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. 10.18260/1-2--22310

\bibitem{asee_peer_22310}
  Tollner, E. W., \& Ma, Q., \& Kazanci, C. (2013, June), \emph{Network Particle Tracking (NPT) and Post Path Analysis for Understanding Student Learning and Retention}
  Paper presented at 2013 ASEE Annual Conference \& Exposition, Atlanta, Georgia.
  10.18260/1-2--22310

Ernest W. Tollner PE, Qianqian Ma, and Caner Kazanci.  "Network Particle Tracking (NPT) and Post Path Analysis for Understanding Student Learning and Retention".  2013 ASEE Annual Conference & Exposition, Atlanta, Georgia, 2013, June.  ASEE Conferences, 2013.  https://strategy.asee.org/22310 Internet.  28 May, 2024

\bibitem{asee_peer_22310}
  Ernest W. Tollner PE, Qianqian Ma, and Caner Kazanci.
  "Network Particle Tracking (NPT) and Post Path Analysis for Understanding Student Learning and Retention".
  \emph{2013 ASEE Annual Conference \& Exposition, Atlanta, Georgia, 2013, June}.
  ASEE Conferences, 2013.
  https://strategy.asee.org/22310 Internet.  28 May, 2024

@INPROCEEDINGS{asee_peer_22310
author = "Ernest W. Tollner PE, Qianqian Ma, and Caner Kazanci"
title = "Network Particle Tracking (NPT) and Post Path Analysis for Understanding Student Learning and Retention"
booktitle = "2013 ASEE Annual Conference \& Exposition"
year = "2013"
month = "June"
address = "Atlanta, Georgia"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/22310}
number = {10.18260/1-2--22310}
}

TY  - CPAPER
AB  - 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.  
AU  - Ernest W. Tollner PE
AU  - Qianqian Ma
AU  - Caner Kazanci
CY  - Atlanta, Georgia
DA  - 2013/06/23
PB  - ASEE Conferences
TI  - Network Particle Tracking (NPT) and Post Path Analysis for Understanding Student Learning and Retention
UR  - https://strategy.asee.org/22310
DO  - 10.18260/1-2--22310
ER  -