Measuring Computing Self-Efficacy

Hannah Kolar; Adam R Carberry; Ashish Amresh

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Conference: 2013 ASEE Annual Conference & Exposition
Location: Atlanta, Georgia
Publication Date: June 23, 2013
Start Date: June 23, 2013
End Date: June 26, 2013
ISSN: 2153-5965
Conference Session: Motivation and Self-Efficacy
Tagged Division: Educational Research and Methods
Page Count: 7
Page Numbers: 23.889.1 - 23.889.7
DOI: 10.18260/1-2--22274
Permanent URL: https://peer.asee.org/22274
Download Count: 612

Paper Authors

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Hannah Kolar Arizona State University

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Hannah Kolar is currently an undergraduate at Arizona State University. She is a rising senior working toward a Bachelor's of Science in Engineering from the College of Technology and Innovation with a focus on Mechanical Engineering and a secondary focus in Aeronautical Engineering. She has been working on engineering education research with Dr. Adam Carberry since November 2011.

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Adam R Carberry Arizona State University orcid.org/0000-0003-0041-7060

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Adam R. Carberry, Ph.D., is an Assistant Professor at Arizona State University in the College of Technology & Innovation’s Department of Engineering. He earned a B.S. in Materials Science Engineering from Alfred University, and received his M.S. and Ph.D., both from Tufts University, in Chemistry and Engineering Education respectively. Dr. Carberry was previously an employee of the Tufts’ Center for Engineering Education & Outreach and manager of the Student Teacher Outreach Mentorship Program (STOMP).

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Ashish Amresh Arizona State University orcid.org/0000-0003-3722-0720

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Ashish Amresh is an Assistant Professor in the College of Technology and Innovation and is leading the Computer Gaming curriculum initiatives at Arizona State University, where he founded the Computer Gaming Certificate and the Camp Game summer program. In the past he worked for Ronin Entertainment as Graphics Software Engineer known for Star Wars: Force Commander and Bruce Lee Quest of the Dragon. He is the author of Unreal Game Development, a popular book used for teaching game development skills for high school students. He has given numerous talks on using games in the class room for enhancing math and science learning. He is currently researching on how to improve learning of math word problems using games.

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Abstract

Measuring Computing Self-EfficacyAbstractComputing is a field of study that is growing in importance everyday. Unfortunately,studies have shown there to be a huge dropout rate within computing-related majors. Thefollowing project discusses the creation of an instrument intended to ultimatelyinvestigate whether computing skill self-efficacy is tied to the retention findings.An instrument was developed throughout the 2012 spring semester that tested the self-efficacy of first-year engineering and computing students enrolled in a fused course. Theinstrument asked students a series of questions pertaining to many different computingtasks. Questions utilized a 100-point range on a Likert scale with 10-unit intervals; 0being “cannot do at all” and 100 being “highly certain can do.” The preliminary resultssuggested the new instrument held promise in its ability to accurately assess computingself-efficacy, yet the sample size was too small to validate the instrument.The instrument was subsequently given to a larger group of students (N = 271). Thesample consisted students who were attending the Ultimate Intel Experience Internship.The instrument was disseminated using the online tool, Survey Monkey, on the first dayof the internship. Demographic information was also collected, specifically previouscomputing experience through the question: “Have you ever taken a course to learn howto program?”The instrument was validated using both content and criterion-related validity. Contentvalidity came by way of two resources. First, we researched past studies on the field ofcomputing and computing-related self-efficacy. Second, we conducted fifteen-minute in-person interviews with computing professors at a large southwest university to make sureour questions were relevant and related to the study. Criterion-related validity wasconducted by using our measure of previous programming coursework.The statistical software package, SPSS, was used to analyze the data. Exploratory factoranalysis revealed two factors. The first factor contained all seven items, which wesubsequently named "computing". The second factor contained the only two itemspertaining to hardware. The relative factor loadings allowed us to disregard the hardwarefactor in an effort to focus on the one overall computing factor. A very basic correlationcheck of the new computing factor z-scores with our first broad question, “How confidentdo you feel solving a computing task?”, showed that our seven items correlated highly tothe general focus of computing (r = 0.711). A check of reliability revealed excellentinternal reliability (α = 0.929). Finally, we performed a t-test of each individual item tosee if there was a significant difference in the students who had taken a computingcourse. All items were significant to at least p ≤ .05 meaning that students with priorexperience in computing rated their level of self-efficacy significantly higher than theother students as suspected. The instrument was successfully validated using content andcriterion-related validity.

Citation
Format

Kolar, H., & Carberry, A. R., & Amresh, A. (2013, June), Measuring Computing Self-Efficacy Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--22274

TY - CPAPER
AB - Measuring Computing Self-EfficacyAbstractComputing is a field of study that is growing in importance everyday. Unfortunately,studies have shown there to be a huge dropout rate within computing-related majors. Thefollowing project discusses the creation of an instrument intended to ultimatelyinvestigate whether computing skill self-efficacy is tied to the retention findings.An instrument was developed throughout the 2012 spring semester that tested the self-efficacy of first-year engineering and computing students enrolled in a fused course. Theinstrument asked students a series of questions pertaining to many different computingtasks. Questions utilized a 100-point range on a Likert scale with 10-unit intervals; 0being “cannot do at all” and 100 being “highly certain can do.” The preliminary resultssuggested the new instrument held promise in its ability to accurately assess computingself-efficacy, yet the sample size was too small to validate the instrument.The instrument was subsequently given to a larger group of students (N = 271). Thesample consisted students who were attending the Ultimate Intel Experience Internship.The instrument was disseminated using the online tool, Survey Monkey, on the first dayof the internship. Demographic information was also collected, specifically previouscomputing experience through the question: “Have you ever taken a course to learn howto program?”The instrument was validated using both content and criterion-related validity. Contentvalidity came by way of two resources. First, we researched past studies on the field ofcomputing and computing-related self-efficacy. Second, we conducted fifteen-minute in-person interviews with computing professors at a large southwest university to make sureour questions were relevant and related to the study. Criterion-related validity wasconducted by using our measure of previous programming coursework.The statistical software package, SPSS, was used to analyze the data. Exploratory factoranalysis revealed two factors. The first factor contained all seven items, which wesubsequently named &quot;computing&quot;. The second factor contained the only two itemspertaining to hardware. The relative factor loadings allowed us to disregard the hardwarefactor in an effort to focus on the one overall computing factor. A very basic correlationcheck of the new computing factor z-scores with our first broad question, “How confidentdo you feel solving a computing task?”, showed that our seven items correlated highly tothe general focus of computing (r = 0.711). A check of reliability revealed excellentinternal reliability (α = 0.929). Finally, we performed a t-test of each individual item tosee if there was a significant difference in the students who had taken a computingcourse. All items were significant to at least p ≤ .05 meaning that students with priorexperience in computing rated their level of self-efficacy significantly higher than theother students as suspected. The instrument was successfully validated using content andcriterion-related validity.
AU - Hannah Kolar
AU - Adam R Carberry
AU - Ashish Amresh
CY - Atlanta, Georgia
DA - 2013/06/23
PB - ASEE Conferences
TI - Measuring Computing Self-Efficacy
UR - https://peer.asee.org/22274
DO - 10.18260/1-2--22274
ER -