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A Mixed Methods Analysis of Motivation Factors in Senior Capstone Design Courses

Authors:
Paper ID #27578
A Mixed Methods Analysis of Motivation Factors in Senior Capstone Design
Courses
Elisabeth Kames, Florida Institute of Technology
Elisabeth Kames is a graduate student working on her Ph.D. in Mechanical Engineering at Florida Institute
of Technology. Her research focuses on the impact of motivation on performance and persistence in
mechanical engineering design courses under the guidance of Dr. Beshoy Morkos. She also serves as a
graduate student advisor to senior design teams within the mechanical engineering department. Elisabeth
is a member of ASME, ASEE, Tau Beta Pi Engineering Honor Society and Pi Tau Sigma International
Mechanical Engineering Honor Society.
Devanshi Shah, Florida Institute of Technology
I am a graduate student pursuing M.S. in Mechanical Engineering at Florida Institute of Technology
with specialization in Structures, Solid Mechanics and Materials. I graduated with B.E. in Mechanical
Engineering in India in May 2016. My research is focused on Student’s Motivation in Engineering under
the advisement of Dr. Beshoy Morkos.
Miss McKenzie Carol Clark
Dr. Beshoy Morkos, Florida Institute of Technology
Beshoy Morkos is an associate professor in the Department of Mechanical and Civil Engineering at the
Florida Institute of Technology where he directs the STRIDE Lab (SysTems Research on Intelligent De-
sign and Engineering). His engineering design research focuses on developing computational represen-
tation and reasoning support for managing complex system design. The goal of Dr. Morkos’ research is
to fundamentally reframe our understanding and utilization of system representations and computational
reasoning capabilities to support the development of system models which help engineers and project
planners intelligently make informed decisions at earlier stages of engineering design. On the engineer-
ing education front, Dr. Morkos’ research explores means to integrate innovation and entrepreneurship
in engineering education through entrepreneurially-minded learning, improve persistence in engineering,
address challenges in senior design education, and promote engineering education in international teams
and settings. Dr. Morkos’ research is currently supported by the National Science Foundation (NSF),
Kern Entrepreneurial Engineering Network (KEEN), and NASA JPL. Dr. Morkos received his Ph.D.
from Clemson University in the Clemson Engineering Design and Applications Research (CEDAR) lab
under Dr. Joshua Summers. In 2014, he was awarded the ASME CIE Dissertation of the year award for his
doctoral research. He graduated with his B.S. and M.S in Mechanical Engineering in 2006 and 2008 from
Clemson University and has worked on multiple sponsored projects funded by partners such as NASA,
Michelin, and BMW. His past work experience include working at the BMW Information Technology
Research Center (ITRC) as a Research Associate and Robert Bosch Corporation as a Manufacturing En-
gineer. Dr. Morkos was a postdoctoral researcher in the Department of Engineering & Science Education
at Clemson University performing NSF funded research on engineering student motivation and its ef-
fects on persistence and the use of advanced technology in engineering classroom environments. Dr.
Morkos’ research thrust include: design automation, design representations, computational reasoning,
systems modeling, engineering education, design education, collaborative design, and data/knowledge
management.
c
American Society for Engineering Education, 2019
A Mixed Methods Analysis of Motivation Factors in Senior
Capstone Design Courses
Abstract
This paper presents a continuation of prior research exploring the impact of motivation factors on
performance in cornerstone and capstone design courses. The previous longitudinal study focused
on a single cohort of 32 students, examining their motivation at two instances in time: their
freshman cornerstone design course and their senior capstone design course. The results from the
prior quantitative research revealed that student grades in their freshman cornerstone design course
were impacted by their anxiety levels with significance. The student’s senior capstone design
grades were determined by their intrinsic motivation, and the delta in their grade between their
freshman and senior year was correlated to their freshman year anxiety and their residency.
An interesting finding from the quantitative survey was the student’s anxiety levels did not
decrease significantly between their cornerstone and capstone design course. However, the
student’s capstone design grades were not affected by their anxiety. This indicated that there was
a paradigm shift in which the students no longer allowed their anxiety to dictate their performance
in design courses. This prompted the authors to further explore the impact of motivation on the
student’s performance in their senior capstone design courses. This study focuses on a cohort of
80 students, and uses data from two instances in time: their Fall and Spring senior capstone design
course. The findings from the prior longitudinal study also impelled the authors to implement a
qualitative survey to gain insight into the student’s perspective of their motivation. Both of the
surveys measure five factors of student motivation: cognitive value, intrinsic value, self-regulation,
self-efficacy, and test/presentation anxiety.
This paper presents quantitative and qualitative results to further explore the impact of student
motivation on their performance in senior capstone design courses. The study also examines the
student’s motivation factors with regard to their demographic information. This includes the
student’s gender, age, residency (domestic or international), family income, and the highest degree
attained by parents.
The results of the study indicate that the students’ fall, spring, and change in performance are all
impacted by their intrinsic value, with the students’ spring performance being further exacerbated
by their cognitive value. Their performance in the spring is also found to be closely related to their
residency. Interestingly, the student’s intrinsic value actually dropped, with significance, between
the beginning of the fall and end of the spring semester. The results of the qualitative study indicate
that the students who were confident entering into senior capstone design identify necessary areas
of improvement by the end of the two semester course.
Keywords: Student Motivation, Freshman Cornerstone Design, Senior Capstone Design,
Engineering Education, Engineering Retention
1. Introduction
Engineering curriculum at the university level typically culminates in a senior design capstone
course. The goal of the senior capstone design course is to challenge the students with an example
of a real-world project, preparing them for industry. University curriculum used to focus heavily
on design and design challenges, typical of industry level engineering. Due to increasing system
complexity, engineering curriculums were prompted to add more science and mathematics classes
to help students understand needed tools and methods.1 However, over time this produced students
with a decreasing understanding of the practical applications of engineering and design.1 The
reintroduction of modern day senior capstone design in the 1980’s and 1990’s served to bring the
practical application of technical topics back to university level engineering.2,3 It was recently
identified that corporations also yearned for students entering industry to have a greater
understanding of problem solving, critical thinking, and presentation and communication skills.4
Senior capstone design serves as a transition from compartmentalized learning experienced in
introductory level engineering courses to the design and application desired by students entering
industry. Further, it provides students the opportunity to work on a project where they can both
address both technical requirements and learn how to manage projects.5 Prior research performed
by the authors has determined that motivation plays a significant role on students’ performance in
senior design capstone courses.69 As such, we implement qualitative research methods to assist in
explaining this phenomenon.
Many studies have observed motivation as a predictor of academic performance.1012 Busato, et.
al, identified that motivation and intellectual ability were the two key indicators of academic
success at the university level.12 Pintrich implemented the Motivated Strategies for Learning
Questionnaire (MSLQ) to measure student motivation levels and hypothesized their importance
for academic performance. Intrinsic motivation factors have been closely linked to design thinking
and creativity in designing.13 It is hypothesized that motivation factors impact student performance
in design courses and ultimately their success in senior capstone design.
A previous longitudinal study conducted by the authors examined the impact of student motivation
on performance in cornerstone and capstone design courses for the common group of 32 students
that had maintained the proper, four-year trajectory between freshman and senior year. One
interesting finding was that the students’ anxiety levels did not change between their first and last
years of undergraduate curriculum, however the senior capstone design students mitigate their
anxiety using their cognitive value. This prompted the authors to further explore the motivation
levels the senior capstone design students, including the implementation of a qualitative exit
interview to examine key factors that the students identified.
The goal of this study is to explore the impact of student motivation on academic performance in
senior capstone design courses. The study also observes these variables with respect to the
student’s demographic information, including their gender, age, residency (domestic or
international student), family socialization, (including parents’ highest levels of education and
household income) and academic history through the use of student cumulative grade point
average (GPA) and whether or not the student had transferred into the university.
This research specifically addresses three research questions (RQ):
RQ1: Does a correlation exist between motivational factors and student success during
each individual semester of Senior Capstone Design (fall and spring)?
RQ2: Does a correlation exist between motivational factors and the change in student
success over the course of the two semester senior capstone design course?
RQ3: Can qualitative data collected via a team interview format provide insight into
specific variables impacting student motivation and performance in senior capstone
design?
2. Background
This study utilizes qualitative data to gain insight on the results of a quantitative analysis of
motivation levels and their impact on performance in senior capstone design. The quantitative
study makes use of a modified version of Pintrich’s MSLQ to measure five motivation factors:
cognitive value, intrinsic value, self-regulation, self-efficacy, and presentation anxiety. The
performance metric in the study is measured using the student’s final grade in the course. Both the
student’s motivation and performance are also viewed with respect to the student’s demographic
information, including their gender, age, residency, family socialization, and academic history.
2.1. Senior Capstone Design Courses
Senior capstone design has been regarded as the pinnacle of an undergraduate’s engineering
education. While the essence of senior capstone design has been dated back to the late 18th and
early 19th centuries, these courses began to resurface in the 1970’s. However, their significance in
the curriculum went unrecognized, with approximately 3% of American institutions offering a
formalized design curriculum.14 Modern day capstone courses did not emerge until the early
1990’s.3 Senior capstone design serves to prepare students for industry by bringing practical
application of design back to the university level.2 It is typically the student’s first exposure to
team- and project- based engineering similar to what they will experience as a professional
engineer. Its importance has been emphasized by accreditation bureaus such as the Accreditation
Board for Engineering and Technology, Inc. (ABET).2,15
At Florida Institute of Technology, senior capstone design is a final requirement before graduation
from undergraduate engineering curriculum. The course is a three semester sequence, preparing
students for industry. The first course, Design Methodologies, takes place during the student’s
spring semester of their junior year. This course is intended to equip the students with all of the
skills and considerations necessary to complete their senior capstone design projects. The course
overviews the basics of the design process, the preparation of technical reports and presentations,
and different problem-solving methodologies.16 The students are assigned three mini group
projects to introduce them to project-based course requirements and allow them to employ the
skills they are learning at the time. Design Methodologies culminates with the students being
assigned their senior capstone design projects and teams. The second course in the sequence is
Mechanical Engineering Design 1. This course takes place during the fall semester of the student’s
senior year and is the first of the two semesters in which the students work on their senior projects.
The fall semester focuses on the design and analysis of the chosen solution. The students formulate
their solution, generate hand and CAD drawings of the full system and sub-systems, and perform
calculations and computer-based analyses on their systems to guarantee it meets all designated
requirements for the project. The final course in the sequence, Mechanical Engineering Design 2,
requires the students to complete any supplementary analyses, build and test the prototype of their
system, and manufacture a final solution. The course concludes with a design showcase of all of
the final products.
The projects at Florida Institute of Technology fall into one of three major categories: industry
sponsored, university sponsored, or humanitarian projects. The industry sponsored projects are
provided to the university by an industry sponsor and allow the students to experience “real-world”
engineering. The university sponsored projects are generated by the mechanical engineering
department or the college of engineering. The humanitarian projects are generated based on a need
as designated by the student teams themselves. Some previous examples include a manually
operated water purifying device and a machine to manufacture shoes from old plastic bottles for
third world countries.
Aside from focusing on the design and build of their senior capstone design projects, the students
are also required to submit weekly executive summaries and presentations to their advisory
committee throughout both semesters of their Mechanical Engineering Design course; the
committee is comprised of the course professor, graduate student assistants (GSAs), and industry
or university sponsors.1,7,16,17 The weekly presentations are given to ensure the project health, as
well as to provide the students with a platform to practice their presentation skills in a semi-formal
environment and prepare for their formal presentations. The end of the student’s fall and spring
semesters of senior year culminate in a Preliminary Design Review (PDR) and Critical Design
Review (CDR), respectively. Each of these milestones require the students to submit a technical
report outlining their design and processes, and a comprehensive presentation to their advisory
boards.
2.2. Motivation Factors
Pintrich identified the importance of motivation on academic performance.10,18,19 The MSLQ is a
widely used tool in the academic community to measure student motivation. The students are
required to self-assess their motivation on a seven-point Likert scale, with a value of 1
corresponding to “not true to me at all” and a 7 corresponding to “very true to me”. A value of 4
is accepted as neutral and the other values are a gradient between the aforementioned digits. The
five factors observed in this study are cognitive value, self-regulation, self-efficacy, intrinsic value,
and anxiety.
Cognitive value describes the ability to recognize the competencies and sequence of events
necessary to complete a task and self-regulation is the student’s ability to organize themselves to
do so successfully.18,19 Self-efficacy is the student’s self-confidence in their ability to achieve a
goal.20,21 Intrinsic value is similar to self-efficacy; the difference is that intrinsic value includes the
interest that the student possesses in the task and their perception of the benefit of having a specific
outcome.7,9,19
Anxiety is specifically looked at with regard with presentation anxiety. Recall, this is due to the
fact that the senior capstone design teams are required to give weekly presentations to their
advisory committee. The advisory committee consists of the course professor, graduate student
assistants, and any industry representatives that oversee the project. The students are also required
to present a PDR at the end of the fall semester and a CDR at the end of the spring semester at the
conclusion of the project. If the students are not confident in their presentation skills, these events
have been identified to be a source of anxiety.
2.3. Student Demographics
Studies have suggested that demographics impact the motivation and performance of students. The
authors request the participants of the quantitative study to provide their demographic information
for comparison. The demographics considered by the authors are gender, age, residency (domestic
or international student), family income, and the highest degree obtained by the student’s parents.
The authors also consider the student’s GPA and whether or not the student had transferred into
Florida Institute of Technology to see if these factors impact the student’s motivation.
2.3.1. Student Gender
Even though the statistic reached a ten-year high in 2017, women still only accounted for 21.3%
of all undergraduate engineering degrees.22 The lack of female students in science, engineering,
technology, and math (STEM) fields has been attributed to many different causes, including the
gender stereotype that these fields are masculine fields. Other causes discussed by literature are:
lack of female role models2325, lack of outreach to young girls26, and the self-perception of the
inability to succeed in these fields and low self-efficacy due to their gender27. While these issues
can cause females to avoid entering the sciences altogether, there are also difficulties with the
retention of female students upon entering a STEM field.28 Women are 2.5 times more likely to
leave a STEM field after entering university level study.20,29 Shih, et al. showed that motivation
can shift, causing women to change their identification within the field.30 This change in
motivation can make the female students feel uncomfortable within their field, causing a
“disidentification”29,31,32 as a scientist. The females tend to shift to other, more empathetic fields
where they feel comfortable.
2.3.2. Student Residency
Only 10.1% of all of the engineering degrees awarded in the United States in 2017 were awarded
to students of international descent.22 Depersonalized instruction has been revealed to cause
students to disidentify with their field of study33. This is a threat for international students due to
factors including language barriers and cultural disparities. Studies have identified that language
barriers cause international students to have trouble assimilating to a project based environment,
due to the necessity to communicate as a team. The students may understand formal English as
taught for their proficiency exams, but have trouble communicating in an informal
environment.34,35 This is further exacerbated by the need to make formal presentations to their
advisory committee, causing anxiety for the students and pushing them away from a lead
position.35 International students face cultural disparities, while also being required to integrate
into university level curriculum both academically and socially. Studies show the two concepts to
be interrelated36; therefore if international students have trouble assimilating socially, their
academic performance will be impacted as well. On the contrary, if international students are
having trouble integrating themselves into the academic environment due to depersonalized
instruction, they are more likely to remove themselves from their peers.
Florida Institute of Technology has one of the largest international student populations in the
United States (34% of the student body and 40% of the engineering student population), affording
the ability to observe motivational differences based on the student’s residency. In this study
specifically, the cohort is 60% international; this is outlined further in the explanation of the study.
This allows for a unique analysis of the differences between the domestic and international student
populations, as most studies in the literature focus heavily on the domestic student populations,
with a small sample size of international students.
2.3.3. Family Socialization
Tinto hypothesized and studied the effect of the parent’s education and socioeconomic status, as
well as their expectation of the student, on the student’s motivation and performance.37 One study
of Tinto’s suggested that higher social status produced a higher aptitude on standardized tests,38
whereas the opposite equally applies. This disparity stems from a student’s early education and
propagates through the student’s academic tenure. A study by Gut, Reimann, and Grob confirmed
prior research that children with low socioeconomic status and family expectations tend to perform
poorly compared to students that have parents with higher status and a higher expectation of their
competence.39 At the university level, socioeconomic status impacts the student’s performance on
multiple levels. Students from lower income families tend to consider a cost-benefit tradeoff of
attending university due to the high costs associated with going to college.40 This can also result
in student attrition after entering university and seeing the costs associated with their attendance.
This likelihood of attrition is further exacerbated if the student is ill-performing. Family
expectations affect the student’s outlook on education from an early age, as well. If the student’s
parents attended higher education, the student is more likely to distinguish attending university as
the norm due to their parent’s success.38 Students with families that attend university are also more
likely to receive attention and support regarding their education.41
2.3.4. Previous Student Performance
The study performed also accounted for the student’s previous educational experience through the
use of the student’s cumulative GPA and whether or not the student had transferred into Florida
Institute of Technology or had been there through the duration of their undergraduate tenure. While
multiple studies have suggested that GPA is a flawed determination of the student’s performance
at the university level,4244 others have shown that GPA tends to be consistent across semesters.
45,46 Students that have higher GPA’s are generally expected to maintain a higher GPA, while
students with a lower GPA tend to maintain a lower GPA. Therefore, the authors view the student’s
cumulative GPA with respect to their motivation levels and performance to see if a correlation
exists.
With the rising cost of tuition at the university level, more students are taking courses at lower
level colleges and transferring into university for their intermediate and higher level courses.
Studies have also been conducted to determine the impact of transferring into university, versus
attending university for the standard, four-year trajectory. A study by Dills and Hernandez-Julian
found that students who transfer into university tend to perform poorly in their intermediate
courses compared to students that enter university at the freshman level. While the difference was
found to be small, it was statistically significant.47 Another study by Sinha found that students that
transfer into an undergraduate STEM program take longer to graduate than those that begin their
program and finish their program at the same university.48
3. Research Method
This research observes the impact of student motivation on performance in senior capstone design.
The study is performed using quantitative and qualitative input from the cohort of students. The
quantitative data is collected through the use of a modified version of Pintrich’s MSLQ at two
instances in time: the beginning of the fall semester of the students’ senior year and the end of the
spring semester of the students’ senior year. The qualitative data was collected in the spring
semester of the students’ senior year, after the conclusion of the senior capstone design course.
The qualitative data serves to further explain the students’ performances.
3.1. Study Subjects
The study subjects were all students in senior capstone design during the 2017-2018 academic
year. There were a total of 88 students enrolled in the senior capstone design course. The
demographic information for these students is provided in Table 1. The total population is 87.5%
male and 12.5% female. Approximately 40% of the population are domestic students, while 60%
of the seniors are international students.
Table 1: Senior Demographic Information
Domestic
International
Total
Males
28
49
77
Females
7
4
11
Total
35
53
88
In order to draw direct comparison, only the students who completed both MSLQ surveys and the
qualitative exit interview were considered. This reduces the student cohort size to 80 students. The
demographic information for the students is shown Table 2. In the normalized cohort of students,
87.5% are male and 12.5% are female. The international population of students is larger than the
domestic population: approximately 59% and 41%, respectively.
Table 2: Cohort Demographic Information
Domestic
International
Total
Males
27
43
70
Females
6
4
10
Total
33
47
80
3.2. Quantitative Analysis
The quantitative data collection was administered at two instances in time: the first MSLQ survey
was disseminated at the beginning of the fall 2017 semester, before the students had begun to work
on their senior capstone design projects; the second MSLQ survey was disseminated at the end of
the spring 2018 semester, after the students had completed their senior design projects. The MSLQ
views five motivation factors: cognitive value, intrinsic value, self-regulation, self-efficacy, and
test/presentation anxiety. Each of these five motivation factors are compared to the student’s
performance in the senior capstone design course, which is measured through the use of the student
grade for the fall and spring semester. This addresses RQ1: Does a correlation exist between
motivational factors and student success during each individual semester of Senior Capstone
Design (fall and spring)? The student’s demographic information is also taken into consideration
to determine if trends exist within demographics or between demographics. The delta in the
motivation factors is also compared to the change in the student’s performance throughout the
course to address RQ2: Does a correlation exist between motivational factors and the change in
student success over the course of the two semester senior capstone design course? The change in
the student’s motivation is compared to the change in the student’s final grade between the fall
and spring semesters of senior capstone design, to determine if a relationship exists. This is again
considered with respect to the student’s demographic information to determine if trends exist
within or across demographic groups.
The quantitative data is analyzed using two statistical methods t-tests and linear regression to
compare subjects and correlate them to relevant variables. The linear regression seeks to determine
if there is correlation between the hypothesized independent variables and the dependent variable
(student performance). The analysis utilizes Akaike’s Information Criterion (AIC) to find the best
fit model since correlations may be multi-level.49 Paired t-tests are also performed between the
student’s fall and spring MSLQ data. Significance is considered to exist at an α < 0.05, however α
< 0.10 is maintained for discussion.
3.3. Qualitative Analysis
The authors implemented a qualitative interview at the end of the senior capstone design sequence
to gain insight into possible motivation factors. This qualitative study was performed at the end of
spring semester 2018, as the students were finishing up their respective senior capstone design
projects. Each of the nine student teams were requested to perform a group exit interview regarding
their experience in senior capstone design. Some of the questions inquired to student groups were
focused and targeted with regard to their motivation factors: “Were you motivated to do well in
this course? If so, what were you motivated by?” Other questions were not as direct, but were
intended to gain insight into the student’s choice of senior design teams and future goals: “Do you
intend on going to industry or continuing your education after graduation? Did senior capstone
design play any role in this?” and “Do you feel more confident in your ability as an engineer having
completed this course?” This interview was implemented to address the secondary goal of the
research, RQ3: Can qualitative data collected via a team interview format provide insight into
specific variables impacting student motivation and performance in senior capstone design to
determine whether the student’s qualitative responses could provide further insight into the
variables affecting motivation and performance in senior capstone design.
Each of the qualitative interviews by the nine senior capstone design teams were transcribed, and
the authors individually extracted the codes from the transcriptions. The result was 69 different
codes, each of which were recurring topics from the exit interviews of the students. A few
examples of these codes include “teamwork”, “potential job opportunity”, “skills”, and “grades”.
A coding tree is presented in Figure 5 in the Appendix. The authors used this coding scheme to
correlate recurring topics to motivation factors and performance in the course. The Cohen’s Kappa
coefficient50 to measure the agreement of the author’s codes was calculated to be κ = 0.99. Any
value of over κ = 0.75 is generally accepted to be excellent51, while some have arbitrarily suggested
that a value of κ = 0.81-1 as perfect agreement for the model52.
4. Results
The authors measured the student’s performance in senior capstone design with respect to five
motivation factors: cognitive value, self-regulation, anxiety (modified to presentation anxiety),
intrinsic value, and self-efficacy. The student’s demographic information was also considered,
including their gender, age, residency, family income, and the highest degree attained by their
parents.
The results of the MSLQ were compared to the student’s performance in senior capstone design.
Recall, the MSLQ is graded on a seven point Likert scale, where a value of 1 indicates “not true
to me” and a value of seven indicates “very true to me”. The performance metric for the study was
the student’s final grade in the fall and spring semesters of the senior capstone design course, as
well as the delta in the student grade. The student’s grades were correlated to a numeric value for
comparison, which is reflective of the GPA calculation at Florida Institute of Technology. The
numerical values for performance are represented as traditional GPA scoring whereby A=4.0,
B=3.0, C=2.0, D=1.0 and F=0.
To supplement the quantitative study results, the authors performed an exit interview with each of
the senior capstone design teams. The students were asked a total of 19 questions, in an open-floor,
interview type format. The students were instructed to be as specific as possible in their answers.
The authors subsequently coded each of the qualitative transcripts to identify recurring factors
contributing to the students’ motivations and performances.
4.1. Motivation in Mechanical Engineering Design 1 (Fall 2017)
A linear regression was performed for each of the students in the cohort to determine which of the
five motivation factors of interest impacted their performance (measured using student’s final
grade in the course). The students’ fall grades were found to be impacted by their intrinsic value,
with significance (p=0.0258). Figure 1 shows the correlation between the student’s self-reported
intrinsic value and performance for the Fall 2017 senior capstone design course. It is important to
note that because many datapoints overlap, the datapoints were removed from the plot since
overlap could not be visualized.
Coefficients
Standard Error
t Stat
P-value
Intercept
1.6009
0.8147
1.965
0.0530
Intrinsic
0.2979
0.1311
2.273
0.0258
Residual standard error: 0.7441
F-statistic: 5.167
Model p-value: 0.02581
Figure 1: Correlation of Fall Performance to Intrinsic Value
It is shown that students with higher intrinsic value earned higher grades in the course. Recall, the
MSLQ surveys were disseminated at the beginning of the senior capstone course, before the
students had begun working on their projects. Therefore, students entering with a higher self-
confidence in their ability and perception of the importance of senior capstone design performed
better than students without. Also, it is important to note that the student’s intrinsic values were
not impacted by their demographic information, including gender or residency, when entering into
senior capstone design.
0
1
2
3
4
1234567
Final Grade
Intrinsic Value
4.2. Motivation in Mechanical Engineering Design 2 (Spring 2018)
An AIC analysis was also performed using the spring senior capstone design final grades and
student’s self-reported motivation factors. Again, intrinsic value was determined to be the primary
determination for the student’s performance. This was further exacerbated by the student’s
cognitive value. Figure 2 shows the correlation between student performance (measured using
course grades) and intrinsic value. It is important to note that because many datapoints overlap,
the datapoints were removed from the plot since overlap could not be visualized.
Coefficients
Standard Error
t Stat
P-value
Intercept
2.6933
1.0047
2.681
0.00901
Cognitive Value
-0.2726
0.1651
-1.651
0.10296
Intrinsic
0.3363
0.1802
1.866
0.06592
Residual standard error: 0.9841
F-statistic: 2.217
Model p-value: 0.1159
Figure 2: Correlation of Spring Performance to Intrinsic and Cognitive Value
While a p-value < 0.05 was preferred, the authors maintained p-value < 0.1 for discussion. As
shown in the figure, the student’s intrinsic value was significant to p-value < 0.1 (p=0.06592),
however the model p-value was greater than 0.1 at a p-value of = 0.1159.
Interestingly, the research identified that the student’s performance was significantly related to the
student’s residency for the spring semester of senior capstone design. This is shown in Figure 3,
below. The domestic students performed significantly higher (p<0.0004) in the spring semester
than the international students.
0
1
2
3
4
1234567
Final Grade
Intrinsic Value
Domestic
International
Course Grade
3.667 ± 0.595
2.894 ± 1.106
Comparison p-value: 0.0004708
Coefficients
Standard Error
t Stat
P-value
Intercept
2.9130
0.1375
21.18
2e-16
Residency
0.7536
0.2128
3.542
0.00068
Residual standard error: 0.9327
F-statistic: 12.54
Model p-value: 0.0006784
Figure 3: Comparison and Correlation of Mean Grades to Residency
4.3. Change in Motivation and Performance
The linear regression model for the change in motivation and performance between the fall and
the spring semesters showed that the change in the students’ grades were impacted by the change
in their intrinsic value. While the model did not produce a p<0.05 for significance as indicated by
the authors, the model value of p<0.1 was maintained for discussion. Figure 4 shows the
relationship between the change in grade from the fall to spring semesters and the change in
intrinsic value. It is important to note that because many datapoints overlap, the datapoints were
removed from the plot since overlap could not be visualized.
The model p-value = 0.07127. The trend line shows students that performed poorly in the spring
semester of senior capstone design saw a decrease in intrinsic value. However, it is peculiar to note
that only students that exhibited an increase in intrinsic value were the students that did not have
a change in their grade between the fall and spring semesters; most students experienced a decrease
in intrinsic value, even if their grades increased between the fall and the spring semesters.
3.67
2.89
0
0.5
1
1.5
2
2.5
3
3.5
4
Final Grade
Domestic
International
Coefficients
Standard Error
t Stat
P-value
Intercept
-0.04722
0.12976
-0.364
0.7170
Intrinsic
0.26717
0.14607
1.829
0.0713
Residual standard error: 0.8148
F-statistic: 3.345
Model p-value: 0.07127
Figure 4: Correlation between Changes in Course Grade to Changes in Intrinsic Value between Fall and
Spring Semesters
4.4. Qualitative Results
The authors implemented a qualitative survey to gain further insight into the student’s motivation
factors and their impact on student performance during senior capstone design. Each senior design
team attended an open-floor exit interview which inquired about items that positively or negatively
affected their senior capstone design experience. The authors identified 69 unique codes that were
realized through the interviews. These codes were generalized into the following themes: selection,
process, and results. The “selection” theme primarily focused on the entrance into senior capstone
design; this included reasoning behind specific project selections and current and future goals for
the students. The “process” theme explored the period of time during senior capstone design. This
included the student’s challenges and motivation for persistence, as well as the senior design
experience as a whole. Finally, the results” theme generally looked at the outcome of senior
capstone design, including any skills and reflections by the students. The three main themes, as
well as their sub-themes are shown below in Table 3. This also shows the occurrence of each of
the sub-themes. The count is the total number of times that factor was mentioned during the exit
interviews with the student teams.
-4
-3
-2
-1
0
1
2
3
-4 -3 -2 -1 0 1 2
Change in Grade
Change in Intrinsic Value
Table 3: Qualitative Factors
Theme
Sub-Theme
Count
Selection
Personal
87
Previous Experience
21
Current Goals
19
Future Goals
24
Challenges
15
Process
People
127
Challenges/Motivations
85
Project Requirements
6
Results
Future
15
Skills
37
Reflections
152
The qualitative analysis determined that personal goals had a major impact on student’s project
selection. While future goals played a role, it was personal thrusts that were referenced when
detailing why one selected the project. When referencing the process of the design course, students
often referenced the personnel involved, specifically their team mates. The team element of the
design experience seemed to have a significant impact on the students’ perception of the design
process. When discussing the results of the project, students reflected on their decisions and
activities often. The reflection is a positive sign as reflection is often a necessary component of
learning. Students often reflected on what they would do differently or decisions they made that
were later found to be instrumental to their progress.
5. Discussion
The impact of motivation on each of the fall and spring semesters of senior capstone design are
presented, as well as the change in motivation and change in grade across the two semesters.
Qualitative interview data was also collected to identify key factors for the students’ performance,
as told by the students.
5.1. Individual Semester Performance and Intrinsic Value
The senior capstone design student’s fall performance was found to be correlated to their intrinsic
value (p<0.05). Likewise, their spring performance was found to be correlated to their intrinsic
value, however the model lacked significance (p=0.1159). The student’s intrinsic value is their
confidence in completing the task at hand and their recognition of its significance for their learning.
The students that recognized the significance of the course tend to outperform the students that do
not.
An interesting finding was that the student’s fall performance was not affected by their
demographic information, however their spring performance was impacted by their residency, with
significance. The domestic students received higher grades in the spring than their international
counterparts. Studies have identified that social integration can be problematic for international
students;36,53 this includes language barriers that may exist, making it difficult for them to
understand colloquial English in informal environments.34,35 This is challenging for the students,
especially in a group environment such as senior capstone design. Likewise, studies have shown
that the international students have a difficult time succeeding in courses requiring the students to
give formal presentations, which is true in senior capstone design.9,36,54 During the qualitative data
collection, international students often cited the nontraditional mode of course presentations
instead of traditional course learning modes (sitting in class or laboratory). Further, international
students expressed concern that their limited English-speaking ability may adversely affect their
team.
It was also found that the student’s intrinsic value decreased overall from the beginning of the fall
semester of senior capstone design to the end of the spring semester of senior capstone design. A
t-test comparison was performed to observe the change in intrinsic value between the fall and the
spring semester of capstone design. As detailed in Table 4, the intrinsic value decreased
significantly from approximately 6.19 to 5.55 to a p-value<0.0001.
Table 4: Intrinsic Value Paired T-Test Results
Intrinsic Value
Fall
Spring
Mean
6.1889
5.5472
Standard Deviation
0.4108
0.4559
Pearson Correlation
0.5309
t Stat
8.9935
p-value
1.0e-13
t Critical
1.9905
In addressing the first research question - does a correlation exist between motivational factors
and student success during each individual semester of Senior Capstone Design - the authors find
that a positive correlation exists in each of the semesters, where an increased intrinsic value
indicates increased performance; this correlation is significant in the fall semester, but insignificant
during the spring. While this relationship is further exacerbated during the spring semester by the
student’s cognitive values, it still does not produce a significant model.
5.2. Changes in Performance and Motivation
The student’s change in motivation levels was observed with respect to the change in their final
grades between the fall and spring semesters of senior capstone design. The authors found that the
change in the student’s intrinsic value was correlated to the change in their grade (p<0.07127).
However, as discussed, the students’ intrinsic values decreased with significance between the fall
and spring semesters of senior capstone design.
In addressing the second research question - does a correlation exist between motivational factors
and the change in student success over the course of the two semester senior capstone design
course - the authors find that the change in intrinsic value between the two semesters is correlated
to the change in final grade of the cohort. While this correlation was found to be significant, many
of the students experienced a decrease in intrinsic value. The only students that experienced an
increase in intrinsic value did not see a change in grade between the fall and the spring semesters.
5.3. Qualitative Interview Feedback
The 69 unique codes identified in the student interviews were segmented into a tree with the three
overarching themes being:
Selection - prior to entering senior capstone design,
Process - during the process of senior design, and
Results - goals and factors at the completion of senior design capstone design.
Selection saw the students most concerned with their personal goals, having an occurrence of 87;
process was concerned with the people sub-themes with an occurrence of 127; results were
influenced by reflections with an occurrence of 152.
Selection- The personal goals identified by the students included the subcategories of:
Desirability of project
Interest in project
Competition project
Industry project
Confidence
Desire to design
Desire to help
Entering into senior capstone design, the students were primarily concerned with their choice of
project. This included their desire and interest in their project choice, including their choice of
participating on a competition or industry sponsored project. The students also identified their
desire to help and design. These results are consistent with the quantitative results from prior
semester; indicating the student’s performance was impacted by their intrinsic motivation. The
students recognized the importance of the task at hand and were confident in their abilities to
complete their project. The students that indicated that project choice was important generally
spoke of the prospective impact of their project. For example, many of the industry teams indicated
that their project choice was based off of the possibility of getting a job with that company post-
graduation. The students were cognizant that their performance in the course and on their project
could successfully result in a career with that company.
Process- The people subcategory included a multitude of subdivisions that were grouped into:
University resources
External resources
Team
The university resources included personnel within the department (the professor, graduate student
assistants), the machine shop instructors, and our student design center’s assistants. External
resources were identified by the industry sponsored teams as their appointed industry
representative. The team category specifically looked at the members of the team and their
skillsets, team dynamics, and communication.
Results- The reflections subcategory of results included the subcategories of:
Good experience/consistent project choice
Learning
Application of previous courses
Graduate school motivation
Success on other teams
Change of project choice
Desire for prerequisites
At the conclusion of senior design, many students indicated that they had a positive experience
and had felt that they had learned many skills throughout the course of senior design. Moreover,
students indicated they would choose the same project again if they were to do it again. Students
recognized the need to apply their previous coursework during to ensure successful project
completion. Some students indicated their desire to attend graduate school due to their experience
in senior design. Also, some students indicated that they were confident that they could have been
successful on any other senior design team having acquired the necessary skills.
On the other hand, some students indicated that they felt that they would have been more successful
on a different senior design team and would change their project choice if they were given the
chance to start senior design over again. A few students indicated that there should be additional
introductory and prerequisite courses required before entering into senior capstone design because
they felt that they were ill-prepared for the professional standards needed to succeed. The authors
hypothesize that responses such as these could be an indication as to why the student’s intrinsic
value decreased over the two semester sequence. The students entered senior capstone design
confident in their ability to complete their design challenge and perform at an industry level. The
experience of senior design allowed for the students to reflect on their strengths and weaknesses,
and identify possible areas of improvement in their skills; this includes technical skills and
competencies as well as professional skills and competencies.
In addressing the third research question - can qualitative data collected via a team interview
format provide insight into specific variables impacting student motivation and performance in
senior capstone design - the authors believe that the qualitative student interviews provide insight
into the quantitative results, such as the decrease in intrinsic value between the two semesters of
senior capstone design.
5.4. Limitations of the Study
The primary limitation of the study was the ability to gather qualitative data from each of the
students in the senior capstone design course. Given the size of the senior design class, it was not
possible to schedule and perform comprehensive individual student interviews. Therefore, the
qualitative exit interviews were administered on a by-team basis. This resulted in nine, thirty
minute interviews with each of the senior design teams. This provides a snapshot of the variables
impacting student motivation and performance. However, some of the students did not get the
chance to participate at all, or did not feel comfortable disclosing this information in front of their
colleagues. To try to mitigate these effects, the authors provided each participant with the list of
interview questions and contact information for the authors; this was intended to allow for the
students to expand upon some of their answers or disclose information in a private setting. While
some students did take advantage of this opportunity, it is not certain that all possible factors were
extracted for review.
The data was only collected at two instances in time: before the students began working on their
senior capstone design projects and at the end of the student’s senior design experience. While the
authors intentionally designed the study this way to prevent survey fatigue, it may be insightful to
include an additional survey between the two semesters of the course to gather data from the
students while they are fully engaged in their projects. It also may be beneficial to collect
qualitative data from the students at the start of their senior capstone design experience. The
qualitative data was only collected once the students had completed their projects, requiring them
to answer some of the questions in a reflective manner.
6. Conclusion
This study examines the impact of motivation factors on student performance in senior capstone
design. The motivation factors observed in the study are cognitive value, self-regulation, anxiety,
intrinsic value, and self-efficacy. Quantitative data collection occurred at two points in time: the
beginning and end of the student’s fall and spring semester of senior capstone design respectively.
Qualitative data was also collected through the use of team-based exit interviews at the conclusion
of the senior design course. The study identifies that the student’s fall, spring, and delta (changes
between fall and spring) correlated to various motivational factors, as measured by the MSLQ.
When considering course performance, grades were also found to be correlated to motivational
factors, indicating that motivation toward the design course may impact how students perform. A
t-test was also performed to examine the change in intrinsic motivation between the two semesters.
It was found that the student’s intrinsic motivation drops, with significance, between the two
semesters. In an attempt to determine what may cause students to be motivated toward the senior
design capstone course, a qualitative data is collected through the form of interviews and a coding
scheme is developed based on the findings.
6.1. Future Work
Suggested future work includes the refinement of the qualitative survey to allow for effective
questioning. While the survey was proven to be robust and provided effective feedback for the
authors with respect to the changes in motivation factors, the initial feedback from the student can
be used to tailor the survey to efficiently gauge student motivation. For example, one recurring
topic in the qualitative interview was the importance of the graduate student assistants for the team.
The GSA’s would provide extrinsic motivation for the students. Extrinsic motivation has been
shown to impact intrinsic motivation.55 Therefore, the graduate student would be a factor affecting
the student’s intrinsic value. The authors can use this feedback to tailor the surveys further toward
the motivations factors being measured in the study. The qualitative surveys will also be coded
and correlated to specific motivation factors.
The authors also intend on implementing a qualitative entrance interview for the students. Rather
than asking the students to consider their experiences in retrospect, the authors see the importance
of inquiring about the student’s expectations of the senior design course in real time, before the
students are exposed to the course. The authors realize that some of the questions that were asked
of the students in retrospect could have produced skewed answers due to the students having
completed senior design. Therefore, the students knew the overall outcome of the course and adjust
their answers accordingly. For example, some of the higher performing teams in senior capstone
design reported less anxiety than the teams that performed poorly.
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Figure 5: Coding Tree of Coding Scheme Developed
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Science instruction has focused on teaching students scientific content knowledge and problem-solving skills. However, even the best content instruction does not guarantee improved learning, as students’ motivation ultimately determines whether or not they will take advantage of the content. The goal of our instruction is to address the “leaky STEM pipeline” problem and retain more students in STEM fields. We designed a struggle-oriented instruction that tells stories about how even the greatest scientists struggled and failed prior to their discoveries. We describe how we have gone about designing this instruction to increase students’ motivation and better prepare them to interact and engage with content knowledge. We first discuss why we took this struggle-oriented approach to instruction by delineating the limitations of content-focused science instruction, especially from a motivational standpoint. Second, we detail how we designed and implemented this instruction in schools, outlining the factors that influenced our decisions under specific situational constraints. Finally, we discuss implications for future designers interested in utilizing this approach to instruction.
Article
We examine how students who transfer credit for an introductory-level course from schools of varying quality perform in a subsequent intermediate-level course, holding constant many student characteristics. We measure college quality by the percent of students admitted, the student faculty ratio, listed tuition, the percentage of professors with Ph.D.s, and SAT scores. Students taking introductory courses at higher quality institutions earn higher grades in their intermediate courses than students transferring from lower quality institutions. A back-of-the-envelope calculation suggests that, for the average student, the benefit from attending a higher quality institution is more than worth the higher tuition. Eastern Economic Journal (2008) 34, 172–189. doi:10.1057/palgrave.eej.9050010
Conference Paper
This paper presents the findings of a study comparing the motivational factors and performance of two mechanical engineering senior capstone design course cohorts: 2014 and 2015 students. The study measures the motivation of students throughout capstone, a non-traditional course, and the impacts motivation had on their course performance (peer evaluations and team grade). The second cohort participated in an intervention plan during a design methodologies course completed the semester prior to senior capstone design. Quantitative data was collected at both the beginning and end of the course; whereas, qualitative data was collected at the end of the course. Our method utilizes an adapted version of the Motivational Student Learning Questionnaire (MSLQ). The survey asks a variety of questions to measure the performance and motivation levels of the student. Performance factors studied are cognitive value and self-regulation. Motivation factors include presentation anxiety, intrinsic value, and self-efficacy. Statistical analysis is performed among the factors, cohort populations, and within the population (male versus females and domestic versus international) to identify if a correlation exists with student course performance. Amongst other findings, the results indicate the student’s experienced positive change due to participation in the intervention plan. Copyright © 2015 by ASME Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal