Understanding the Challenges of Introducing Self-driven Blended
Learning in a Restrictive Ecosystem
Step 1 for Change Management: Understanding Student Motivation
Kay Berkling
1
and Armin Zundel
2
1
Cooperative State University Baden-Württemberg, Karlsruhe, Germany
2
Inline Internet Online Dienste GmbH, Karlsruhe, Germany
Keywords: Blended Learning, Problem based Learning, Software Engineering, Education, Ecosystem of Learning,
Self-directed Learning.
Abstract: This paper describes the implementation of a prototype for blended learning in a Software Engineering
course at the Cooperative State University Baden-Württemberg in Karlsruhe. The University has certain
particularities that distinguish it from other Universities because students alternate quarters between study
and work. Thus, students receive a salary during their three years towards earning a Bachelor Degree and
attendance is mandatory. In cohorts, around 30 students spend an average day with at least 5 hours of
frontal lecture in the same classroom. Software Engineering takes up about 5 hours a week of in-class time
in their second year of study and is the first course students have seen with a self-driven, blended learning
format. The paper describes the set-up of the learning environment based on known research results of
motivational factors. Based on an exploratory survey of 59 students, these motivation factors are compared
to students’ motivations and their realizations in traditional and self-driven lecture format. Answers revealed
that change presents a major challenge for most students and identifies the need for explicit habit building,
change management and improved serving of students’ basic needs in a grade-based ecosystem.
1 INTRODUCTION
In this paper, an approach to self-driven blended
learning is adopted with a group of 90 students in
their second year (out of three) during their Bachelor
program at the Cooperative State University Baden-
Württemberg in Karlsruhe, Germany. In this setting,
the academic year is based on a Quarter system.
Students spend alternating quarters studying or
working, earning a salary throughout the year. Their
attendance at University is mandatory and they study
in cohorts of around thirty students. As students are
required to remain within their cohort in order to
graduate, failing a course results in the failure of the
entire Bachelor degree. As a result of this set-up,
students spend more than 5 hours a day and
sometimes up to 25 hours per week in frontal
lectures in a single classroom. 15 minute breaks
mornings and afternoons and a lunch break in the
middle of the day round up the program. Evenings
and weekends are spent with learning the material.
Some full-time lecturers can teach over 20 hours a
week, including courses outside of their immediate
expertise. Other lecturers teach on the side while
working in industry. Both students and lecturers
work under intense time and performance pressures.
Students have never before been responsible for
their own learning beyond what is needed to perform
well on an exam in a traditional setting. Neither in
school nor at the University has self-regulated
learning been explored. However, there are
necessary reasons for changing the learning
environment away from the frontal lecture from
employers’, students’ and University points of view.
From the employer point of view, it is important
to move students from a check-box based approach
to obtaining good grades to a mastery based
approach, which is more aligned with workplace
demands. Whereas in school, handing in something
to be graded on a certain date may count as a
completed task, industry work environment expects
several passes through a piece of work until
perfected. While one might think that behaviour can
be adapted based on environment, employers report
that key reasons for not hiring students include their
lack of transfer skills, lack of critical reflection on
311
Berkling K. and Zundel A..
Understanding the Challenges of Introducing Self-driven Blended Learning in a Restrictive Ecosystem - Step 1 for Change Management: Understanding
Student Motivation.
DOI: 10.5220/0004381403110320
In Proceedings of the 5th International Conference on Computer Supported Education (CSEDU-2013), pages 311-320
ISBN: 978-989-8565-53-2
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
own performance and lack of soft-skills
(Heidenreich, 2011).
From the student point of view, reasons for
changing the format of coursework are threefold:
First, the number of hours spent listening to lectures
can be decreased by letting students work
independently towards pre-defined goals, thereby
becoming more actively involved in the learning
process. Then, with up to 25 weekly hours of frontal
lecture, this format may be perceived as a nice
change of pace. Finally, due to the noticeable
difference in know-how between students and even
between lecturer and student in the case of current
industry standards, there is an advantage to leaving
space to learn from other students in the small-class
setting.
The University’s reasons include knowledge of
research results about positive learning outcomes
when creating a more active and problem based
learning environment (Garrison and Kanuka 2004;
Goel and Sharda, 2004) despite the fear of change
(Hall et al., 2002). Key reasons for change include
the overwhelming variety of high-quality
information sources that are available on the web.
With the most current appearance of MOOCs
(Massively Open Online Courses) from some of the
top Universities in the US, standards for frontal
lectures are set, including written transcripts of what
was said, the ability to rewind and re-listen to any
lecture any time and communicating with an active
global community. Increasingly, excellent
information is available via Youtube and Internet
outside of any systematic courses as students are
relying less and less on books to acquire knowledge
in the field of Information Technology.
The goal of this exploratory study and
corresponding survey is to understand student
motivation in the example of a restrictive
environment, where grades and efficiency are central
to survival. A gap in research on this topic has been
noted the literature (Shea Bidjerano, 2010). Software
Engineering was chosen as the first class to move
towards self-driven learning for three reasons: 1.
many hours are available for a reasonable amount of
material so there is room for slow buy-in, 2. high-
quality information is available online for this topic,
3. large differences in student know-how, ranging
from expert to novice based on their work
experience, 4. the subject is very applied and
matches closely with most students' reality at work.
Students experienced the new set up and were asked
to respond to an initial exploratory survey that
revealed a surprising and much deeper complexity of
the issues involved in this change.
Section 2 will summarize the theoretical foundations
of the didactic set-up for this approach. Section 3
will describe the experience from the teacher point
of view. Section 4 and 5 will evaluate qualitative
and quantitative results from an exploratory survey
taken at the end of the second month out of the 11-
week long course in order to understand how student
motivation corresponds with inbuilt motivators for
the new format. Section 6 will conclude by
discussing mismatches in motivation and proposing
concrete steps to manage change and build habits.
2 THEORETICAL FOUNDATION
The software engineering course was redesigned
around motivators with content and platforms
aligned as shown to be important (Derntl and
Motschnig-Pitrik, 2005). This section discusses the
theoretical foundation behind the motivators, the
content design and platform requirements.
2.1 Motivators
Despite some controversy as to the exact definition
of extrinsic and intrinsic motivators (Rheinberg,
2006), we will distinguish internal drivers such as
autonomy, purpose, and mastery from external
drivers, a number of chosen mechanics from
gamification that have been shown as effective in
real world systems with academic research still
forthcoming in this emerging field.
2.1.1 Intrinsic Motivation
According to positive psychology’s theories about
motivation (Pink, 2010; Deci, 2012; Scott Rigby et
al., 1992; Seligman and Csikszentmihalzi, 2000;
Kearsley, 2000; Gagné and Deci, 2005), humans are
motivated to work on cognitively difficult tasks
when they are granted autonomy, purpose and
mastery. Accordingly, the course was designed to
grant students autonomy by allowing choice of speed
and order for studying six out of nine topics of their
choice (see 2.3) with enough time to obtain mastery.
The purpose was given because the acquired
knowledge would make students more powerful
partners in project work for the coming quarter and
because the material is immediately useful at their
workplace as software engineers in training.
2.1.2 Extrinsic Motivation
Gamification is a controversial topic that has
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become ubiquitous in the business world since 2010
when the term was coined by the gamification
community (Zicherman and Linder, 2010;
McGonigal, 2012). Part of the idea behind
gamification is to understand which mechanics keep
gamers motivated to come back to play and apply
those constructs to non-game environments with the
goal of encouraging similar engagement. Since these
have been shown to work (Lepper et al., 1999;
Charles et al., 2011; Rebitzer and Taylor, 2011),
some typical game mechanics were incorporated
into the classroom as listed in Table 1.
Table 1: Theoretical Motivators / Classroom Realization.
Mechanics Realization
External Motivators
Platform
Aesthetics Gamification platform
Progress Bar
Overview
Poster on wall
Gamification platform
Feedback Moodle online quiz
Leaderboards Email (anonymous)
Points, Levels
Heroes
Gamification platform
Internal Motivators
Classroom
Autonomy
Lecture on demand
Various paths though
content
Personal timeline
Personal learning materials
and interaction
Mastery
Quiz until mastery
Bloom’s Taxonomy
Purpose
2 Semesters
Project based
Basic Needs Teaching to the test
2.2 Content
Content is structured to support intrinsic motivation
of autonomy as defined above for the purpose of this
work. A choice of independent pathways organized
into levels through the material is provided. Each of
three paths consists of three topics of mastery
divided into Bloom’s cognitive levels as described
below (see also Table 1).
2.2.1 Topic Organization into Levels
Topics covered in this Software Engineering course
is structured into three pillars of three topics each:
Software writing (Design Patterns, Metrics,
Testing), Communication (Documentation, Estima-
tion, Reverse Engineering) and Project Management
(Processes, Configuration Management, Lifecycle
Management) and culminates in project based
experience. The current version of the topic
separates the theoretical parts into the first Quarter
and the project part into the second Quarter, with
students spending the intermittent Quarter in their
respective work places. This separation is designed
to give students enough time to learn all aspects of
Software Engineering before applying the collective
know-how in a project. Additionally, tuned into the
subtopics, they are able to inspect how these topics
are treated in their workplace during their practical
phase, thereby integrating industry know-how into
the classroom.
2.2.2 Towards High-level Thinking with
Bloom’s Taxonomy
Bloom's taxonomy classifies educational goals into
a hierarchical system in the cognitive domain and
builds towards higher level thinking skills and has
been used effectively in Computer Science in the
past (Krathwohl, 2002; Thompson et al., 2008). An
example is depicted in Figure 1 for the topic of
Software Testing. At the knowledge level, students
receive a theoretical lecture on demand. Here,
terminology, facts, principles and theories are
presented. For the example of testing the lecture
explains what the different types of software tests
are, when they are performed and what they cover.
Figure 1: Bloom's Taxonomy (Bloom ’56) as applied to
one of the learning areas in Software Engineering.
Understanding the facts is encouraged by asking
students to then implement several unit tests for a
given suggested code or a code of their own choice.
They are then asked to choose a testing framework
for later use in their projects to prepare for the next
semester. For example, how can tests be automated
and tied in with the lifecycle management. Analysis,
Evaluation and Creation of further tests is then left
to the actual project based experience in the next
Quarter/Semester in a larger scale testing
environment.
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2.3 Blended Learning
Learning platform(s) are integrated into the
classroom in order to create a blended learning
environment to match motivators (Bekele, 2010;
Graham, 2006; Mohammad and Job, 2012) with the
known shortcoming of not living up to professional
graphic interfaces that people are used to these days
(Schober and Keller, 2012). Table 1 lists the
connections that were implemented in this case to
align motivators with elements of blended learning
environment.
2.3.1 E-Platforms
Lecture slides and learning objectives for each of the
nine topics to be studied where provided on Moodle
and supplemented with links to external information
and tools. Online quizzes provided a 24x7 platform
for submitting work to be checked manually by the
lecturer to provide more or less immediate and
personal feedback. In addition, a separate
gamification platform apart from Moodle supplied
explicit task lists, levels, points, a progress bar and
an overview over class progress that was also
available in paper form on the wall.
2.3.2 Human Interaction
With 5 hours of in-class time per week and
mandatory attendance, this time is used for
teamwork among students and choosing frontal
lectures on any of the nine topics on demand as the
student progresses through the topics (levels).
Theoretically, the student has the opportunity to pick
up to 9 lectures in any order over the course of 11
weeks duration of the quarter and work through
related problem sets and quizzes with feedback from
instructor with no restriction on collaboration. Only
six topics were required for the final exam that
covers only the “remember” level of Bloom’s
taxonomy but would be facilitated by understanding
the topics more thoroughly after completing all three
of Bloom’s cognitive levels per topic. The next
section will describe the experience from the teacher
point of view.
3 PERCEPTION OF THE
LECTURER
The expectation of the lecturer was a general relief
on the part of the students to be able to work
autonomously with increased time in an
environment of knowledgeable students, where a
lecture could be held on demand in small groups that
allowed more questions and interactions and control
over lecture times. No longer would a student have
to pay attention on demand by the teacher, but vice
versa.
In reality, students took a long time to warm up
to the new system as they did not ask for lectures
during the first weeks but tried to just read the slides.
Students were also unable to schedule their own
pace through the material despite the fact that the
lecturer gave example schedules for various
different pathways through the three pillars. Students
usually did not resubmit quizzes that did not receive
optimal points by instructor through Moodle.
Students sitting in a frontal lecture often seem to
subside in an impassionate “coma” not being able to
take breaks or rewind. In the new mode, I see
students sitting in small groups in front of computers
discussing how to solve the given problems. They
take breaks when they need them, move their post-it
notes to the next level once they have accomplished
a level. They collaborate in various sized groups or
work on their own with a headset listening to music.
They are aware of who is working in a similar area
and after some weeks were able to prepare and
request frontal lectures in groups of 2-10. These
lectures were always given within the three-hour
class-time following their demand. During peak time
in the middle of the quarter, up to three lectures on
different topics where given in one session to
various subgroups. These students came prepared,
asked questions and gave feedback as to the quality
of the lecture, which could then be immediately
improved for the next groups.
As a result, motivation for the lecturer has
increased. While it would be hard to prove
quantitatively, it is apparent in comparison to other
classrooms that the questions were asked without
intimidation and with more background knowledge
and depth. Because of this positive perception on
the part of the lecturer, the student perception
explained in Section 4 came as a surprise.
4 QUANTITATIVE ANALYSIS OF
STUDENT SURVEY
An exploratory survey was conducted to find out
what students expect from a good class and how
they are motivated in order to receive feedback on
the class set up and how well it matches their
motivators. The survey was not mandatory and 59
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students chose to anticipate in anonymous manner
during weeks 8 and 9 out of a total of 11. These
responses form the basis of the reported analysis. To
get a rough idea about how well the course was
received, students were asked to give it a grade. In
Figure 2 below, which plots grading of a student for
an average frontal lecture minus the grading of
Software Engineering on a scale of 1 (best) to 6
(worst), it can be seen that grading for Software
Engineering (open format) is about equal (with mean
around zero) with a tendency toward preferring the
frontal, known style (distribution tends to left side).
After years of school training to learn to the test it
may not be surprising to see that students will have
problems with the new learning environment. Still it
was surprising since above mentioned research had
suggested otherwise.
The following sections will evaluate the student
survey that was designed to elucidate their
expectations of a good lecture and their motivators
with respect to the research based categories of
autonomy, purpose and mastery. The hypothesis
was naively that the proposed learning environment
was much more closely aligned to their motivators.
Figure 2: Grade of Frontal - Free Lecture Style.
4.1 Student Profiles
It is of interest to study how students’ profiles differ
according to how they like each format (frontal vs.
Software Engineering). Based on the grades the
students gave, they were grouped into “Dislike”,
giving a bad grade (1 <= grade < 3) and “Like”,
giving a good grade (3 < grade <= 6) for each
format. The resulting number of students in each
category is shown in Table 2:
Table 2: Subset of Students with Strong Dis/Likes.
# of students
Like
(grade < 3)
Dislike
(grade > 3)
Software Engineering 21 19
Average Frontal Lecture 20 9
4.1.1 Perception of Format
Two hypotheses were that students grow to like the
format after getting used to it and that prior
knowledge would automatically lead to a higher
acceptance of the course format.
As expected, students who like the new format
noted an improvement of the format over time.
However, students who dislike the format
alarmingly worsened their opinion over time
indicating a lack of necessary scaffolding.
As frontal lecture treats every student the same,
whereas the free choices afforded students to move
past topics according to their own needs, students
with prior knowledge are expected to prefer open
style lecture of Software Engineering to average
frontal lecture. Strangely, Figure 3 shows that
opinion on the course format is not a function of
prior knowledge, which as will be seen later, does
not suggest a lack of challenging problems.
Figure 3: Impact of previous know-how on acceptance of
course format and change in opinion.
4.1.2 Students’ Expectation of Lecturer
In order to understand the acceptance of the new
format, the survey also gathered information on
desired characteristics of a lecturer that are
perceived as desirable. Due to his point of view and
experience, the list given in Figure 4 was designed
by a student from the graduating class.
The hypothesis was that a teacher should know
how to teach and have expertise both in theory and
work experience. Yet, students overwhelmingly
answered that it is important to see problems,
solutions and obtain a script (in Germany this is a
transcript of a lecture, which is more concise than a
textbook). This seems to point towards students that
are training to the test.
Looking at students’ responses in terms of their
format preferences, we would expect students who
like the Software Engineering open format to put
less emphasis on slides and script. This hypothesis is
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shown to be mostly supported by the data. However,
mostly there is no difference between the groups.
The data revealed additionally, that students who
dislike frontal lecture were looking for more for
theoretical know-how and practical experience in a
lecturer (which they probably did not get since they
didn’t like the lecture) and to get the necessary
content find both slides and textbooks more
important than other subgroups (see Figure 5).
Figure 4: Number of students who choose specific lecture
characteristic.
A new hypothesis based on this result is that
these students would prefer the free format of
Software Engineering. Figure 6 shows that grades
given by students show a tendency of anti-
correlation between frontal and free style of
teaching.
Figure 5: Students’ opinion based on subgroups. Numbers
are given in % for comparison.
In summary it can probably be said that if the
lecturer is good, interaction is important, otherwise
the lecturer becomes irrelevant and students seek
their information in slides, script and lastly books if
necessary – no matter the format of the course.
4.1.3 Students Motivators
Since one of the key design elements of the new
format builds on motivating students according to
research-based ideas on what motivates humans in
general, it is of interest to poll students on their
motivators in alignment with the elements in Table 1
above. Under the elements of purpose, mastery,
autonomy, and extrinsic (grade) / intrinsic (content)
motivation, the questionnaire seeks to explore which
elements are motivating for students.
Figure 6: Tendency of anti-correlated grades for frontal vs.
free style Software Engineering.
Added categories include challenges and urgency
from game mechanics. The original hypothesis that
we wanted to explore was whether students can be
grouped by motivators that can then be catered to in
different teaching styles and with different
mechanics.
Figure 7: Number of students who voted motivator as
important (choosing up to four motivators).
Instead, there was an overwhelming response
across all students regarding purpose and path as the
main motivators (Fig.7). After asking students, this
is to be interpreted within the framework of taking
an exam and obtaining a high grade. The clear path
refers to receiving material from the teacher that
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prepares for the exam with the purpose of knowing
this material to obtain a good/passing grade. The key
is to fulfill the basic needs of staying in the program.
Looking at the subgroups, however, there are some
clear differences between the two groups with
respect to some of the secondary motivating factors.
Figure 8 depicts some of the more pronounced
differences. Students who enjoy either format
(green, blue) like receiving feedback about their
learning status. Time to experiment with new
material and content tends to be more important for
those who like the open style of the Software
Engineering course than for any of the other
subgroups. This goes along with our expectations
but is clearly and strongly overshadowed by the
motivators fulfilling the basic needs.
Figure 8: Student Motivators by rating on new format.
Numbers are given in % of subgroup.
Because the new format required self-discipline
in scheduling the material and a fair amount of team
work, another set of questions is designed to find out
whether students’ dislike of teamwork is related to
the dislike of the new format. 80% of those students
who enjoy the new format like working in small
groups. Only 30% of students who dislike the new
format only enjoy working in teams.
Figure 9: Setting own goals by rating on new format.
Results are given in % for subgroups.
The original hypothesis was that most students
would welcome the motivation of being able to work
autonomously as a relaxing contrast to most of the
other “clocked” courses. Figure 9 depicts students’
desire regarding autonomy with respect to
scheduling of content according to subgroups and
supports our hypothesis only for the subgroup of
students who like the free style.
4.2 Matching Motivational Factors
Finally, it is important to see how the motivational
factors that are important to students match up with
their perceived experience of the different lecture
styles. In order to understand this, the same
questions have been asked about the average frontal
lecture in comparison with this particular Software
Engineering format in order to see how the results
compare.
Questions relate to the three motivators purpose,
mastery and autonomy as well as some questions
that relate to whether or not the basic needs, such as
performing well on the exam, are met. Figure 10
contrasts the replies corresponding to the average
frontal lecture when compared to Software
Engineering using the new format with numbers
given in % of 59 of those students who answered
with yes out of yes/no answers possible. While the
new format loses on fulfilling the basic need of a
student for efficiency and feeling prepared for the
final exam, the new format wins on aspects of
lecturer adapting to individual needs, students
wanting to understand the material and granting
autonomy. Still, with either format there is not
enough time to master the material properly.
Figure 10: How well are motivators fulfilled in frontal
lecture and new format classroom?
Figure 11 shows how opinions differ for each of
the four subgroups to gain a more detailed
understanding of the results. More students who like
the new format feel that the lecturer adapts to their
individual needs. Students who dislike frontal
lectures or like the new format tend to be more
interested in studying to understand the material
(mastery) rather than learning just for the exam.
Students with strong feelings either way about the
new format agree that the new format grants more
autonomy with respect to speed and order of the
material. On the other hand, students who have
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strong feelings about frontal lectures strangely do
not perceive autonomy granted by new format. It is
possible that autonomy may be perceived negatively
as being forced to work during class time.
Looking back at the analysis that shows that the
primary motivator was to fulfill the basic needs of
staying in the program with good grades, it is
especially disturbing that students who do not like
the new format are especially concerned about the
efficiency of in-class time when compared to frontal
lectures.
Despite individual feedback on their
performance using online quizzes throughout the
semester, most students regardless of subgroup, tend
to feel less prepared for the exam when compared to
a frontal lecture. After conferring with students on
this finding, it seems that the reason for this are the
open problems, no clear script to follow and more
than one correct answer for the higher level
problems, which makes it very difficult to know
when preparation for an exam has ended. This
mismatch of basic needs and fulfillment is
problematic.
5 QUALITATIVE ANALYSIS OF
STUDENT FEEDBACK
General student qualitative feedback, especially for
those that struggled with the new format reflected
three major areas: 1. the importance of knowing how
to obtain a good grade, 2. the difficulty of on-
boarding in this new learning style, 3. the lack of
supporting material even after buy-in to the new
format.
5.1 Open Questions vs. Clear Answers
The importance of the exam and the grade and as a
result the desire for clear “structure” - meaning that
the lectures should be very exact in preparing the
student for the exam by clearly covering necessary
material, sample questions and corresponding
answers that are known to be correct is clearly the
equivalent of the basic need that should be fulfilled
given the students’ “ecosystem” at the University.
This student goal is diagonally opposed to the
inability to memorize the correct answer to a
question like:
“List and weigh important criteria when selecting
a supporting tool for Lifecycle Management. Then
compare two tools of your choice and argue your
final choice based on your chosen criteria and
assumptions.” While this may be a real-life question
Figure 11: Contrasting how motivators are fulfilled in
Software Engineering for all different subgroups (in %.).
as it would be posed in the workplace, it is not a
“good” exam question (like “What does UML stand
for?”) as it does not have a single correct response
that could potentially be memorized. A good answer
would reflect how much time a student has spent
looking at what Lifecycle Management is
(information given in a lecture), what kinds of
processes it can support (lecture) finally what types
of tools and capabilities are available on the market
and which features distinguish these (research on the
web with provided links to start). The student has to
be able to analytically formulate criteria based on
assumptions that are important to a project and with
those in mind compare a number of tools. This
requires analytical thinking and transfer of know-
how to unknown situations, an important skill with
constantly changing tools in information technology.
There is no correct answer and assumptions have to
be stated as part of the answer to the question. Yet,
results from this work show a clear need for
facilitating the move towards answering these kinds
of questions.
5.2 On-boarding
Key take-home message from feedback of all
students is that the on-boarding process, the steps
from frontal lecture to free learning has to be gradual
and guided. Students had problems with
scheduling their own work
asking for lectures on demand
using the platform to their advantage (taking
quizzes regularly, improving answers upon
feedback, contacting expert students for help)
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leveraging in-class time (collaboration, lectures)
understanding that a question has more than one
correct answer
Feedback clarified the need for providing more
scaffolding at the beginning of the class and
removing these at rates depending on the individual
student. One of the questions on the questionnaire
asked students how they could have taken better
advantage of the new format of learning. It is
remarkable to note that only around five students
were able to reflect on their own ability to cope with
the new format. The importance of acknowledging
such difficulties and the need to “[…] intentionally
articulate[…] and foster[…] self-reflection and
awareness of processes important to learning such as
self-efficacy” (Shea, 2010, p. 1727) has been
documented in the literature.
5.3 Learning Material
Due to the misunderstandings of how students relate
to the materials that are given in the classroom, all
material has to be reviewed to support more self-
study and rely less on interaction between lecturer
and student. It has to be clarified that slides are not
meant to be stand-alone material. They cannot
replace reading a textbook, which is how students
expect to use them. Scripts in form of related books
need to be made more explicit. Problems have to be
more structured and even more explicitly marked as
closed form vs. open form answers based on
inference or analytic thinking. There were four
different platforms used for the first couple of weeks
before settling on two. A single platform, even if
suboptimal, is essential.
6 CONCLUSIONS AND FUTURE
WORK
A software engineering course was redesigned to
sort topics into paths and cognitive levels, game
mechanics were added and tools used to support
some of those mechanics to include motivators into
the classroom that are well known in the literatures.
Results showed that the primary needs for students
in a restricted grade-based environment were not
met in the given setup. Change management has to
be incorporated into the course to improve
acceptance beyond the type of student who is ready
for this new format.
The following changes are proposed based on the
findings: In order to survive, the design of this
course has to take into account the surrounding
ecology that is heavily grade-based and time-
constrained. It is necessary to meet the basic needs
of the students to make the path to good grades clear
(Maslow, ‘43). Yet, making this path too easy to
obtain more time to spend on deepening
understanding may cause other parts of the
ecosystem (other classes) to infringe on time that is
superfluous according to students’ goals. Portfolios
that count toward points on the exam may be part of
the solution.
Students feel that they have no overview over all
necessary and important areas of study. While they
will acquire knowledge in all areas during the course
of the third quarter, this was reportedly
uncomfortable. Giving an overview lecture of the
nine areas of study is very important. While students
were worried about missing something, only two
attended all nine lectures on their own accord.
The increasing difficulty due to increasing
openness of problem statements within Bloom’s
taxonomy has to be made explicit through improved
material and increased scaffolding showing example
answers and arguments for all levels. In combination
with the known need by employers for this
capability, there is a clear need to manage this
change in most students’ manner of working and
acquiring higher-order thinking skills.
The study showed clearly that the semantics
behind motivators such as autonomy, purpose and
mastery are defined differently for students. Mastery
relates to memorizing material for an exam in such a
way as to receive a good grade. Autonomy means
that a student is able to not participate during class
time and choose their own time for learning. Purpose
is to receive the necessary grades to obtain a
Bachelor degree to obtain a good job. These terms
have been defined throughout prescriptive school,
University and life in society that expects diplomas.
Even phases on the job within their time of study
have not had an effect on these definitions by the
time students reach their second year of study.
Further study is necessary to find out whether this
perception will change before the Bachelor is
obtained. In order to manage this change of
perception it may be necessary to include
employers’ in this dialogue by inviting them to class.
Future work includes reporting on the proposed
changes and further feedback to be collected from
the present group to find out long-term effects of the
learning experience after the end of second semester.
UnderstandingtheChallengesofIntroducingSelf-drivenBlendedLearninginaRestrictiveEcosystem-Step1forChange
Management:UnderstandingStudentMotivation
319
ACKNOWLEDGEMENTS
The authors would like to thank conversations with
employers and students who participated in the
survey and were part of the new way of teaching
while giving continuous feedback to the lecturer.
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