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Considerations in the development of a follow-up exploratory quantitative design for student's motivation regarding to work industry-related activities in higher engineering education

Conference Paper

Considerations in the development of a follow-up exploratory quantitative design for student's motivation regarding to work industry-related activities in higher engineering education

Abstract

This Work in Progress paper describes considerations relative to the development of a follow-up exploratory quantitative design for examining student motivation in higher engineering education. The intent of the current work is to build on the outcomes of a previous qualitative study exploring the perceptions of students with regard to work industry-related activities included as part of their formal study experience in Swedish university settings. In the follow-up study design discussed in this paper we focus on a quantitative approach to assessing the impact of such experiences on student motivation. Findings from our previous study indicate both that how these different work industry-related activities are conducted and how the different relationships that are present can effect students’ motivation for learning in tertiary engineering education. However, while the earlier study provides understanding of which scenarios can affect student motivation, there is a need to consider relative effect sizes. To address this issue in this paper, we present considerations for a survey design and discuss the determination of population and sample size and study variables for a preliminary survey instrument. We also propose methods with which to establish validity and reliability, as well as presenting a data analysis plan. At this juncture, the development of a follow-up exploratory quantitative study will contribute to a better understanding of students’ perceptions about work industry-related activities which is currently a prime concern in higher engineering education, providing guidelines for a more critical planning of these activities in the future.
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Considerations in the development of a follow-up
exploratory quantitative design for student's
motivation regarding to work industry-related
activities in higher engineering education
Panagiotis Pantzos
Dept. of Learning in
Engineering Sciences
KTH Royal Intitute of
Technology
Stockholm, Sweden
pantzos@kth.se
Lena Gumaelius
Dept. of Learning in
Engineering Sciences
KTH Royal Intitute of
Technology
Stockholm, Sweden
lenagu@kth.se
Jeffrey Buckley
Dept. of Learning in
Engineering Sciences
KTH Royal Institute of
Technology
Stockholm, Sweden
Faculty of Engineering &
Informatics
Athlone Institute of Technology
Co. Westmeath, Ireland
jbuckley@kth.se
Arnold Pears
Dept. of Learning in
Engineering Sciences
KTH Royal Intitute of
Technology
Stockholm, Sweden
arnold.pears@ieee.org
Abstract—This Work in Progress paper describes
considerations relative to the development of a follow-up
exploratory quantitative design for examining student
motivation in higher engineering education. The intent of the
current work is to build on the outcomes of a previous
qualitative study exploring the perceptions of students with
regard to work industry-related activities included as part of
their formal study experience in Swedish university settings. In
the follow-up study design discussed in this paper we focus on a
quantitative approach to assessing the impact of such
experiences on student motivation. Findings from our previous
study indicate both that how these different work industry-
related activities are conducted and how the different
relationships that are present can effect students’ motivation
for learning in tertiary engineering education. However, while
the earlier study provides understanding of which scenarios
can affect student motivation, there is a need to consider
relative effect sizes. To address this issue in this paper, we
present considerations for a survey design and discuss the
determination of population and sample size and study
variables for a preliminary survey instrument. We also
propose methods with which to establish validity and
reliability, as well as presenting a data analysis plan. At this
juncture, the development of a follow-up exploratory
quantitative study will contribute to a better understanding of
students’ perceptions about work industry-related activities
which is currently a prime concern in higher engineering
education, providing guidelines for a more critical planning of
these activities in the future.
Keywords—follow-up exploratory quantitative design,
engineering education, student motivation
I. I
NTRODUCTION
Qualitative research studies typically lead to inductively
generated theories and themes that emerge from subjective
interpretations and are derived from data that are revealed
within a specific context. From a mixed methods perspective,
the question then is how these findings can be extended with
the supplementary strengths of quantitative methods. In other
words, what it means for a quantitative follow-up study to
enhance the findings from a core qualitative study. The
fundamental principle of a quantitative follow-up study is
that the researcher has goals that go beyond the usual
stopping point for a stand-alone qualitative study. In fact, the
main scope is to increase the credibility of the previous
qualitative findings by presenting that they can be converted
into substantial measures that perform in predictable
practices. Furthermore, through conducting a quantitative
follow-up study, greater generality can be demonstrated [1].
Specifically, an exploratory sequential design is developed in
which firstly, the researcher creates an instrument that builds
on the qualitative findings and is used in the subsequent
quantitative data collection [2], and secondly the exploratory
quantitative follow-up study is conducted [3].
The purpose of this paper is to present the development
of an exploratory follow-up quantitative study design for use
in higher engineering education based on previous qualitative
work [4]. The design is based on the premise that appropriate
instruments are not available in advance. The previous
qualitative research study explored the variability of
students’ perceptions of the nature of work industry-related
activities that they encountered within engineering education
in two large research-intensive Swedish universities, and
given an understanding of which scenarios can affect student
motivation there is a need now to consider relative effect
sizes [5]. At this juncture, the conduction of a follow-up
quantitative study is going to contribute to a better
understanding of students’ perceptions about work industry-
related activities which is a prime concern in higher
engineering research and education today, exploring the
phenomenon in depth through measuring the prevalence of
its dimensions, and contributing to the critical planning of
these activities in the future. The findings from this previous
study provided insights into how different work industry-
related activities, and different relationships between actors
involved in those activities, can affect students’ motivation
for studying and continued learning in higher engineering
education.
II. P
REVIOUS QUALITATIVE STUDY FINDINGS
The previous study explored what impact industry-related
activities have on students’ motivation when studying on
different engineering programmes. Based on this, the
following research questions were posed: a) what industry-
related activities did the sample of engineering students
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engage with during their studies, and b) how did the students
perceive these industry-related activities to affect their
motivation to study and learning. An explorative, qualitative
strategy was employed. Semi-structured interviews were
conducted in which questions and sub-questions were asked
representing thematic categories so that the research
questions were addressed. The sample of the study included
18 students of both Swedish and international nationalities
who were studying on different undergraduate and masters’
engineering programmes at two large, research intensive
Swedish universities. Subsequently, an inductive analysis
was carried out on the transcribed data using the NVivo
software. The inductive thematic analysis process that was
applied derived several key concepts. These themes are
considered to be crucial in determining students’
understandings on how the students perceived these industry-
related activities to affect their motivation to study and for
learning. The findings of the previous qualitative study are
illustrated in Table I. The new instrument, which will be
considered in this paper builds on these findings.
TABLE I. Q
UALITATIVE
F
INDINGS OF
P
REVIOUS
S
TUDY
-M
OTIVATION
F
ACTORS OF
W
ORK
-I
NDUSTRY
R
ELATED
A
CTIVIT IES
Motivation factors of work-industry related activities
Internships Industry-tours Thesis Job-student fair Guests’ lecturers Summer school Lunch seminars
Developing
interpersonal skills, and
fostering computing
knowledge
Future professional
orientation identity as
engineers
Inspirational role model
Practice oriented
knowledge
Relevance to university
programme studies
Lack of empirical
research collaboration
Irrelevance to students’
interest areas
Delimited connection
among students,
doctoral students, and
company’s R&D
Marketization of higher
education
Banking co ncept of
education
III. S
TUDY DESIGN
A. Research Design
In this study a non-experimental research design is
proposed. More specifically, this paper develops a
correlational research survey design. The aim of a
correlational study is to explore and measure relationships
between two or more variables. Correlational research in
education investigates abilities, conditions, or traits that co-
relate, or covary, with each other [6]. A correlational study
tries to examine whether and to what degree a statistical
relationship exists between two or more variables. Such a
study is commonly used to describe or measure prevailing
conditions or something that happened in the past [7]. In fact,
the proposed study design will involve measuring if there is a
positive or negative relationship – and if so, how strong –
between the students’ experiences on different kind of work
industry-related activities and their motivation for learning
and studying in higher engineering education. It is important
to state that, at the time the researcher would collect data on
these variables (i.e., “student beliefs on work industry-related
activities” and “student motivation”), they have already
occurred. In addition, it is ordinarily the case in a
correlational study that the variables measured appear
naturally. In the present study, students would “naturally”
participate on several work industry-related activities in
engineering education and they would “naturally” be affected
in regards to their motivation for learning and studying.
Therefore, when a correlational research design is used, there
is no experimental manipulation of any of the conditions
being measured in the study.
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B. Identification of the topic/problem to be studied
As a correlational study is designed with an aim to
measure relationships between variables and/or to test
hypotheses about predictions, the variables should be
selected in such a way that some logical rationale exists [8],
[9]. In this case, the follow-up quantitative study examines
the following research question: What is the relationship
between different work industry-related activities and
students’ motivation factors of work-industry related
activities?
C. Identification and Selection of participants
The population for the follow-up quantitative study will
be both undergraduate students and masters’ students who
are pursuing five-year engineering programmes in Sweden.
In Sweden, a master’s program consists of a two-year
program, the final two years of a five-year programme,
containing both coursework and a Master’s research thesis
during the final semester. The participating students should
meet the criteria of having encountered work industry-related
activities during their study time. Both Swedish and
international students who take part in the study should be
either at the end of the third year of their undergraduate
engineering studies, i.e., just about to commence studying at
master’s level, or in the first or the second year of their
master’s studies. The reason for selecting these years’ is that
all of the students would have participated in several work
industry-related activities since these are included in their
programmes of study.
In Swedish tertiary engineering education, the offered
study programmes are mainly focusing on both professional
practice and research in its teaching and learning [10]. Based
on the aim of the study and the above participant criteria, the
researcher plans to distribute the survey through university
administrations, primarily to the departments that their
programmes emphasising connections to industry.
Finally, for a correlational study, an appropriate sample
size is needed in order to achieve the necessary (80%)
statistical power [9] and in this case, representation. When
sample size increases, the standard error of the mean gets
reduced. A full power analysis will be conducted once the
quantitative instrument is development so that it can
accurately reflect the included variables.
D. Determination of the mode of data collection
Due to the sequential approach, the instrument
development of this study is be based on the emergent
themes and specific statements acquired from participants in
the previous initial qualitative data collection. In the next
stage, these statements will be used as specific items and the
themes for scales to create a survey instrument that is
grounded in the views of the participants [11]. Next, validity
of items will be examined through pilot work. The most
appropriate method currently is a web-based survey for
collecting data from students who are engaged in higher
education due to the current COVID-19 pandemic and from
a logistical perspective [12]. A Likert-type scale survey
instrument will be distributed to potential respondents via e-
mail [6] and this follow-up study will be developed on
software such as SurveyMonkey or Qualtrics since these are
considered to be the most common and user-friendly tools
for survey creation. The online questionnaire will be
designed according to previous qualitative study themes as
presented before in Table I. Additionally, the questions will
be developed into the following main conceptual categories:
how do students’ work industry-related activities
effect/impact on students’ motivation to studying and
learning in higher engineering education; student motivation
meaning; student’s perceptions during their studies in higher
engineering education; motivational factors for continuing
studying; motivational factors for learning (while their
studies). Finally, a pilot test on a small cross-sectional
population sample will be run to specify if any revisions
should be made before actual data collection occurs [9].
Through this pilot study the reliability will be ensured and
also it will guarantee the correctness of the instrument, the
language of instruction, and acceptability among the
participants.
E. Collection of Data
Data will be gathered in a manner appropriate to the
variables of interest. Furthermore, it will be collected
through the administration of the web-based survey
instrument. Following this, the collected data will be
analyzed within appropriate statistical software, such as
SPSS or RStudio.
F. Data Analysis
The quantitative data of this exploratory follow-up
quantitative study will be analyzed by examining descriptive
statistics and ordinal logistic regression including odds ratios
as effect sizes [13]. Additionally, according to Bryman
statistical inference, which is “the process of inferring
findings from a probability sample to the population from
which it was selected” [13: 346] will be explored. The
findings of the previous qualitative study are now going to be
measured by means of ordinal scales due to the nature of the
variables. When it is necessary to control feasible
confounding factors or when several factors need to be taken
into consideration, multivariate analyses for ordinal data
such as multinomial logistic regression will be conducted.
Finally, it is important to mention that effect sizes will also
be tested. According to Durlak (2009), the effect sizes
statistics “provide information about the magnitude and
direction of the difference between two groups or the
relationship between two variables” [19: 917]. Specifically,
an effect size can be characterised such as a difference
between means, a correlation, a percentage, odds ratio or any
other meaningfully quantified difference or association [16].
G. Answering Research Questions and Drawing
Conclusions
The results of the data analyses should allow the guiding
research questions to be answered, or the hypotheses to be
addressed, for the study. Inferences can be drawn such as to
what extent the relationship between the variables of interest
within the population occurs and appropriate associational,
but not causal, conclusions about the study can be asserted.
More specifically, the main research question of what is the
relationship between different work industry-related
activities and students’ motivation factors of work-industry
related activities will be answered through sub-questions and
objectives of the study, for instance to what extent are
industry tours, career fairs and guest lectures associated
misaligned with students’ interest areas?
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IV. V
ALIDITY AND RELIABILITY OF THE RESEARCH
INSTRUMENT
Validity gives meaning to how well the collected data
applies to the actual area of exploration [17]. According to
Field, validity primarily concerns the ability to “measure
what is intended to be measured” [18]. On the other hand,
reliability corresponds to the extent to which a measurement
of a phenomenon gives consist and stable results [19].
Additionally, reliability is concerned with repeatability. For
instance, a test or scale is considered to be reliable if repeated
measurements made by it under steady conditions will
provide the same result [20]. In order for the consistency of
all parts of a measuring instrument to be determined, testing
for reliability is required [21]. A scale is supposed to have
high internal consistency if the items of a scale “hang
together” and measure the same form [21], [22]. The
Cronbach’s Alpha coefficient will be used as a measure of
internal consistency since Likert-type scales will be made
use of within this study [22], [23]. Moreover, it is suggested
that reliability should be equal to or above 0.60 for an
exploratory study [24]. Finally, Durlak [15] has suggested
that a comparison of the validity components, which
provides the most well accepted techniques that need to be
conducted for validity and reliability of the research
instrument. Some of these techniques, which are illustrated
below in Table II, will be applied to this follow-up
quantitative study as well.
TABLE II.
V
ALIDITY
&
R
ELIABILITY
C
OMPONENTS OF
I
NSTRUMENT
Comparison of Validities
Validity
Component
Definition Type Technique
Suggested
Construct
Discriminant
validity
The extent that
measures of
different
constructs
diverge or
minimally
correlate with
one another
Mandatory
MTMM;
PCA; CFA;
PLS AVE;
Q-sorting
Construct
Convergent
validity
The extent that
different
measures of the
same construct
converge or
strongly
correlate with
one another
Mandatory
MTMM;
PCA; CFA;
Q- sorting
Criterion
Predictive
Validity
The extent that
a measure
predicts another
measure
Mandatory
Regression
Analysis,
Discriminant
Analysis
Criterion
Concurrent
Validity
The extent that
a measure
simultaneously
relates to
another measure
that it is
supposed to
relate
Mandatory
Correlation
Analysis
Criterion
Postdictive
Validity
The extent that
a measure is
related to the
scores on
another, already
established in
past
Mandatory
Correlation
Analysis
Reliability
Internal
The extent to
which a
Mandatory
Cronbach’s a;
correlations;
consistency
measurement of
a phenomenon
provides stable
and consist
result
SEM
reliability
coefficients
V. E
THICAL CONSIDERATIONS WHEN CONDUCTING RESEARCH
Society’s expectation of greater accountability when
conducting research brings an increased and broadened level
of attention on its ethical conduct (the actions that are
personal, professional, and during research activity) [25]–
[27]. One of the fundamentals of ethical research is informed
consent [28]. Participants of the study need to be fully
informed of the nature of the research and what (if any)
consequences there could come from their participation at
the outset. The participants will need to sign an explicit,
active, consent form to taking part with the research, in
which understanding their rights to access to their
information and the right to withdraw anytime and at any
point are included [14].
An important concern is the degree to which invasion of
privacy can be accepted, consequently it is critical that the
identity of the participants is anonymous or confidential.
Specifically, assurances must be extended beyond protecting
participants’ names, as well as, the avoidance of using self-
identifying information and statements need to be
considered. In fact, during the whole research process,
anonymity and confidentiality should be provided for
protecting the participants from potential harm. More
specifically, participant anonymity will be respected and the
participant’s identity will be unknown to the researcher in the
follow-up quantitative study, since it is an anonymous
survey. Participant confidentiality was respected in the
previous qualitative study, since the data collected through
the interviews was de-identified and the identity of
informants was kept confidential [14].
VI. C
ONCLUSION
Designing a research methodology and appropriate
choice of methods in advance should be considered as an
important aspect of engineering education research. With
pre-registration, the researcher specifies their hypotheses and
analysis plan in advance of data collection [29]. Research
context, research questions, and the researcher capability
must always be considered. Specifically, this paper describes
a number of considerations underpinning the development of
a follow-up exploratory quantitative study in a
comprehensive way. This development connects the previous
qualitative phase to the subsequent quantitative strand of the
study. The follow-up study will build on the primal findings
of the qualitative phase by developing an instrument,
identifying variables, or referring propositions for
investigating based on an emergent theoretical framework.
Next, the study will implement the quantitative strand to test
the salient variables using the developed instrument with a
new sample of participants in an ethical manner. What is
critical is that the research methodology and methods align
with the aim of the research and the nature of the research
questions. Finally, the results of the study will be interpreted
with consideration for what ways and to what extent they can
be generalized or expand on the primary qualitative findings
and with an avoidance of external validity threats.
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... However, the field visits are usually to regional industries and infrequently to national or international industries, therefore limited to specific industrial niches and often with a lack of edge technology, culminating in a generalized or irrelevant visit to the topics of the course. The foregoing does not favor the theory-practice relationship and does not help the development of transversal and disciplinary skills that organizations seek in students, because local or regional outlets do not help develop a global vision or offer sufficient knowledge to develop proposals of innovative and creative solutions [4]. ...
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This full research paper aims to investigate the nature of industry-related activities engineering students encounter at a Swedish university, as well as the impact these activities have on their motivation to study engineering. Over the last decade, many studies have been conducted concerning university-industry engagement which chart the landscape of activities, educational approaches, and challenges that students face when involved in industry-related activities. Despite the existing close collaboration between Swedish engineering universities and industry, it seems that not only the feedback from the industry to universities is missing, but also students' perceptions of their industry experience and their needs are not taken adequately into consideration by the other two actors. As a consequence, there is a gap among the above three actors preventing the advancement of engineering education in terms of industrial interventions. Furthermore, there is a lack of research about students' perceptions of university-industry engagement activities. This study adopts a qualitative and exploratory research perspective, intending to gain a deep understanding of students' perceptions of industry-related activities which were integrated into their education. Semi-structured interviews were conducted with nine master's students studying on five-year long engineering programmes in a large research-intensive Swedish university. An inductive thematic analysis was employed, and social cognitive theory was considered as an interpretive tool through which to explore student motivation. The interviews indicated that the students participated actively in various industry-related activities, such as guest lectures, field-trips, internships, summer schools, and masters' theses in collaboration with industry partners which give context to the findings which highlight how industry-related activities can either positively or negatively affect students' motivation for studying and learning in engineering education.
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Cambridge Core - Management: General Interest - Research Methods in Business Studies - by Pervez Ghauri
Chapter
Much rhetoric around the construct of a work-ready graduate has focused on the technical abilities of students to fulfill the expectations of the future workplace. Efforts have been made to extend from the technical skills (e.g., skills in calculation for engineers) to include soft or behavioral skills (e.g., communication). However, within previous models of understanding of the work-ready graduate there has been little done to explore them as critical moral agents within the workplace. That is, whilst the focus has been on being work-ready, it is argued here that in current and future workplaces it is more important for university graduates to be profession-ready. Our understanding of the profession-ready graduate is characterized by the ability to demonstrate capacities in critical thinking and reflection, and to have an ability to navigate the ethical challenges and shape the organizational culture of the future workplace. This chapter aims to explore a movement of thinking away from simply aspiring to develop work-ready graduates, expanding this understanding to argue for the development of profession-ready graduates. The chapter begins with an exploration of the debates around the characteristics of being work-ready, and through a consideration of two professional elements: professional identity and critical moral agency, argues for a reframing of work-readiness towards professional-readiness. The chapter then considers the role of work-integrated learning (WIL) in being able to support the development of the professionready graduate.