Abstract
An increasing number of introductory physics courses are seeking to incorporate “authentic practices,” and a large area of focus in this trend is the incorporation of computational problems into the curriculum. These problems offer students an opportunity to engage with the programming practices and numerical problem-solving methods used by physicists. Understanding how instructors approach teaching such problems is essential for improving instruction and problem design. We conducted a phenomenographic study using semistructured interviews with undergraduate learning assistants in a problem-based introductory mechanics course that incorporates several computational problems. The learning assistants’ prior involvement as students, along with their relatively fewer experiences with programming and physics compared to the faculty instructors, give them a unique perspective on teaching in the course. We present here the results of our analysis: the identification of four approaches that learning assistants make take to teaching computational problems in this course. These approaches, programming focus, learning physics via computation focus, computation as a tool focus, and shifting perceptions of learning focus, provide a lens for understanding the different ways learning assistants perceive computation, the degree to which they take up course-intended learning goals surrounding computation, the factors that may impact the approaches they take, and how we might affect their approaches through training and support.Received 6 February 2019Accepted 12 May 2020DOI:https://doi.org/10.1103/PhysRevPhysEducRes.16.010139Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEpistemology, attitudes, & beliefsInstructional strategiesTechnologyPhysics Education Research
Highlights
In order to meet the repeatedly identified need for wellprepared science, technology, engineering, and mathematics (STEM) majors in both modern academia and industry [1,2,3,4,5], undergraduate physics education has adapted in many ways [6,7,8,9,10]
Our aim was to examine how they approached teaching computational problems in this environment, because understanding how these uniquely positioned members of the teaching team work in such a complex environment provides valuable insight into how we can best design and implement computational work for students, and gaining such insight will be increasingly important as computation becomes more prevalent in physics curricula
The participants were undergraduate learning assistants, a relatively little studied population [27,29,30,31], the course was taught in a problem-based learning style, a context rich with complex interactions among students and instructors [11,20,21,23], and the course made use of computational problems, a unique type of activity at the center of the push to incorporating authentic practices into physics education [19,33,34,35,36,37,38,39]
Summary
In order to meet the repeatedly identified need for wellprepared science, technology, engineering, and mathematics (STEM) majors in both modern academia and industry [1,2,3,4,5], undergraduate physics education has adapted in many ways [6,7,8,9,10]. Instructors may need to moderate group dynamics and guide students’ learning in a way that is not demanded of them in a traditional lecture course [11,22,23,24] This context offers a rich environment in which to study instructors’ enacted teaching methods, that is, their practices, and their beliefs about teaching and learning. There are a variety of definitions and implementations of problem-based learning, but these learning environments are often characterized by students working in collaborative groups, instructors functioning as guides rather than lecturers, and problems that require students to leverage decision making, planning, and problem-solving skills. The dynamic structure and complex tasks in problem-based learning environments make them a rich environment for educational research, and such research has been done on the perspectives of both instructors and students
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