Using Off-campus Student-designed Experiments to Aid in Student Learning

In an upper level undergraduate elective course in vehicle dynamics, the author has developed some off-campus experimental-based assignments that involve the students (in pairs) designing the experiments and providing their own measuring tools and test vehicle to get results that they can compare with their calculated predictions.The students are free to design their own procedure or do some investigation and use an industry-standard approach.The students typically find that their experimental results vary considerably from their predictions.While this can be due to simple student implementation errors, it is typically a result of more complex issues.This leads to some deep learning (and a little frustration) for the students as they look into why their results, which they have clearly observed, should differ so much from those predicted by standard machine design and dynamics formulae they have previously used without question.Students must dig into and understand the assumptions behind the standard formulas and also the assumptions they made in designing and executing their experiments.(Texts and Internet articles are often misleading on this subject so students also get an appreciation for the nuances of interpreting what someone has written.)To balance the frustration factor associated with the "reality" of the assignment, there is a fun factor in testing using real vehicles (in various states of conforming to the original manufacturers' specifications) that pushes the students further in the assignment than they would go with a typical campus lab experiment.The paper describes two of the experiments providing some sample student approaches with examples of experiment-calculation discrepancies and their likely causes.

Authors

Institutions

Publication Details

Published
2025-04-01
DOI
https://doi.org/10.18260/1-2-1153-52797
Primary Topic
Experimental Learning in Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using Off-campus Student-designed Experiments to Aid in Student Learning

George Sutherland
Experimental Learning in Engineering
article

Using Off-campus Student-designed Experiments to Aid in Student Learning

George Sutherland
article en

Abstract

In an upper level undergraduate elective course in vehicle dynamics, the author has developed some off-campus experimental-based assignments that involve the students (in pairs) designing the experiments and providing their own measuring tools and test vehicle to get results that they can compare with their calculated predictions.The students are free to design their own procedure or do some investigation and use an industry-standard approach.The students typically find that their experimental results vary considerably from their predictions.While this can be due to simple student implementation errors, it is typically a result of more complex issues.This leads to some deep learning (and a little frustration) for the students as they look into why their results, which they have clearly observed, should differ so much from those predicted by standard machine design and dynamics formulae they have previously used without question.Students must dig into and understand the assumptions behind the standard formulas and also the assumptions they made in designing and executing their experiments.(Texts and Internet articles are often misleading on this subject so students also get an appreciation for the nuances of interpreting what someone has written.)To balance the frustration factor associated with the "reality" of the assignment, there is a fun factor in testing using real vehicles (in various states of conforming to the original manufacturers' specifications) that pushes the students further in the assignment than they would go with a typical campus lab experiment.The paper describes two of the experiments providing some sample student approaches with examples of experiment-calculation discrepancies and their likely causes.

Rochester Institute of Technology (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 100%
Experimental Learning in Engineering
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.

Using Off-campus Student-designed Experiments to Aid in Student Learning — George Sutherland · (2025) | TGRS Research Map | TGRS