Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Educational Research and Methods
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?
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. https://peer.asee.org/29659
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