Minimizing the Number of Dropouts in University Pedagogy Online
Courses
Samuli Laato, Emilia Lipponen, Heidi Salmento, Henna Vilppu and Mari Murtonen
Department of Education, University of Turku, Turku, Finland
Keywords: Online Education, Distance Learning, Engagement, Retention, University Pedagogy, Staff Development.
Abstract: Students’ engagement and retention in online courses have been found to be in general significantly lower
than in contact teaching. Multiple reasons for this exist, but improving student retention is ubiquitously seen
as a beneficial improvement. We take a look at student engagement in online courses aimed specifically for
university teachers and doctoral students, and use a mixed methods approach to obtain a holistic understanding
of student engagement in our domain. We analyse quantitative data from two cases (n=346 and n=271)
collected from students of three university pedagogy online modules over the course of years 2016-2017. We
identify key moments in our modules where students drop out and, for example, differences in dropout rates
between various demographics (i.e. faculty and whether the student is a university staff member or not). The
main moment where students drop out is found to be in the very beginning of the courses, and the introduction
of a pre- and post-test to the courses improved retention. This study suggests that when all other factors
affecting student engagement are in order, additional focus should be paid to the very beginning of the course
and get as many students to do the first couple tasks as possible in order to reduce the dropout rate.
1 INTRODUCTION
Online courses have become notorious for their high
dropout rates in comparison to contact teaching (Lee
and Choi, 2011; Murhpy and Stewart, 2017). A 2014
study reports most Massively Open Online Courses
(MOOC’s) have a dropout rate higher than 87%
(Onah et al., 2014) or even 90% (Gütl et al., 2014).
The situation is arguably better with Small Private
Online Courses (SPOC’s), but as there is too much
variance in the way SPOC’s are organised, it is
impossible to make an accurate general comparison
between the two. This can be seen in the statistics, as
research in online course engagement and student
retention heavily favors MOOC’s over SPOC’s. For
example, a search on Google Scholar on articles
published in 2017-2018 with the term “SPOC dropout
rates” yields 152 search results, whereas a search on
the same years with “MOOC dropout rates” yields
1430 results. Both types of online courses are still
present in recently published papers of all levels.
Multiple reasons exist why SPOC organisers want
to enhance students’ engagement in their courses. Not
only do more engaged student learn better (Kuh,
2003), but engagement also reduces course dropout
rates and increases retention. Due to the causal
relationship between student’s retention and engagem-
ent, dropout rate can be seen as an indicator of general
student motivation during online courses. Therefore it
is feasible to presume that a MOOC or a SPOC with a
high dropout rate is also not the most engaging and
motivating course for those students who pass it.
In this study we focus on engagement in online
courses, with emphasis on courses aimed for
university employers, researchers and doctoral
students. As a case study we will use data collected
from three SPOC style university pedagogical online
modules organised in the University of Turku
between 2016-2017 (Laato et al., 2018). In our
courses we observed a dropout rate of 55% of
students over 346 course enrollments. We then
introduced a pre and post -test setup in our modules
in order to measure students’ learning, and
unexpectedly recorded an increase in student
retention with the dropout rate falling as low as 34%
with 271 enrollments in autumn 2017. This
observation prompted us to form the hypothesis that
the time consuming “first task” as we named our
pretest, actually increases student retention despite it
creating additional workload for the students.
However, the situation is quite complex and
multilayered. Naturally multiple factors affect student
retention, and a single statistic of the course dropout
Laato, S., Lipponen, E., Salmento, H., Vilppu, H. and Murtonen, M.
Minimizing the Number of Dropouts in University Pedagogy Online Courses.
DOI: 10.5220/0007686005870596
In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 587-596
ISBN: 978-989-758-367-4
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
587

rates was insufficient in creating an understanding of
the overall student engagement in our case courses.
In order to gain a holistic understanding of student
retention in university pedagogy online courses, using
our case study and previous studies as sources, we
utilize a mixed methods approach and propose the
following research questions to be answered in this
study:
1. How well do our online courses take into account
the factors influencing student engagement and
retention that have been identified in previous
studies?
2. When dividing our online module into small
segments, in which parts do most students drop
out?
3. What then makes the specific segments such
which cause students to yield their participation in
our courses?
4. Are there any statistically significant differences
in student retention between:
a) Faculties
b) Doctoral students and University Staff
c) Student age
d) Our three case study courses.
First, we go through prominent previous studies in the
field, and identify the major factors affecting student
retention that the studies bring forth. Second, we
compare our course and platform design to these
factors, in order to see if and how we have taken them
into account. Thirdly, we analyse quantitative data
collected from our case courses between the years
2016-2017 to find answers to the rest of the research
questions. We finalize this study with a discussion on
the current situation of engagement and retention in
university pedagogy online courses and propose ideas
for future studies.
2 BACKGROUND
Online learning provides flexible studying
possibilities that are not time or place dependent.
Thus, it can be regarded suitable for educating adults
that are already in working life. Online learning is
also considered a cost-effective way of organizing
education, as the only fixed costs for holding an
online course after it is finished are maintenance fees.
A popular criticism on online learning has been that
it is unsocial and lacks the social presence of contact
teaching, but for example Costley and Lange (2018)
show that quality collaborative learning situations can
occur online. Already in 2004 Zhang et al. stated that
e-learning can supplement classroom learning, and at
times be more effective than traditional teaching
methods. Since then, online learning studies have
become numerous. The research on online learning
used to focus on young degree students whereas adult
learners received less attention (Ke and Xie 2009)
although the amount of adult students in online
courses was higher (Kahu et al., 2013). However,
recently a broad range of studies on adult learning
have emerged, for example (Broadbent and Pool
2015; Deming et al., 2015; Hoffman, 2018).
In the early retention studies the focus was on
degree studies (Murphy and Stewart 2017, 4). Some
recent studies have focused on long-term engagement
in studies with the timeframe varying from one
semester to whole degree programme (Yang et al.,
2017; Yoo and Huang 2013). Course-specific
engagement has been examined in past few years
mostly in MOOC courses. The length of these courses
vary between 5 to 12 weeks and they are usually open
for everyone without prerequisites (Henderikx et al.,
2017). Short courses and training have received less
attention. MOOC research has, however, produced
great amount of information that is applicable to
online learning generally.
2.1 Engagement in Online Learning
Engagement can be divided into three types:
behavioural, emotional and cognitive engagement
(Henrie et al., 2015). Archambault (2009) carried two
studies using the three above mentioned indices:
behavioural, emotional and cognitive engagement in
order to gain insight on which of these three might
have a causal relationship with students’ high school
dropout rates. The findings were, that at least in the
high school context, only the behavioural engagement
affects students’ retention. More specifically, rule
compliance, interest in school and willingness to
learn were identified as factors that indicate an
increased risk for dropping out. Problems with
emotional and cognitive engagement did not seem to
have an effect, however, this cannot be
straightforwardly transferred to the context of online
courses for adult students.
Student’s possibilities to control his/her studies
are also connected to engagement. Control can be
divided into instruction related control and control of
schedule (Karim and Behrend 2015). The more
students can influence teaching (pace, order, content)
the more they have to focus on off-task aspects and
self-regulation, which might be problematic from
engagement and learning perspective. In contrast,
control of schedule can promote engagement.
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2.2 Adult Learners Engagement in
Online Courses
The knowledge obtained from retention and
engagement studies with younger demographics
might not straightforwardly transfer to adult learners,
therefore it is important to take a look at adult learners
in online courses specifically (Ke and Xie 2009).
Some studies show that for adult learners the
relevance of studies for individual and professional
needs, possibility to acquire skills and satisfaction
with the courses and learning results are central in
promoting continuing studies (Yang et al., 2017).
Additionally, adult learners can utilize their
professional experience in their studies and
respectively apply their learning in their work (Kahu
et al., 2013). In recent MOOC research the focus has
been on motivating factors of the courses (Watted and
Barak, 2018). Watted and Barak (2018) compared the
perceptions of higher education students and other
course participants in a STEM MOOC regarding the
benefits of MOOC course. They discovered that for
students with higher education background studying
is based on personal and education related reasons,
whereas other participants, e.g. academic researchers’
motives were work and career related in addition to
personal reasons. Studies on SPOC courses have
identified a direct correlation between engagement
and performance (Liu et al., 2018).
For adult learners environmental or external
factors, such as family or organizational support or
lack thereof, are significant reasons for quitting
online learning (Park and Choi, 2009). According to
Vayre and Vonthron (2017), of different types of
social support only teacher’s support has a crucial
role in online learners’ engagement in studies.
Nevertheless, they also stress the importance of a
sense of community and presence for engagement.
The sense of community promotes the development
of academic self-efficacy (ibid.). Creating sense of
social presence in online settings depends both on the
interaction between instructor and students and
between students (Shelton et al., 2017). Interaction
with peers does not necessarily exist in an online
course, even though it has been identified as a major
component in improving student retention (Shelton,
et al., 2017; Costley and Lange 2018; Hew et al.,
2016).
2.3 Dropout Rates in Online Courses
High dropout rates in online courses have been paired
with a low level of engagement (Willging, 2009).
SPOC courses generally record significantly lower
dropout rates in comparison (Kaplan and Haenlein,
2016), but the results are hard to objectively
generalize as there is a large variance in the way
SPOC’s are organized. Some, for example, contain
elements of blended learning (Martínez-Muñoz and
Pulido, 2015) and the dropout rate can also be
influenced by SPOC’s often being compulsory to
educational degrees whereas MOOC’s are not. The
dropout rate in MOOC’s has been recorded to be so
staggeringly high, that it has sparked a numerous
amount of research from various angles trying to
discover the reasons behind students dropping out.
Onah et al., (2014) found 8 reasons in their study of
why people drop out of MOOC courses:
1. No intention to complete.
2. Lack of time.
3. Course difficulty and lack of support.
4. Lack of digital skills or learning skills.
5. Bad experiences.
6. Expectations
7. Starting late
8. Peer review
These reasons have all been explored in further detail.
For example, Stracke (2017) argues that the high
dropout rates in MOOC’s are a natural phenomenon
that we should not attempt to fix. Because enrolling
to online courses gives students access to all the study
materials and because the barriers for entry are so
low, many students join MOOC’s with no real
intention to complete them, or to take a look if the
course seems good enough for them to complete at a
later time. This, however, is not the case in our case
study, as the course material in our case study is open
for everyone at all times regardless of enrolment.
In addition to the 8 reasons listed by Onah et al.
(2014), at least the demographic can have an impact
to student retention and motivation. Cochran et al.,
(2014) analyse the effect of student characteristics on
retention and form a model predicting the probability
of a student withdrawing from an online course based
on their prior study record. Hew et al., (2016) takes a
look at why some MOOC courses were rated better
by students than others, and found out that if the
course is built so that most learning is problem-
centred, students have access to a passionate
instructor, the course utilizes active learning methods
and peer interaction and provides helpful course
resources, then it is much more likely to be found
engaging by students. There is much additional
evidence that certain types of tasks and a certain kind
of a course design is effective than the alternatives in
online courses in general (Fournier, 2015). Fournier
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(2015) highlights participant focused and learner
driven processes as the most important factor in
making a MOOC engaging and motivating. These
findings and the motivation to create better online
courses has led to the development of strategies and
frameworks which assist in developing and
implementing an online course in a way that is more
likely to result in high levels of engagement in course
participants.
Fidalgo-Blanco et al., (2016) explored the role of
the course participants profile and the pedagogical
model in attrition from MOOC courses. They
developed a model which combines MOOCs based
on traditional online learning platforms (xMOOC)
and connectivist MOOCs (xMOOC) based on
collaboration and utilization of social media
applications. Their findings were that the model had
stronger impact on course completion rate than
factors related to the learning platform, participants
profile or course theme. In addition, student centred
teaching and collaborative learning were found to
have a positive effect on engagement (Herrmann,
2013; Fidalgo-Blanco et al., 2016). Another example
of an online course design approach is the ELED
framework (Czerkawski, 2016) but as Czekawski et
al., write in their paper: “Student engagement in
online learning environments is a relatively new
problem for instructional designers and requires more
empirical research to advance the knowledge base.”
A previous study by Leeds et al., (2014) show that
many of the attempted and currently used retention
strategies in online courses are on their own
insufficient, or at least the results and effects on
student retention are inconclusive. The call for more
empirical evidence by Czerkawski (2016), is
something this study will answer.
2.4 Case Study: The UNIPS
Environment
Our case study platform is called UNIPS, which is an
acronym from the words University Pedagogical
Support. Since the site launch in autumn 2015 until
spring 2017, the three first courses were completed all
together over 334 times. All courses can be accessed
from the front page https://unips.fi, which is shown in
Figure 1. The courses, or modules as we often refer to
them, are called Lecturing & Expertise, Becoming a
Teacher and How to Plan my Teaching. Each of the
three above mentioned modules consists of an
individual task period and a group study period. In the
individual task period, students are tasked with
studying all the course material, which consist of
videos, scientific articles and small exercises, and
then write an essay of 1000-1500 words on a topic
related to the course. The estimated time required to
complete the first task is 12-14 hours. All students
who return an acceptable essay are then added to the
group study period, where they comment and reflect
on each other’s essays, and embark on discussions.
The teamwork period is moderated by the course
instructor, but the instructor does not participate in the
conversations unless necessary. The time reserved for
the teamwork period is 16 hours, but in reality we
have estimated that students spend no more than 4
hours on average on the discussions.
Figure 1: The frontpage of the UNIPS environment.
In the group work period of the UNIPS modules
the students study collaboratively on Google Drive
where they introduce themselves and attach their
essays for peer feedback and discussion. Sense of
presence affects the way students interact with each
other. According to Meyer (2014) it allows
individuals to speak freely and comfortably in a
discussion, and they are more willing to reveal their
personality. This contributes to increased student
engagement based on previous studies (Herrmann,
2013).
We gathered data on how many students enrolled
to the courses and how many students finally
completed the courses. During pilot testing in 2015
we noticed that adding small and easy tasks had a
positive effect on students’ retention. To test this
further, in autumn 2017 we introduced a pre-course
task called “the first task” to the beginning of the
courses before the individual task, and also a “final
task” to the end of all three courses after the group
study period. We wanted to figure out if this change
had an effect on the numbers on how many of the
enrolled students passed the courses. Our hypothesis
was, that dropout rates were higher in the beginning,
and much lower towards the end of the course. We
suspected that besides students who are initially more
motivated to complete the course, students who
successfully complete tasks during the course are
more engaged.
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3 METHOD
This study uses a mixed method approach in order to
obtain a holistic understanding on adult student
motivation and engagement in online courses. Firstly
we summarize the key factors affecting student
engagement from previous studies, and glance
through how well these are taken into account in our
course design. Due to the fact that even our initial
student dropout rate of 55% was significantly lower
than the over 87% from most popular MOOCS (Onah
et al. 2014), our hypothesis is that the case online
courses should be designed quite well according to
the suggestions from literature.
Next we go through quantitative data collected
from three UNIPS modules Lecturing & Expertise,
Becoming a Teacher and How to Plan my Teaching
over the years 2015-2017 to see how student dropout
rates evolved after adding a pre-and post-test to our
courses. Additionally we take a look at differences
between faculties, student age and if the student is a
doctoral student or a member of university staff.
3.1 Case Study Platform Design
Based on our hypothesis that low student retention
indicates lower engagement and hence lower
motivation, we explore how to improve student
retention, as it is the most clearly observable
quantitative statistic. We conclude from previous
studies the following four factors to focus on:
1. Instructors role (Ma et al. 2015; Goh et al.,
2017)
2. Technical aspects: usability of the platform,
quality of the study materials. (Onah et al., 2014;
Swan, 2001)
3. Perceived relevance of the course (Park and
Choi, 2009)
4. Support the learner gets from peers (Costley and
Lange, 2018, 69; Hew et al., 2016)
Using the information we have on our course design,
derived from the existing UNIPS solution
https://unips.fi and previous work (Laato et al., 2018)
we go through each of the four factors and evaluate
how they are present in the actual course
implementation, and also evaluate if and how they
could be improved upon.
3.2 Quantitative Analysis
For the quantitative data collection we create five
checkpoints between course enrolment and course
passing to figure out the instances where students
drop out. These checkpoints are unevenly scattered
across the course in all our three case modules, and
are situations where students are given a strict
deadline to return a task, otherwise they are marked
as dropouts. The five checkpoints are the following:
1. Students who enroll to the course, but never
complete the first task.
2. Students who complete the first task (pre-course
survey), but never sign in to the course Moodle
page.
3. Students who have signed in to Moodle, but who
never return the individual task.
4. Students who have returned the individual task,
but don’t participate in the first part of the
teamwork period.
5. Students who successfully complete the
teamwork period, but who do not complete the
final task.
Students who successfully manage to go through all
five checkpoints passed the course. Data with the
checkpoints was gathered from two instances in
autumn 2017 and spring 2018.
4 RESULTS
From autumn 2016 until spring 2017 our UNIPS
(previous name UTUPS) modules had a cumulative
dropout rate of 55% across the three modules. These
statistics can be seen in Figure 2. The course clear
rates are significantly better than the reported below
13% pass rate of popular MOOC’s (Onah et al.,
2014). One of the reasons for this is that the courses
are open and visible for everyone to observe, so there
is no need to enrol just to be able to look at the
materials. As for the other reasons, we will now
proceed to presenting our findings on our course
design based on the 4 key factors identified and listed
in the Method-section.
Figure 2: The dropout rates of three UNIPS modules during
the years 2016-2017.
Minimizing the Number of Dropouts in University Pedagogy Online Courses
591

4.1 Course Design Evaluation
(1) The instructors’ role in our modules is always the
same. To accept enrolments, to welcome students to
the course via email, to inform of them how to enter
Moodle, and then use Moodle to communicate
deadlines for each task and to remind students of
approaching deadlines. Hew et al., (2016) stress the
point that the instructor should be passionate about
the course, as the enthusiasm will show through to the
students and encourage them. The enthusiasm,
however, is very difficult to objectively measure or
evaluate. One approach is to measure the frequency
of communication between the instructor and the
students. In our case over the observed period (2016-
2018) the fixed amount of emails sent to students
during the one month course was six without pre-test
and eight with the pre-test. In addition the instructor
contacted students through the Moodle discussion
forums and occasionally reminded students who
failed to meet deadlines that they had been given a
few extra days to complete a task. The instructor also
always replied within a day to all inquiries students
sent regarding the course.
(2) The platform usability and design are discussed
more in depth in our previous work (Laato et al.,
2018). The basic pedagogical principles aimed to
make the user experience as smooth and as engaging
as possible are the following:
• Concise design
• Use of multimedia resources
• Short snippets of information
• Clear categorisation of materials
(3) How the students perceive the relevance of the
course can be measured in multiple stages: the first
impression, during studies and after completing the
course. In our case example the courses were directly
aimed at our university employers and doctoral
students with teaching duties, and also marketed as
such. This probably increased the perceived
relevance.
(4) In our case a teamwork period was included in all
three modules to answer the demand of feeling social
presence during studies. In the realm of higher
education, where students are quite familiar with the
used learning methods, the role of the instructor does
not necessarily have to be a big one in facilitating the
conversations among students.
We can conclude that all the four key factors
identified in previous studies as indicators of a
successful online course are present in our case. In
order to extend our understanding of students’
engagement and motivation, we now continue to the
quantitative analysis of student retention.
4.2 Quantitative Data on Student
Retention
To find out whether the pre- and post-test affect
student retention, we have two data groups. First, we
have data from autumn 2016 until spring 2017
collected from our modules without the pre-and post-
tests. Overall we had 346 students registering to our
courses with 156 students completing them. Figure 3
demonstrates the individual phases where students
forfeited their participation to the courses.
Figure 3: Phases where students withdraw from university
pedagogy online courses.
We observe the clearest spike right after
registration, as from 346 registered students only 213,
roughly 62% came to Moodle. This contradicts our
hypothesis that the most time consuming task
(individual task period) would be the one where
majority of students would leave the course. Instead,
what seems likely in light of this data is that the longer
students participate in the course, the more likely they
are to retain their participation.
Figure 4: Breakdown of student persistence by module in
autumn 1 module of 2016.
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Another phenomenon we wanted to take a look at
was if there is an observable difference in students’
engagement between our three modules. Figure 4
shows the case of autumn 2016 modules. The only
difference in student retention can be observed
between the first two phases: registration and signing
up to Moodle. 37 students signed up for both
Becoming a teacher and Lecturing and Expertise, but
28 and 23 students registered in Moodle respectively.
Adding to this data we have Figure 5 showing student
participation in the three modules from autumn 2017,
which is also our first instance with the pre- and post-
tests present. Based on the data presented in Figure 5,
we can conclude that there is no notable difference in
student retention between our three case modules.
Comparing the graphs Figure 3 and Figure 5 we
see a clear difference in the student dropout pattern.
Instead of a huge spike between registration and
joining Moodle, we now observe a much smoother
curve. This is also seen clearly on the overall course
clear rate, as in our first case the clear rate was only
45%, after the introduction of the pre-and post-tests
the clear rate climbed all the way up to 66%! These
results indicate that the very beginning of the course
is extremely important in order to engage students
and increase overall course retention.
Figure 5: Showing student persistence in the three case
modules in autumn 2017 with the pre-and post-tests
enabled.
Next we take a look at the demographics. Each of
our case course participants is either a university
employee or a doctoral student. As we offer the online
courses to all faculties, we had the unique opportunity
of measuring which faculties inside our university
were the most active in participating in the
pedagogical studies. It turns out as we can see from
Figure 6 that Humanities, Science & Engineering and
Medicine were the most active out of the seven main
faculties in our university. The faculty of Law on the
other hand had the fewest participants to the UNIPS
courses, which can partially be explained by the fact
that if measured by the number of employees, it is
also the smallest out of the seven faculties.
Figure 6: Students who enrolled to the UNIPS courses in
spring1, 2017 sorted by faculty.
Finally we take a look at the role of the students
to see sorted by module. No single module seemed to
be significantly popular over others among any group
of students. We could not either see any notable
differences in engagement or dropout rate based on
whether a student was a staff member or a doctoral
student. The role or status of the students is displayed
in Figure 7.
Figure 7: The current status of students who registered to
the UNIPS modules.
5 DISCUSSION
Perhaps the most interesting part in our results was
the improvement observed in student retention after
introducing pre-and post-tests to our courses. This
finding is in line with Evans et al., (2016, 209) finding
that students are more likely to complete a MOOC
course if they have completed a pre-course survey.
According to Evans et al., early engagement in
courses provides a strong predictor of sustained
engagement that leads to course completion. This
study confirms this observation in the realm of
Minimizing the Number of Dropouts in University Pedagogy Online Courses
593

university pedagogy online courses where students
are all either doctoral students or university staff
members.
Other factors like huge tasks, home faculty or the
status of the student did not have an observable
impact on retention. Course design most likely plays
a big role in general as previous studies suggest
(Fournier, 2015; Czerkawski, 2016), but as our 3 case
courses were constructed according to best practises
found in previous studies and were similar to each
other, no notable differences were found in retention
rates among the three case courses. Yang et al.,
(2013) show that at least in some cases social factors
within a MOOC and outside it affect student retention
rates, but in our case we observed only a few rogue
student quitting in the teamwork period with the
outstanding majority completing the course after
passing the individual task period and the half way
mark.
6 CONCLUSIONS AND FUTURE
WORK
In this study we looked up previous studies to find out
factors influencing student engagement and retention
in online courses. We found four key factors and
compared our case course design to these. We then
analysed student persistence during three online
courses and identified the stages when students
withdraw from the course. We did not examine the
possible extrinsic or intrinsic motivations students
have to continue studying the modules. Instead, the
focus was on online behaviour that can be traced in
the UNIPS platform and Moodle. The main
contribution of this study is that it provides empirical
evidence to support the previously stated theory that
the early stages of an online course are the most
crucial to the overall student engagement (Evans et
al., 2016).
In light of findings from this study, the next step
for us to improve our existing courses is to focus on
the beginning of our modules. How can we welcome
all students in a way they feel motivated and engaged
from the very beginning? What factors are there in the
very beginning of an online course that turn some
students away? In addition we are going to expand
our SPOC style courses to MOOC’s, and offer them
to a much larger audience. This will allow us see if
the findings of this case study are transferable to
outside our context. One final aspect that could be
explored in further research is why students decide to
study online. University pedagogy courses are also
available as synchronous, face-to-face teaching at the
University of Turku. However, online modules that
allow complete distance learning are popular among
students.
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