Project-Based Team Learning:Teaching Systems Engineering When the Data are Sparse

Undergraduate systems engineering courses face several challenges, including the need to provide students with integrative projects which present sufficient depth and complexity while being scoped for execution within a one or two semester course.This paper describes the results of providing students with challenging real-world analysis projects despite the sparse availability of established technical and operating data.Sixteen teams were presented with projects regarding evaluation of advanced technology systems whose technical, operating or cost characteristics were to some degree unknown.Eight teams were tasked with evaluating two different prototypes of high energy lasers designed for military defense against incoming rocket, mortar and artillery attacks.The other teams were tasked with analyzing the tradeoffs involved in either deploying more instances of an expensive but already operational Unmanned Aerial Vehicle system or in proceeding with R&D towards a second, less expensive UAV for reconnaissance in support of ground maneuver companies.Both problems entailed multiple, competing objectives and complex technical, social and operational tradeoffs.The teams used a structured process that combines formal decision analysis with more traditional modeling, simulation and life cycle cost estimation techniques.A second faculty member, a domain expert, served as proxy client decision-maker.Data were gathered from the client as well as from open source literature and through interviews with experienced commanders, soldiers and technology providers.Simulation models were required in order to estimate system performance, value functions were elicited from the decision-maker and rough cost-benefit analysis was applied.Sensitivity analysis was especially important given the sparse and uncertain nature of some of the data used.We describe responses of the students to these more-challenging-than-usual project topics, the degree to which our pedagogical objectives were achieved and success factors in crafting similar project-based learning courses.

Authors

Institutions

Publication Details

Published
2025-04-02
DOI
https://doi.org/10.18260/1-2-1153-53715
Primary Topic
Software Engineering Techniques and Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Project-Based Team Learning:Teaching Systems Engineering When the Data are Sparse

Robin Burk
Software Engineering Techniques and Practices
article

Project-Based Team Learning:Teaching Systems Engineering When the Data are Sparse

Robin Burk
article en

Abstract

Undergraduate systems engineering courses face several challenges, including the need to provide students with integrative projects which present sufficient depth and complexity while being scoped for execution within a one or two semester course.This paper describes the results of providing students with challenging real-world analysis projects despite the sparse availability of established technical and operating data.Sixteen teams were presented with projects regarding evaluation of advanced technology systems whose technical, operating or cost characteristics were to some degree unknown.Eight teams were tasked with evaluating two different prototypes of high energy lasers designed for military defense against incoming rocket, mortar and artillery attacks.The other teams were tasked with analyzing the tradeoffs involved in either deploying more instances of an expensive but already operational Unmanned Aerial Vehicle system or in proceeding with R&D towards a second, less expensive UAV for reconnaissance in support of ground maneuver companies.Both problems entailed multiple, competing objectives and complex technical, social and operational tradeoffs.The teams used a structured process that combines formal decision analysis with more traditional modeling, simulation and life cycle cost estimation techniques.A second faculty member, a domain expert, served as proxy client decision-maker.Data were gathered from the client as well as from open source literature and through interviews with experienced commanders, soldiers and technology providers.Simulation models were required in order to estimate system performance, value functions were elicited from the decision-maker and rough cost-benefit analysis was applied.Sensitivity analysis was especially important given the sparse and uncertain nature of some of the data used.We describe responses of the students to these more-challenging-than-usual project topics, the degree to which our pedagogical objectives were achieved and success factors in crafting similar project-based learning courses.

Carol I National Defence University (RO)
Openalex Percentile: Top 92%
Software Engineering Techniques and Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Project-Based Team Learning:Teaching Systems Engineering When the Data are Sparse — Robin Burk · (2025) | TGRS Research Map | TGRS