A Cognitive Approach to Predicting Academic Success in Computing

Colby Goettel; Barry M. Lunt

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Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: Practice I: Academic Success
Tagged Division: Educational Research and Methods
Tagged Topic: Diversity
Page Count: 22
DOI: 10.18260/1-2--29659
Permanent URL: https://peer.asee.org/29659
Download Count: 563

Paper Authors

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Colby Goettel Brigham Young University

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Puppet admin at Walmart Stores, Inc and graduate student at Brigham Young University in Information Technology.

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biography

Barry M. Lunt Brigham Young University

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Dr. Barry Lunt has taught electronics engineering technology and information technology at Brigham Young University since 1993 where he now serves as full professor and Director of the School of Technology. He has also taught electronics at Utah State University and Snow College. Prior to his work in academics he worked for seven years as a design engineer for IBM in Tucson, AZ. He has consulted for several companies and has worked summer internships for Bell Labs (now Lucent Technologies), Larson - Davis (Utah), IBM (Vermont), and Micron Technologies (Utah and Idaho). His research areas are permanent digital data storage and engineering/computing education.
Dr. Lunt is the author of "Electronic Physical Design" (Pearson Prentice Hall, 2004) and “The Marvels of Modern Electronics” (Dover, 2013) and has produced more than 70 peer-reviewed publications in the areas of electronic physical design, engineering education, and permanent data storage. He has seven U.S. patents and 20 more applied for. He was the chair of the committee that wrote the IT 2008 Model Curriculum, and a member of the task force that wrote the IT 2017 Model Curriculum. He was a member of the task force that wrote the CC2005 document, and is a member of the task force working on the 2020 update to that document.

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Abstract

Incoming freshmen struggle deciding which field they should enter. In computing, there are many fields to choose from and this can cause confusion for new students, especially because the fields are so closely related. For example, many students don't know the difference between computer science (CS), information systems (IS), and information technology (IT). It would be so nice to have a simple way to determine which field would best suit the student. But how do these fields differ? Can their differences be empirically measured?

One potential way to look at differences among the computing fields is to look at the characteristics of the students in each of these fields, including especially the ways in which they learn. CS, IS, and IT all focus on different areas of computing and each requires a different skill set. It seems that people in these various fields even have a preference for being taught differently. Is it possible to predict in which computing discipline an incoming freshman would succeed, based on their learning style preferences? Previous research has shown a correlation between learning preference and academic success for engineering students and other students, but does this correlation also exist for computing students?

In the early 1970s, Dr. David Kolb developed a cognitive model to represent learning preferences. His model works on a two-axis system: concrete experience (CE) versus abstract conceptualization (AC), and reflective observation (RO) versus active experimentation (AE). This two-axis spectrum is meant to place a student in one of four quadrants, showing their learning preferences and strengths.

The x-axis, AE-RO, differentiates between students who prefer to learn by doing, by being active, by seeing results, and those who prefer to learn by watching, listening, taking their time, and relying on observations. The y-axis, AC-CE, differentiates between students who prefer to learn by thinking things out, reasoning, being rational, and those who are more intuitive and prefer to trust their feelings.

It doesn’t appear that any research has been done in this area. Some of the research is close, but most deal with programming aptitude or success in a first year program. Interestingly, hardly any research has taken a cognitive approach. Finally, no research was found that focused on the differences between CS, IS, and IT.

Purpose The purpose of this research is to discover if there is a correlation between a student's preference for CE-AC and RO-AE and their GPA, and overall satisfaction in IT, IS, and CS. This research matters because incoming freshman interested in computing struggle to decide between the various, computing majors. If there is a statistically significant correlation between Kolb's learning styles and success in CS, IS, and IT, then advisement centers could use the LSI to help incoming students choose among computing majors.

Research questions - How strong is the correlation between CE-AC and RO-AE, and college GPA in CS, IS, and IT? - How strong is the correlation between CE-AC and RO-AE, and student satisfaction in CS, IS, and IT? - Is there a correlation between college GPA and student satisfaction? - What is the best multiple regression model to fit these correlations?

Citation
Format

Goettel, C., & Lunt, B. M. (2018, June), A Cognitive Approach to Predicting Academic Success in Computing Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--29659

TY - CPAPER
AB - Incoming freshmen struggle deciding which field they should enter. In computing, there are many fields to choose from and this can cause confusion for new students, especially because the fields are so closely related. For example, many students don&#39;t know the difference between computer science (CS), information systems (IS), and information technology (IT). It would be so nice to have a simple way to determine which field would best suit the student. But how do these fields differ? Can their differences be empirically measured?

One potential way to look at differences among the computing fields is to look at the characteristics of the students in each of these fields, including especially the ways in which they learn. CS, IS, and IT all focus on different areas of computing and each requires a different skill set. It seems that people in these various fields even have a preference for being taught differently. Is it possible to predict in which computing discipline an incoming freshman would succeed, based on their learning style preferences? Previous research has shown a correlation between learning preference and academic success for engineering students and other students, but does this correlation also exist for computing students?

In the early 1970s, Dr. David Kolb developed a cognitive model to represent learning preferences. His model works on a two-axis system: concrete experience (CE) versus abstract conceptualization (AC), and reflective observation (RO) versus active experimentation (AE). This two-axis spectrum is meant to place a student in one of four quadrants, showing their learning preferences and strengths.

The x-axis, AE-RO, differentiates between students who prefer to learn by doing, by being active, by seeing results, and those who prefer to learn by watching, listening, taking their time, and relying on observations. The y-axis, AC-CE, differentiates between students who prefer to learn by thinking things out, reasoning, being rational, and those who are more intuitive and prefer to trust their feelings.

It doesn’t appear that any research has been done in this area. Some of the research is close, but most deal with programming aptitude or success in a first year program. Interestingly, hardly any research has taken a cognitive approach. Finally, no research was found that focused on the differences between CS, IS, and IT.

Purpose
The purpose of this research is to discover if there is a correlation between a student&#39;s preference for CE-AC and RO-AE and their GPA, and overall satisfaction in IT, IS, and CS. This research matters because incoming freshman interested in computing struggle to decide between the various, computing majors. If there is a statistically significant correlation between Kolb&#39;s learning styles and success in CS, IS, and IT, then advisement centers could use the LSI to help incoming students choose among computing majors.

Research questions
- How strong is the correlation between CE-AC and RO-AE, and college GPA in CS, IS, and IT?
- How strong is the correlation between CE-AC and RO-AE, and student satisfaction in CS, IS, and IT?
- Is there a correlation between college GPA and student satisfaction?
- What is the best multiple regression model to fit these correlations?

AU - Colby Goettel
AU - Barry M. Lunt
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - A Cognitive Approach to Predicting Academic Success in Computing
UR - https://peer.asee.org/29659
DO - 10.18260/1-2--29659
ER -