to test the orthogonality of these three reasoning subdomains. We present results from exploratory factor analysis, confirmatory factor analysis, and module analysis that inform interpretations of the underlying structure of the PIQL from a student viewpoint, emphasizing ways in which these results agree and disagree with expert categorization. In addition to informing the development of existing and new PIQL assessment items, these results are also providing exciting insights into students quantitative reasoning at the introductory level.
Computational Thinking (CT) is still a relatively new term in the lexicon of learning objectives and science standards. There is not yet widespread agreement on the precise definition or implementation of CT, and efforts to assess CT are still maturing, even as more states adopt K-12 computer science standards. In this article we will try to summarize what CT means for a typical introductory (i.e. high school or early college) physics class. This will include a discussion of the ways that instructors may already be incorporating elements of CT in their classes without knowing it. Our intention in writing this article is to provide a helpful, concise and readable introduction to this topic for physics instructors. We also put forward some ideas for what the future of CT in introductory physics may look like.
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