Cluster Analysis in Engineering Education

Andrew Jackson; Nathan Mentzer

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Conference: 2017 ASEE Annual Conference & Exposition
Location: Columbus, Ohio
Publication Date: June 24, 2017
Start Date: June 24, 2017
End Date: June 28, 2017
Conference Session: Quantitative Research Methods
Tagged Division: Educational Research and Methods
Page Count: 16
DOI: 10.18260/1-2--28044
Permanent URL: https://peer.asee.org/28044
Download Count: 1492

Paper Authors

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Andrew Jackson Purdue Polytechnic Institute orcid.org/0000-0003-2882-3052

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Andrew Jackson is currently pursuing a PhD in Technology through Purdue's Polytechnic Institute, with an emphasis on Engineering and Technology Teacher Education. His research interests are engineering self-efficacy, motivation, and decision making. Andrew is the recipient of a 2015 Ross Fellowship from Purdue University and has been recognized as a 21st Century Fellow by the International Technology and Engineering Educators Association. He completed his Master of Science in Technology Leadership and Innovation at Purdue University with a thesis investigating middle school engineering self-efficacy beliefs. He previously taught middle school and undergraduate technology courses, accompanying both experiences with classroom research to improve practice.

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Nathan Mentzer Purdue University, West Lafayette (College of Engineering)

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Nathan Mentzer is an assistant professor in the College of Technology with a joint appointment in the College of Education at Purdue University. Hired as a part of the strategic P12 STEM initiative, he prepares Engineering/Technology candidates for teacher licensure. Dr. Mentzer’s educational efforts in pedagogical content knowledge are guided by a research theme centered in student learning of engineering design thinking on the secondary level. Nathan was a former middle and high school technology educator in Montana prior to pursuing a doctoral degree. He was a National Center for Engineering and Technology Education (NCETE) Fellow at Utah State University while pursuing a Ph.D. in Curriculum and Instruction. After graduation he completed a one year appointment with the Center as a postdoctoral researcher.

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Abstract

Arguably, the purpose of quantitative analysis is to explain truth. This can be done through descriptive analysis, inferences, hypothesis testing, comparing groups to a model of some kind or through goodness-of-fit indices. Using methods such as analysis of variance (ANOVA), t tests, or tests of invariance, patterns or groupings are explicated by the researcher and are known a priori. However, cluster analysis, a method for identifying those groupings which are close together solely from the data provided, may prove useful in augmenting instructor and stakeholder understanding of student characteristics and choices. In order to situate a discussion of cluster analysis within Engineering Education, we surveyed the use of “cluster analysis” within 24 volumes of the JEE available online. Of 136 search results, only 5 reported using the statistical technique as opposed to using the term ambiguously. We characterize these studies following a framework of cluster analysis, and conclude that a methodological introduction and practical example of cluster analysis would provide utility. This paper presents cluster analysis generally, by describing the purpose of the technique and steps in performing cluster analysis. It provides a description of potential researcher choices during cluster analysis including several clustering algorithms, or step-by-step procedures for performing clustering, with the intent of introducing researchers to the technique. We believe that conceptual understanding of the approach will be useful in decisions to apply it. Following the methodological overview, we present an example application for obtaining and validating cluster results in an introductory design course. Example data were obtained from an end-of-semester reflection taken by 937 students enrolled in an introductory design course, to cluster student groups on the quality of their decision-making process; a 15 question instrument on design decision-making was used to facilitate measurement of the group decision process immediately following a design project. Using k-means analysis, a three cluster solution was identified which included high performing, moderate performing, and low performing groups on the decision-making process. External variables of project grade percentage, team conflict, and team satisfaction were each compared to the cluster groups using one-way ANOVA; significant differences were observed for team conflict and team satisfaction, with negative and positive relationships to decision-making, respectively. This specific description of decision-making clusters among beginning design students is informative for 1) understanding student perceptions and effects of decision-making and 2) demonstrating cluster analysis as an analytical technique. Employing cluster analysis does not replace the individual students in our classrooms, but rather, complements our view as instructors by uncovering student groups that are similar on features that we determine are important. The follow-up to these analyses can help our students feel greater personalization in their instruction as we tailor our teaching to their needs.

Citation
Format

Jackson, A., & Mentzer, N. (2017, June), Cluster Analysis in Engineering Education Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28044

TY - CPAPER
AB - Arguably, the purpose of quantitative analysis is to explain truth. This can be done through descriptive analysis, inferences, hypothesis testing, comparing groups to a model of some kind or through goodness-of-fit indices. Using methods such as analysis of variance (ANOVA), t tests, or tests of invariance, patterns or groupings are explicated by the researcher and are known a priori. However, cluster analysis, a method for identifying those groupings which are close together solely from the data provided, may prove useful in augmenting instructor and stakeholder understanding of student characteristics and choices.
In order to situate a discussion of cluster analysis within Engineering Education, we surveyed the use of “cluster analysis” within 24 volumes of the JEE available online. Of 136 search results, only 5 reported using the statistical technique as opposed to using the term ambiguously. We characterize these studies following a framework of cluster analysis, and conclude that a methodological introduction and practical example of cluster analysis would provide utility.
This paper presents cluster analysis generally, by describing the purpose of the technique and steps in performing cluster analysis. It provides a description of potential researcher choices during cluster analysis including several clustering algorithms, or step-by-step procedures for performing clustering, with the intent of introducing researchers to the technique. We believe that conceptual understanding of the approach will be useful in decisions to apply it.
Following the methodological overview, we present an example application for obtaining and validating cluster results in an introductory design course. Example data were obtained from an end-of-semester reflection taken by 937 students enrolled in an introductory design course, to cluster student groups on the quality of their decision-making process; a 15 question instrument on design decision-making was used to facilitate measurement of the group decision process immediately following a design project. Using k-means analysis, a three cluster solution was identified which included high performing, moderate performing, and low performing groups on the decision-making process. External variables of project grade percentage, team conflict, and team satisfaction were each compared to the cluster groups using one-way ANOVA; significant differences were observed for team conflict and team satisfaction, with negative and positive relationships to decision-making, respectively.
This specific description of decision-making clusters among beginning design students is informative for 1) understanding student perceptions and effects of decision-making and 2) demonstrating cluster analysis as an analytical technique. Employing cluster analysis does not replace the individual students in our classrooms, but rather, complements our view as instructors by uncovering student groups that are similar on features that we determine are important. The follow-up to these analyses can help our students feel greater personalization in their instruction as we tailor our teaching to their needs.
AU - Andrew Jackson
AU - Nathan Mentzer
CY - Columbus, Ohio
DA - 2017/06/24
PB - ASEE Conferences
TI - Cluster Analysis in Engineering Education
UR - https://peer.asee.org/28044
DO - 10.18260/1-2--28044
ER -