Carnasciali, M., & Dieckman, E., & Orabi, I., & Daniels, S. (2022, August), Unified approach to teaching uncertainty across a three-course mechanical engineering laboratory sequence  Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40666

\bibitem{asee_peer_40666}
  Carnasciali, M., \& Dieckman, E., \& Orabi, I., \& Daniels, S. (2022, August), \emph{Unified approach to teaching uncertainty across a three-course mechanical engineering laboratory sequence}
  Paper presented at 2022 ASEE Annual Conference \& Exposition, Minneapolis, MN.
  10.18260/1-2--40666

Maria-isabel Carnasciali, Eric Dieckman, Ismail Orabi, and Samuel Daniels.  "Unified approach to teaching uncertainty across a three-course mechanical engineering laboratory sequence".  2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022, August.  ASEE Conferences, 2022.  https://strategy.asee.org/40666 Internet.  04 Jul, 2024

\bibitem{asee_peer_40666}
  Maria-isabel Carnasciali, Eric Dieckman, Ismail Orabi, and Samuel Daniels.
  "Unified approach to teaching uncertainty across a three-course mechanical engineering laboratory sequence".
  \emph{2022 ASEE Annual Conference \& Exposition, Minneapolis, MN, 2022, August}.
  ASEE Conferences, 2022.
  https://strategy.asee.org/40666 Internet.  04 Jul, 2024

@INPROCEEDINGS{asee_peer_40666
author = "Maria-isabel Carnasciali, Eric Dieckman, Ismail Orabi, and Samuel Daniels"
title = "Unified approach to teaching uncertainty across a three-course mechanical engineering laboratory sequence"
booktitle = "2022 ASEE Annual Conference \& Exposition"
year = "2022"
month = "August"
address = "Minneapolis, MN"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/40666}
number = {10.18260/1-2--40666}
}

TY  - CPAPER
AB  - The ability to analyze and make sense of large volumes of experimental data is critical to prepare engineering graduates for the modern workplace. While a number of students take an elective statistics course, students' main exposure to data analysis in our mechanical engineering undergraduate program comes from a three course in-major laboratory sequence. These courses each target different technical content while emphasizing common skills, including writing (technical memos, lab reports, design reports), formal presentations (oral and poster), and statistical analysis techniques to quantify uncertainty in measured data.

The first laboratory (sophomore year) targets instrumentation and measurement techniques and introduces the concept of bias and precision uncertainty. The second laboratory (junior year) focuses on experiments related to the mechanics of materials and structures and introduces the concept of error propagation. The third laboratory (senior year) includes experiments related to thermo-fluids and heat transfer and is the culmination of experimental uncertainty analysis in preparation for students' capstone design projects. All three labs heavily emphasize digital data acquisition so students are able to apply the learned analysis techniques on large amounts of real-world data.

This paper details the framework of the uncertainty analysis across the 3-course sequence. Impacts are examined through data collected from the students in each lab. Observations and lessons learned are being used to inform further changes in content and lab-to-lab knowledge recall.
AU  - Maria-isabel Carnasciali
AU  - Eric Dieckman
AU  - Ismail Orabi
AU  - Samuel Daniels
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Unified approach to teaching uncertainty across a three-course mechanical engineering laboratory sequence
UR  - https://strategy.asee.org/40666
DO  - 10.18260/1-2--40666
ER  -