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Effects of Student Strategies on Successful Problem Solving

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Conference

2012 ASEE Annual Conference & Exposition

Location

San Antonio, Texas

Publication Date

June 10, 2012

Start Date

June 10, 2012

End Date

June 13, 2012

ISSN

2153-5965

Conference Session

Research Informing Teaching Practice I

Tagged Division

Educational Research and Methods

Page Count

13

Page Numbers

25.508.1 - 25.508.13

Permanent URL

https://peer.asee.org/21266

Download Count

44

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Paper Authors

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Sarah Jane Grigg Clemson University

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Sarah Grigg is a Ph.D. candidate in the Industrial Engineering Department at Clemson University.

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Lisa Benson Clemson University

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Abstract

Effects of Student Strategies on Successful Problem SolvingIn order to analyze engineering students’ problem-solving strategies, Tablet PCs are used tocapture student problem solving attempts and the digital ink is analyzed by identifying events ofinterest within problems completed by students in an introductory engineering course. Adatabase of relevant events (tasks, errors, and strategies), has been constructed and organizedinto categories based on a theoretical framework of process activities used during problemsolving: knowledge access, knowledge generation and self-management. In addition, studenterrors are categorized (conceptual, mechanical, and management), and students’ recognition oftheir errors are being analyzed using signal detection theory. Specific goals of this projectinclude: (1) elucidate how first year engineering students learn problem solving strategies, (2)evaluate successful and unsuccessful problem solving strategies, as well as errors andmisconceptions, in terms of cognitive processes; (3) correlate this information with students’prior knowledge of mathematics and engineering. Data collected from 32 students (29 males, 3females) in Spring 2011 has been analyzed using a validated coding structure to identify relevantevents within 5 well-structured word problems which had multiple possible strategies and onecorrect answer. To identify academic stressors students experience as they solve problems, a taskload index (NASA-TLX) was implemented after students completed each problem. The NASA-TLX is a survey with six subscales: three measuring demand put on the participant by the taskand three measuring stress added by the worker themselves as a result of interacting with thetask.Preliminary statistical analysis of solution data for one of the problems (related to efficiency of amulti-stage solar power system) produced interesting results related to problem definition taskssuch as organizing information at the beginning of the problem (defined in this case as beingcontained in the first half of the problem solution). Twenty-six solutions were analyzed fromthis problem set (23 males, 3 females). Statistical comparisons (Students t-test) revealed thatstudents who explicitly identified equations (n=19 out of 26) within their problem solution tooksignificantly longer to complete the problem than those who did not (p= 0.02) and were morelikely to commit errors as evidenced by higher error rates (p=0.04). This runs counter to manyrecommended problem-solving strategies that recommend explicit equation definition andalgebraic manipulation of equation variables before substituting numeric values. Similar resultswere found for identifying unknown values. While perceived performance levels did not differamong students who explicitly identified unknown values (n=17 out of 25) (p=.97), error rateswere moderately higher for those students who explicitly wrote out unknown values initially(p=0.07). Interestingly, restating the problem initially (n=11 out of 26) appears to have the mostpositive impact on performance of all tasks analyzed. Of students who restated the probleminitially, 100% got the correct answer with only 80% of other students getting the answer correct(χ2=0.11), and they completed the problem significantly faster that other students (p=0.05) eventhough restating the problem is a time consuming task. These students also showed moderatetrends toward lower mental demand (p=.11), lower perceived effort exerted (p=0.06), and fewerunidentified conceptual errors (p=0.07). Continuing data analysis includes other aspects ofstudent strategies such as use of diagrams or sketches, students’ error sensitivity, and how theseare affected by gender, ethnicity, and academic preparation. The ultimate goal of this project is tobetter design and present problems in introductory engineering courses to capitalize on strategiesthat lead to successful building of problem-solving skills.

Grigg, S. J., & Benson, L. (2012, June), Effects of Student Strategies on Successful Problem Solving Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. https://peer.asee.org/21266

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