Madhavan, K., & Richey, M., & McPherson, B. (2016, June), Predictive Data Analytic Approaches for Characterizing Design Behaviors in Design-Build-Fly Aerospace and Aeronautical Capstone Design Courses  Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25938

\bibitem{asee_peer_25938}
  Madhavan, K., \& Richey, M., \& McPherson, B. (2016, June), \emph{Predictive Data Analytic Approaches for Characterizing Design Behaviors in Design-Build-Fly Aerospace and Aeronautical Capstone Design Courses}
  Paper presented at 2016 ASEE Annual Conference \& Exposition, New Orleans, Louisiana.
  10.18260/p.25938

Krishna Madhavan, Michael Richey, and Barry McPherson.  "Predictive Data Analytic Approaches for Characterizing Design Behaviors in Design-Build-Fly Aerospace and Aeronautical Capstone Design Courses".  2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana, 2016, June.  ASEE Conferences, 2016.  https://peer.asee.org/25938 Internet.  25 Apr, 2024

\bibitem{asee_peer_25938}
  Krishna Madhavan, Michael Richey, and Barry McPherson.
  "Predictive Data Analytic Approaches for Characterizing Design Behaviors in Design-Build-Fly Aerospace and Aeronautical Capstone Design Courses".
  \emph{2016 ASEE Annual Conference \& Exposition, New Orleans, Louisiana, 2016, June}.
  ASEE Conferences, 2016.
  https://peer.asee.org/25938 Internet.  25 Apr, 2024

@INPROCEEDINGS{asee_peer_25938
author = "Krishna Madhavan, Michael Richey, and Barry McPherson"
title = "Predictive Data Analytic Approaches for Characterizing Design Behaviors in Design-Build-Fly Aerospace and Aeronautical Capstone Design Courses"
booktitle = "2016 ASEE Annual Conference \& Exposition"
year = "2016"
month = "June"
address = "New Orleans, Louisiana"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/25938}
number = {10.18260/p.25938}
}

TY  - CPAPER
AB  - Introduction: 
Predictive data models and interactive visualizations can be highly effective in understanding workload and skills assignment issues within design-build-fly teams in the aerospace industry. Capturing data that is needed to build predictive models in usable forms and then subsequently applying appropriate data mining techniques to derive insights from such data is a significant challenge. The ultimate goal of our work is to understand design behaviors among engineers that can lead to cost reductions and expediting product development in complex engineering environments. The present study is a first step towards this overall vision. In this paper, we characterize how engineering students interact and perform on complex engineering tasks commonly seen in the aerospace industry. We use course clickstreams, social networking and collaborations as the basis for our observations.
 
Context of the study: 
AerosPACE is an engineering education program developed by a large US aerospace company. The primary goal of this program is help students understand the process of designing, building, and flying an unmanned aerial vehicle (UAV) capable of assisting first responders. Multi-disciplinary, multi-university teams consisting of students from 5 US universities undertake this real-world engineering project. Collaboration between students at different universities is a major theme of the project. It is expected that each design will address technical areas of aerodynamics, materials, propulsion, manufacturing, structures, and controls among others. Major milestones include a Mission Concept Review, Preliminary Design Review, Critical Design Review/Production Readiness Review, Flight Readiness Review, and Post Launch Assessment Review in addition to a flight demonstration. The overall theme of the UAV’s mission is to help various first responders protecting citizens in this country and across the world. First responders fulfill various missions, many of which can benefit from the use of small-unmanned aerial vehicles. As part of this project students define specific missions they will design their vehicle for to support first responders.

Methods: 
The challenge for this study begins with instrumenting the design environment effectively. When engaging in the build-design-fly engineering process, students typically have to interact with a number of online learning and design environments (for example, learning management system, design environments for the aircrafts, simulation environments to test the design, and specification documentation capture systems). Developing an architecture needed to address this data pipeline is the first aspect that this paper addresses in significant depth. Secondly, using clickstream data, our analyses contain a mapping over time of students’ interactions with faculty and industrial partners and time series distribution of skills and collaborative messages. Additionally, text mining and web log mining techniques allows researchers to gain deep insights on the major discussion topics. Further exploration based on the analysis of the behavior of the users by clustering them and extracting most important patterns is also enabled by our research. Learner behavioral similarity is computed using a page co-occurrence method. Topics are found within the messages using the Latent Dirichlet Allocation model.

Outcomes: 
This study is implemented in a fully automated framework under R giving access to the analysis via a web application. This application allows researchers to interact with the results permitting executives and decision makers to go deeper into the training data. Our work also lowers significantly the information management barriers in how engineers are trained to participate in production-oriented teams
AU  - Krishna Madhavan
AU  - Michael Richey
AU  - Barry McPherson
CY  - New Orleans, Louisiana
DA  - 2016/06/26
PB  - ASEE Conferences
TI  - Predictive Data Analytic Approaches for Characterizing Design Behaviors in Design-Build-Fly Aerospace and Aeronautical Capstone Design Courses
UR  - https://peer.asee.org/25938
DO  - 10.18260/p.25938
ER  -