Abstract

In this paper, we address the following question: How can instructors leverage assessment instruments used to process information gleaned from design, build, and test courses to simultaneously improve student outcomes and assess student learning well enough to improve the course in the future? A take-away is unstructured text written by students in AME4163: Principles of engineering design to record what they understand by reflecting on authentic and immersive experiences that occur throughout the semester. The immersive experiences include lectures, assignments, reviews, building, testing, and post-analysis for the design of an electro-mechanical system to address a given customer need. In the context of a take-away, a student then writes a learning statement that is a structured sentence written as a triple, i.e., (Experience—Learning—Value). Between 2019 and 2021, we collected about 10,000 take-aways and learning statements from almost 400 students. In this paper, we address the question from the perspective of students’ feelings and use dictionary-based sentiment analysis to evaluate students’ subjective feelings toward what they are learning. Through quantitative results, we get an overview of students’ sentiments, analyze the underlying reasons, and provide evidence-based guidance to instructors on how to improve the delivery of the course in the future. Our focus in this paper is on explaining the method using data from AME4163, which is general and can be extended to other courses.

References

1.
Mistree
,
F.
,
2013
, “
Strategic Design Engineering: A Contemporary Paradigm for Engineering Design Education for the 21st Century?
,”
ASME J. Mech. Des.
,
135
(
9
), p.
090301
.
2.
Autrey
,
J. L.
,
Sieber
,
J.
,
Siddique
,
Z.
, and
Mistree
,
F.
,
2018
, “
Leveraging Self-Assessment to Encourage Learning Through Reflection on Doing
,”
Int. J. Eng. Educ.
,
4
(
2
), pp.
708
722
.
3.
Turns
,
J.
,
Newstetter
,
W.
,
Allen
,
J. K.
, and
Mistree
,
F.
,
1997
, “
The Reflective Learner: Supporting the Writing of Learning Essays That Support the Learning of Design Through Projects
,”
American Society of Engineering Education
,
Milwaukee, WI
,
June 15–18
, Paper No. 223001.
4.
Peng
,
S.
,
Ming
,
Z.
,
Allen
,
J. K.
,
Siddique
,
Z.
, and
Mistree
,
F.
,
2020
, “
Quantification of Students’ Learning Through Reflection of Doing Based on Text Similarity
,”
ASME Design Education Conference
,
Virtual
, Paper No. IDETC 2020-22267.
5.
Sun
,
Y.
,
Peng
,
S.
,
Ball
,
Z.
,
Ming
,
Z.
,
Allen
,
J. K.
, and
Mistree
,
F.
,
2021
, “
Assessment of Student Learning Through Reflection on Doing in Engineering Design
,”
ASME Design Engineering Conference
,
Virtual
, Paper No. DETC 2021-70250.
6.
Munoz
,
D. A.
, and
Tucker
,
C. S.
,
2014
, “
Assessing Students’ Emotional States: An Approach to Identify Lectures That Provide an Enhanced Learning Experience
,”
ASME Design Education Conference
,
Buffalo, NY
,
Aug. 17–20
, Paper No. DETC2014-34782.
7.
Mäntylä
,
M. V.
,
Graziotin
,
D.
, and
Kuutila
,
M.
,
2018
, “
The Evolution of Sentiment Analysis—A Review of Research Topics, Venues, and Top Cited Papers
,”
Comput. Sci. Rev.
,
27
(
4
), pp.
16
32
.
8.
Anderson
,
H. M.
,
Moore
,
D. L.
,
Anaya
,
G.
, and
Bird
,
E.
,
2005
, “
Student Learning Outcomes Assessment: A Component of Program Assessment
,”
Am. J. Pharm. Educ.
,
69
(
2
), pp.
256
268
.
9.
Goss
,
H.
,
2022
, “
Student Learning Outcomes Assessment in Higher Education and in Academic Libraries: A Review of the Literature
,”
J. Acad. Librariansh.
,
48
(
2
), p.
102485
.
10.
Hensel
,
E.
, and
Ghosh
,
A.
,
2009
, “
A Case Study on Course-Based Outcomes Assessment to Enhance Student Learning and Course Delivery in the Engineering Sciences Core Curriculum
,”
Proceedings ASME 2009 International Mechanical Engineering Congress and Exposition
,
Lake Buena Vista, FL
,
Nov. 13–19
, pp.
43
50
.
11.
Rouser
,
K. P.
,
2017
, “
Oral Assessments of Student Learning in Undergraduate Aerospace Propulsion and Power Courses
,”
ASME J. Eng. Gas Turbines Power
,
139
(
12
), p.
124701
.
12.
Xiao
,
A.
,
Gailani
,
G.
, and
Zhang
,
S.
,
2018
, “
Assessing the Educational Effectiveness of a System Engineering Software in Capstone Design Projects
,”
ASME International Mechanical Engineering Congress and Exposition
,
PA
,
Nov. 9–15
, Paper No. IMECE2018-87640.
13.
Bracken
,
J.
,
Glavin
,
F. X.
,
Henderson
,
D.
,
Jablokow
,
K.
,
Sonalkar
,
N.
, and
Erdman
,
A. M.
,
2019
, “
Can Process Metrics Predict Product Success?: A Pilot Study of Student Design Teams
,”
ASME Design Education Conference
,
Anaheim, CA
,
Aug. 18–21
, Paper No. DETC2019-97704.
14.
Keefe
,
M.
,
Glancey
,
J.
, and
Cloud
,
N.
,
2007
, “
Assessing Student Team Performance in Industry Sponsored Design Projects
,”
ASME J. Mech. Des.
,
129
(
7
), pp.
692
700
.
15.
Bailey
,
R.
,
2006
, “
Effects of Industrial Experience and Coursework During Sophomore and Junior Years on Student Learning of Engineering Design
,”
ASME J. Mech. Des.
,
129
(
7
), pp.
662
667
.
16.
Hans
,
A.
,
Chaudhari
,
A. M.
,
Bilionis
,
I.
, and
Panchal
,
J. H.
,
2020
, “
Quantifying Individuals’ Theory-Based Knowledge Using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach
,”
Proceedings ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering
, Paper No. DETC2020-22613.
17.
Hey
,
J.
,
Pelt
,
A. V.
,
Agogino
,
A.
, and
Beckman
,
S.
,
2007
, “
Self-Reflection: Lessons Learned in a New Product Development Class
,”
ASME J. Mech. Des.
,
129
(
7
), pp.
668
676
.
18.
Evans
,
E.
,
Menold
,
J.
, and
McComb
,
C.
,
2019
, “
Critical Thinking in the Design Classroom: An Analysis of Student Design Reflections
,”
ASME Design Education Conference
,
Anaheim, CA
,
Aug. 18–21
, Paper No. DETC2019-97533.
19.
Shah
,
D.
,
Kames
,
E.
,
Clark
,
M.
, and
Morkos
,
B.
,
2019
, “
Development of a Coding Scheme for Qualitative Analysis of Student Motivation in Senior Capstone Design
,”
ASME Design Education Conference
,
Anaheim, CA
,
Aug. 18–21
, Paper No. DETC2019-98423.
20.
Krishnakumar
,
S.
,
Berdanier
,
C.
,
McComb
,
C.
,
Parkinson
,
M.
, and
Menold
,
J.
,
2020
, “
Comparing Student and Sponsor Perceptions of Interdisciplinary Teams’ Capstone Performance
,”
ASME Design Education Conference
,
Virtual
,
Aug. 17–19
, Paper No. DETC2020-22099.
21.
Riegel
,
C.
,
Starkey
,
E. M.
,
Hunter
,
S. T.
, and
Miller
,
S. R.
,
2019
, “
Do Students Want to Dissect?: A Survey of Student Opinions on the Use of Product Dissection in the Classroom
,”
ASME Design Education Conference
,
Anaheim, CA
,
Aug. 18–21
, Paper No. DETC2019-97569.
22.
Medhat
,
W.
,
Hassan
,
A.
, and
Korashy
,
H.
,
2014
, “
Sentiment Analysis Algorithms and Applications: A Survey
,”
Ain Shams Eng. J.
,
5
(
4
), pp.
1093
1113
.
23.
Hu
,
M.
, and
Liu
,
B.
,
2004
, “
Mining and Summarizing Customer Reviews
,”
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
,
Seattle, WA
,
Aug. 22–25
, pp.
168
177
.
24.
Kim
,
S.-M.
, and
Hovy
,
E.
,
2004
, “
Determining the Sentiment of Opinions
,”
Proceedings COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics
,
Geneva, Switzerland
,
Aug. 23–27
, pp.
1367
1373
.
25.
Taboada
,
M.
,
Brooke
,
J.
,
Tofiloski
,
M.
,
Voll
,
K.
, and
Stede
,
M.
,
2011
, “
Lexicon-Based Methods for Sentiment Analysis
,”
Comput. Linguist.
,
37
(
2
), pp.
267
307
.
26.
Rao
,
Y.
,
Lei
,
J.
,
Wenyin
,
L.
,
Li
,
Q.
, and
Chen
,
M.
,
2014
, “
Building Emotional Dictionary for Sentiment Analysis of Online News
,”
World Wide Web
,
17
(
4
), pp.
723
742
.
27.
Hutto
,
C. J.
, and
Gilbert
,
E.
,
2014
, “
VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text
,”
Int. AAAI Conf. Web Soc. Media
,
8
(
1
), pp.
216
225
.
28.
Jin
,
W.
,
Ho
,
H. H.
, and
Srihari
,
R. K.
,
2009
, “
OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction
,”
Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery
,
Paris, France
,
June 28–July 1
, pp.
1195
1204
.
29.
Song
,
B.
,
Meinzer
,
E.
,
Agrawal
,
A.
, and
McComb
,
C.
,
2020
, “
Topic Modeling and Sentiment Analysis of Social Media Data to Drive Experiential Redesign
,”
ASME Design Automation Conference
,
Virtual,
Aug. 17–19
, Paper No. DETC2020-22567.
30.
Chaplot
,
D. S.
,
Rhim
,
E.
, and
Kim
,
J.
,
2015
, “
Predicting Student Attrition in MOOCs Using Sentiment Analysis and Neural Networks
,”
Proceedings CEUR Workshop
,
Madrid, Spain
,
June 22–26
, pp.
7
12
.
31.
Van Eck
,
N. J.
, and
Waltman
,
L.
,
2007
, “VOS: A New Method for Visualizing Similarities Between Objects,”
Proceedings Advances in Data Analysis
,
R.
Decker
, and
H. J.
Lenz
, eds.,
Springer
,
Berlin/Heidelberg
, pp.
299
306
.
You do not currently have access to this content.