After Lab Ends: How Students Analyze and Interpret Experimental Data

Bridget M. Smyser; Sahar Tariq

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Conference: 2016 ASEE Annual Conference & Exposition
Location: New Orleans, Louisiana
Publication Date: June 26, 2016
Start Date: June 26, 2016
End Date: June 29, 2016
ISBN: 978-0-692-68565-5
ISSN: 2153-5965
Conference Session: Division Experimentation & Lab-Oriented Studies: Laboratory Pedagogy
Tagged Division: Division Experimentation & Lab-Oriented Studies
Page Count: 24
DOI: 10.18260/p.26541
Permanent URL: https://peer.asee.org/26541
Download Count: 9536

Paper Authors

biography

Bridget M. Smyser Northeastern University

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Dr. Smyser is an Associate Academic Specialist and the Lab Director of the Mechanical and Industrial Engineering.

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biography

Sahar Tariq Northeastern University

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Mechanical Engineer from Bangladesh

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Abstract

High-level skills in analysis, scientific argument, and data presentation are a desired outcome of engineering laboratory classes. Students are asked to present data in a way that supports their conclusions and provides estimates of the accuracy of their answers. Although students are specifically asked to perform certain analysis tasks, and can explain what these tasks should be, they often fail to correctly interpret or make inferences from their data. The goal of this study is to examine the processes that students use to interpret and analyze experimental data, from the time they finish their lab experiments until they turn in their lab reports. A survey was administered to students who had completed a laboratory course in Measurements and Analysis. Students were asked to describe the steps they took to analyze and present experimental data. They were also asked to identify how often they took certain desired actions, such as estimating uncertainties, comparing data to the literature, performing statistical analysis, and other commonly accepted best practices for data analysis. In addition, past student lab reports and experimental design projects were examined for evidence of these best practices. Results show a distinct disconnect between the actions students are asked to take, the actions students described in their narratives, and the behaviors exhibited in student work. Students tended to overestimate how often they examine data for consistency, anticipate results from theory, and justify corrections or adjustments to the data, although they recognize the value of these actions. Certain behaviors were strongly correlated with high grades. For example, students who described their calculation methods in detail showed a positive correlation of greater than 0.75 between this behavior and their lab report grades. In addition, there was a strong positive correlation between the total number of best practices demonstrated in student work and high grades, with correlation values greater than 0.74 in all cases. Results suggest that students need to be asked explicitly to use the accepted best practices, even with junior and senior level students. There is also evidence that students treat lab reports as extended homework assignments with a single correct answer, which prevents them from exhibiting high-level data analysis skills. Based on the results of this work, interventions are being developed to give students additional exposure to open-ended problems and practice with explaining data processing.

Citation
Format

Smyser, B. M., & Tariq, S. (2016, June), After Lab Ends: How Students Analyze and Interpret Experimental Data Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26541

TY - CPAPER
AB - High-level skills in analysis, scientific argument, and data presentation are a desired outcome of engineering laboratory classes. Students are asked to present data in a way that supports their conclusions and provides estimates of the accuracy of their answers. Although students are specifically asked to perform certain analysis tasks, and can explain what these tasks should be, they often fail to correctly interpret or make inferences from their data. The goal of this study is to examine the processes that students use to interpret and analyze experimental data, from the time they finish their lab experiments until they turn in their lab reports. A survey was administered to students who had completed a laboratory course in Measurements and Analysis. Students were asked to describe the steps they took to analyze and present experimental data. They were also asked to identify how often they took certain desired actions, such as estimating uncertainties, comparing data to the literature, performing statistical analysis, and other commonly accepted best practices for data analysis. In addition, past student lab reports and experimental design projects were examined for evidence of these best practices. Results show a distinct disconnect between the actions students are asked to take, the actions students described in their narratives, and the behaviors exhibited in student work. Students tended to overestimate how often they examine data for consistency, anticipate results from theory, and justify corrections or adjustments to the data, although they recognize the value of these actions. Certain behaviors were strongly correlated with high grades. For example, students who described their calculation methods in detail showed a positive correlation of greater than 0.75 between this behavior and their lab report grades. In addition, there was a strong positive correlation between the total number of best practices demonstrated in student work and high grades, with correlation values greater than 0.74 in all cases. Results suggest that students need to be asked explicitly to use the accepted best practices, even with junior and senior level students. There is also evidence that students treat lab reports as extended homework assignments with a single correct answer, which prevents them from exhibiting high-level data analysis skills. Based on the results of this work, interventions are being developed to give students additional exposure to open-ended problems and practice with explaining data processing.
AU - Bridget M. Smyser
AU - Sahar Tariq
CY - New Orleans, Louisiana
DA - 2016/06/26
PB - ASEE Conferences
TI - After Lab Ends: How Students Analyze and Interpret Experimental Data
UR - https://peer.asee.org/26541
DO - 10.18260/p.26541
ER -