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ABSTRACT:The procedural understanding of university students at the freshman level, prior to instruction, has been investigated in the context of experimental work in physics. A written instrument was used to explore students' ideas about data collection, data processing, and data comparison; in particular, the need to repeat measurements and the implications of the scatter associated with numerical and graphical data. Two types of reasoning used by students in the laboratory, viz. point and set reasoning, are proposed and used to classify students' responses. The findings show a high degree of consistency of these modes of reasoning across the experimental phases of data collection and data processing. A framework is suggested from which a laboratory teaching program may be developed.

Conventional physics laboratory courses generally include an emphasis on increasing students' ability to carry out data analysis according to scientific practice, in particular, those aspects that relate to measurement uncertainty. This study evaluates the efficacy of the conventional approach by analyzing the understanding of measurement of freshmen following the physics major sequence, i.e., top achievers, with regard to data collection, data processing, and data comparison, through pre-and postinstruction tests by using an established instrument. The findings show that the laboratory course improved the performance of the majority of students insofar as the more mechanical aspects of data collection and data processing were concerned. However, only about 20% of the cohort of physics majors exhibited a deeper understanding of measurement uncertainty required for data comparison.

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An evaluation of a course aimed at developing university students' understanding of the nature of scientific measurement and uncertainty is described. The course materials follow the framework for metrology as recommended in the Guide to the Expression of Uncertainty in Measurement (GUM). The evaluation of the course is based on responses to written questionnaires administered to a cohort of 76 first year physics students both pre-and post-instruction, which were interpreted in terms of 'point' or 'set' reasoning. These findings are compared with responses from a control group of 70 students who completed a similar laboratory course apart from the use of traditional approaches to measurement and data analysis. The results suggest that the GUM framework, together with the specific teaching strategies described, provides opportunities for more effective learning of measurement and uncertainty in the introductory laboratory.

Traditionally physics laboratory courses at the freshman level have aimed to demonstrate various principles of physics introduced in lectures. Experiments tend to be quantitative in nature with experimental and data analysis techniques interwoven as distinct strands of the laboratory course.1 It is often assumed that, in this way, students will end up with an understanding of the nature of measurement and experimentation. Recent research studies have, however, questioned this assumption.2,3 They have pointed to the fact that freshmen who have completed physics laboratory courses are often able to demonstrate mastery of the mechanistic techniques (e.g., calculating means and standard deviations, fitting straight lines, etc.) but lack an appreciation of the nature of scientific evidence, in particular the central role of uncertainty in experimental measurement. We believe that the probabilistic approach to data analysis, as advocated by the International Organization for Standardization (ISO), will result in a more coherent framework for teaching measurement and measurement uncertainty in the introductory physics laboratory course.

This paper describes a course aimed at developing understanding of measurement and uncertainty in the introductory physics laboratory. The course materials, in the form of a student workbook, are based on the probabilistic framework for measurement as recommended by the International Organization for Standardization in their publication Guide to the Expression of Uncertainty in Measurement (GUM).