Strongly and weakly directed approaches to teaching multiple representation use in physics

Patrick B Kohl,Noah D Finkelstein,David Rosengrant

doi:10.1103/physrevstper.3.010108

Abstract

Good use of multiple representations is considered key to learning physics, and so there is considerable motivation both to learn how students use multiple representations when solving problems and to learn how best to teach problem solving using multiple representations. In this study of two large-lecture algebra-based physics courses at the University of Colorado (CU) and Rutgers, the State University of New Jersey, we address both issues. Students in each of the two courses solved five common electrostatics problems of varying difficulty, and we examine their solutions to clarify the relationship between multiple representation use and performance on problems involving free-body diagrams. We also compare our data across the courses, since the two physics-education-research-based courses take substantially different approaches to teaching the use of multiple representations. The course at Rutgers takes a strongly directed approach, emphasizing specific heuristics and problem-solving strategies. The course at CU takes a weakly directed approach, modeling good problem solving without teaching a specific strategy. We find that, in both courses, students make extensive use of multiple representations, and that this use (when both complete and correct) is associated with significantly increased performance. Some minor differences in representation use exist, and are consistent with the types of instruction given. Most significant are the strong and broad similarities in the results, suggesting that either instructional approach or a combination thereof can be useful for helping students learn to use multiple representations for problem solving and concept development.

Highlights

Instructors and researchers in physics education researchPERhave long argued that students can benefit from solving problems that require the use of multiple representations together.[1,2,3,4,5,6] The distinction between multiple representation problems and other problems is somewhat artificial, as it is difficult to imagine solving any physics problem or making sense of any physics idea without making use of more than one representationin thought, if not on paper
Students in each of the two courses solved five common electrostatics problems of varying difficulty, and we examine their solutions to clarify the relationship between multiple representation use and performance on problems involving free-body diagrams
We confirm that multiple representation use is important in successful physics problem solving as seen in previous work, but find that the dependence is not trivial

Summary

Introduction

Instructors and researchers in physics education researchPERhave long argued that students can benefit from solving problems that require the use of multiple representations together.[1,2,3,4,5,6] The distinction between multiple representation problems and other problems is somewhat artificial, as it is difficult to imagine solving any physics problem or making sense of any physics idea without making use of more than one representationin thought, if not on paper. Experts tend to use multiple representations in their problem setups more often than novices, who have a tendency to jump directly to mathematics.[1,7] use of multiple representations brings student problem-solving procedures more in line with expert procedures. These differences extend beyond problem solving, as research has shown that novices and professional scientists differ significantly in their ability and willingness to use multiple representations productively in more applied settings such as the laboratory or workplace.[8,9] It has even been suggested that competency with several representations of a concept is a prerequisite for expertlike understanding,[10] and popular research-based physics assessments have implicitly acknowledged this point by including a spread of representations in their questions and by requiring translations among representations to solve problems.[11,12,13]

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Conclusion