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Network Particle Tracking (NPT) and Post Path Analysis for Understanding Student Learning and Retention

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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

Biological and Agricultural Engineering Education Technical Session

Tagged Division

Biological & Agricultural

Page Count

20

Page Numbers

23.925.1 - 23.925.20

Permanent URL

https://peer.asee.org/22310

Download Count

31

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Paper Authors

biography

Ernest W. Tollner PE University of Georgia

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Dr. Ernest W. Tollner is a native of Maysville, Ky. and received his B.S. and M.S. degrees in Agricultural Engineering at the University of Kentucky. He completed his doctorate at Auburn University in 1980. His graduate work was concerned with erosion control, water resource development and animal waste management. This work provided the foundation for extension into composting and bioconversion research. He was recently appointed director of Graduate Studies and Research for the new University of Georgia College of Engineering.

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Qianqian Ma University of Georgia

biography

Caner Kazanci University of Georgia

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Dr. Caner Kazanci is a native of Izmir, Turkey and received his M.S. and Ph.D. degrees in Mathematical Sciences Department from Carnegie Mellon University at Pittsburgh, Pa. His graduate work was on mathematical biology, and was concerned with modeling and analysis of large biochemical pathways. He is currently an associate professor at the University of Georgia, in a joint appointment in Department of Mathematics and Faculty of Engineering. He is the developer of EcoNet, a cloud-based software for ecosystem modeling, simulation and analysis. He and Dr. Tollner developed a new high resolution simulation technique that provides a unique opportunity for analyzing higher order properties of ecological networks.

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Abstract

A Framework for Modeling why students leave engineering E W Tollner, C. Kazanci and Q. Ma The departure of significant numbers of students leaving engineering represents a significant investment in facility and instructor time which fails to yield expected results. We want to model why students leave engineering and develop a framework for “what if” scenario analysis. The modeling approach likens our educational methodology to a variety of ecological networks which lose energy or mass along the way through the network. Compartments in ecological models represent exposure to concepts necessary to create an educational outcome for individuals in a society. The goal is to model how various educational systems and approaches impact learning for individuals.  A collection of 15 ecological networks were analyzed using a particle tracking technique that partitions continuous flow of energy or mass into an ecological network into discrete “particles” and follows them through the system until discharged. The “particles” reflect individuals, and the compartments each represent various curricular concepts deemed important for engineering. The particular network reflects various curricula that might be put in place to prepare a student population. When analyzing many “particles” or students, what was the probability of significant history being acquired, which would presumably answer how well the curricula did in imparting knowledge to the student population.  The particle tracking software then allows one to view the history of compartments in the network that had been visited by individual particles. We analyzed each network under three different assumptions: *Each compartment had an equal impact and thus history was a simple accumulation of compartments visited before exiting the curriculum system; *Compartments were more impactful than others and thus history was an accumulations of visits multiplied by weighting factors; and, *Compartments were of differing impact as above, but a loss factor was applied to the history depending on how long was spent in a given compartment before exiting the system.   To evaluate results, we looked at the probability of knowledge vs knowledge for the population of students under each of the three above assumptions.  The principle finding was that the parameters of an exponential distribution for the 15 models or curricular approaches were very similar for very different curricula under a given assumption evaluated. Each assumption resulted in a different continuum of knowledge in the remaining population. More critically, the modeling approach can allow one to show impacts of changing curricula and learning environments on patterns of student exit.  

Tollner, E. W., & Ma, Q., & Kazanci, C. (2013, June), Network Particle Tracking (NPT) and Post Path Analysis for Understanding Student Learning and Retention Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. https://peer.asee.org/22310

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