Evaluation of Learning Motivation within an Adaptive e-Learning
Platform for Engineering Science
Mathias Bauer
, Jacqueline Schuldt
and Heidi Krömker
Media Production Group, Technische Universität Ilmenau, Gustav-Kirchhoff-Str. 1, Ilmenau, Germany
Keywords: Adaptive e-Learning, Learning Motivation, Self-regulated Learning, Technology Enhanced Learning.
Abstract: Learning motivation represents an important determinant for a successful learning process. Especially in the
context of self-regulated learning with e-learning systems learning blocks or learning breaks occur
increasingly when motivation is dropping. Creating appropriate learning experiences that respond to learners’
needs is important to maintain learning motivation. This supports continuous usage of e-learning systems at
universities. Adaptive e-learning systems are a possibility to react profoundly to individual needs of the
learners before or during the learning process. Therefore, an e-learning platform for micro- and nano-
technologies was transformed into an adaptive learning system to foster learning motivation at the Technische
Universität Ilmenau within a multi-level development process. Results showed that the e-learning platform
was well accepted but a significant benefit of the adaptive version compared to the non-adaptive version could
not be identified.
Motivated learning is the prerequisite for a deep
processing of learning content and a long retention
performance (Bauer et al., 2018a) as well as the basis
for joy of learning and persistent interest (Schiefele,
2009). Social aspects, personal histories, experiences
and circumstances may influence the motivation of
learners. Disturbances of learning motivation can lead
to superficial learning processes or even to learning
blocks. Therefore, an e-learning platform for micro-
and nanotechnologies was transformed into an
adaptive learning system that adjusts its user
navigation according to the current motivation of the
learner. Motivational user data is acquired through
self-reports within the platform. A multi-level
development and evaluation process was set up from
2017 to 2019 to answer the research question whether
learning motivation can be fostered with techniques
of adaptive navigation support. The focus of this
paper is on the evaluation of the final adaptive system
version. The progress of the current motivation was
measured during a laboratory study with 64
participants in early 2019 and an extract of the results
will be presented in the following paper. Research
questions of the study were: (a) Can a change of the
learning motivation be measured during an e-learning
session? (b) Does the instructional design has an
influence on the learning motivation? (c) How does
learning motivation affect the learning results?
The study heavily relies on the depiction of learning
motivation in a self-regulated e-learning session.
Therefore, a suitable motivation model has to be
defined and operationalised. Also, basics of adaptive
e-learning will be described.
Bauer, M., Schuldt, J. and Krömker, H.
Evaluation of Learning Motivation within an Adaptive e-Learning Platform for Engineering Science.
DOI: 10.5220/0009350600640073
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 2, pages 64-73
ISBN: 978-989-758-417-6
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2.1 Learning Motivation in
Self-regulated Learning Contexts
According to Rheinberg motivation is defined as the
“activating orientation of the current day-to-day
living towards a positively assessed target state”
(Rheinberg & Vollmeyer, 2012). Consequently,
motivation should be able to explain the direction,
persistence and intensity of behavior.
The research focus is on motivation in learning
contexts, especially on self-regulated learning in e-
learning. Intentional learning activities under one´s
own guidance, without direct tutor-instructions or
-control are called “self-regulated learning”
(Rheinberg et al., 2000). Therefore, the cognitive-
motivational process-model of self-regulated learning
was used as a framework for describing the effects of
the interrelation between person and situation factors
on the learning outcomes. As indicated in figure 1 the
framework starts with the antecedents of the current
learning motivation that result indirectly in learning
outcomes for a specific learning task and learning
episode via mediating variables during learning
(Rheinberg et al., 2000).
Besides demographic variables and prerequisite
domain-knowledge, motivational factors like self-
efficacy (Bandura, 1977) can be considered.
2.2 Adaptive e-Learning
Adaptive systems come in different varieties
depending on the frequency of adaptations and the
initiating force for these changes (Ennouamani,
2017). Leutner (2011) distinguishes macro- and
microlevel adaptation. There are adaptable systems
that allow the user to adjust defined system-
components, such as the user interface.
Macrolevel adaptation allows changes by the
system with a low frequency (Leutner, 2011). That
means the system changes only once or only at the
beginning of a session. In learning systems that could
be a test on the previously learned topics.
Microlevel adaptation goes one step further by
changing the system constantly depending on a
continuous stream of information from the user. The
system is monitoring the user and depending on her
state may initiate changes. Microlevel adaptation was
the basis for the development of the adaptive system
described in this paper. Paramythis et al. (2010)
describe the adaptation process for microlevel
adaptive systems in five steps. The basis for any
adaptation is the collection of user data. This could be
usage data or any other kind of measurement. Next,
the system needs to interpret the data. In this case, the
e-learning platform has to decide whether the learner
is motivated or not. Depending on this information
the system must make a decision on applying an
adaptation or not. Through the adaptation technique
the user interface changes and the user becomes
aware of the adaptation. This may again lead to
changes in the user states and initiates the process
Figure 1: Cognitive-motivational process-model of self-regulated learning (in accordance to Rheinberg et al., 2000).
Evaluation of Learning Motivation within an Adaptive e-Learning Platform for Engineering Science
Situational factors can address the instructional
design of e-learning environments and should foster
the current learning motivation. An established model
for the derivation of design recommendations is
Keller´s ARCS-model (Keller, 2010). The four major
components attention (A), relevance (R), confidence
(C) and satisfaction (S) provide the conceptual
framework for the use of motivationally fostering
actions (Zander & Heidig, 2018). The mediating
variables can for example be the learner´s emotional
functional state. This is especially of interest since
are conceptual similarities between motivation
and emotion. Considering Rheinberg´s definition of
motivation it is obvious that positive activation, as
part of a circumplex model of affect (Schallberger,
2005), is also a core component of motivation
(Rheinberg, 2010).
The operationalised framework depicts learning
motivation as a process variable. Direct effects of
learning motivation on learning outcomes are not
automatically to be expected, but mediated through
variables like the emotional functional state.
Especially, complex tasks demand a preferably direct
acquisition of motivation and its indicators, because
learning outcomes in this case are dependent on many
factors. Such "live" acquisition of motivational data
can be achieved through self-reports in the form of
experience sampling approaches (Engeser, 2005).
The adaptation of the described e-learning
platform relies on adaptive navigation support.
Basically, adaptation techniques can be divided into
two large groups: adaptive presentation on the one
hand and adaptive navigation support on the other
hand. While adaptive presentation techniques such as
stretch text or dimming change the content itself,
adaptive navigation support changes the way the
learner is guided through the learning material. A
comprehensive overview of adaptation techniques is
given by Knutov et al. (2009). Two major techniques
for adaptive navigation support are for example direct
guidance and link annotation that were also tested
within the multi-level research design that is
described in the following section.
3.1 Evaluation Process
Figure 2 shows a simplified version of the overall
research design for developing and evaluating the
adaptive e-learning platform NanoTecLearn (NTL).
In the first stage the motivation of learners was
evaluated in a laboratory study with the non-adaptive
basic-version. Also, possible adaptation techniques
were derived with the aid of techniques of user-
centred development, especially in the form of focus
groups with learners and experts for usability, e-
learning didactics and e-learning technology (Bauer
et al., 2018b).
The second stage was then the implementation of
three adaptive versions of the learning platform
specifically link annotation, direct guidance and a
pedagogical agent. These versions were compared to
the non-adaptive version in the form of an
experimental laboratory study. Therefore, a formative
and summative evaluation of the system with learners
was carried out. For in depth results see (Bauer et al.,
2019a; Bauer et al., 2019b).
The current paper focusses on the last stage of the
process, the user evaluation with the final version of
the adaptive system. Within figure 2 this is
represented as no. 4: Survey of learning motivation
with platform-version V2, the optimized adaptive
NTL Platform.
3.2 Description of the Adaptive
e-Learning Platform
The e-learning platform that served as a case study for
the evaluation process was the NanoTecLearn
platform that was developed as a non-adaptive
knowledge and learning platform in a research project
from 2014-2017 at the Technische Universität
Ilmenau. The platform describes different phenomena
in the area of micro- and nanotechnologies. It offers
three access points to the knowledge. The first point
is classical text and images or videos. Most
knowledge is represented in this way. Figure 3 gives
a brief overview of the theory-section based on the
chapter “Wechselwirkungen an Grenzflächen”.
Texts, a table and an embedded learning video are
displayed within this figure.
Furthermore, the platform allows learners to view
and interact with different images of samples. Figure
4 shows a microscope image that is part of the
learning platform. Viewing and interpreting images
from different kinds of microscopes are important
skills for this field of application. Since microscopes
are a limited resource, actual time with these tools is
limited during training. NanoTecLearn offers a
simple but powerful substitute for this. Lastly,
formulas and physical laws describe the phenomena.
Therefore, the platform includes interactive formulas
like the Young-Wenzel-Equation shown in figure 5.
Learners can change different parameters within a
formula and view the implications in graphical
This way, they can gain a better
CSEDU 2020 - 12th International Conference on Computer Supported Education
Figure 2: Procedure model for the further development and evaluation of the e-learning platform.
understanding of the formulas. The content of each
chapter is divided into six parts, which can be seen on
the left side of figure 3. First, there is a short
introduction into the topic of the chapter called
orientation, which is followed by a longer theoretical
section that introduces the main themes of the
chapter. Next is a part on practical implications and
scenarios called application. After that learners can
interact with formulas, samples or 3D-models of
microscopes and other tools in the interaction-section.
Lastly, the main points of knowledge are summarised
and follow-up questions are asked. The chapter closes
with a repetition of the given contents. This also
includes interactive quizzes and questions for testing
the own domain-knowledge. The different chapters of
NanoTecLearn can be accessed via the navigation bar
at the bottom of figure 5.
Figure 3: Screenshot of the theory-section of the chapter on
“Wechselwirkungen an Grenzflächen”.
Figure 4: Screenshot of an interactive sample “Strongly
wetting surface through additional layer”.
Figure 5: Screenshot of an interactive formula “Young-
The evaluation of the adaptive versions described
before resulted in the implementation of a final
adaptive version of the platform that relied mainly on
the link annotation technique and some aspects of the
direct guidance technique.
Evaluation of Learning Motivation within an Adaptive e-Learning Platform for Engineering Science
Basically, link annotation is a recommendation
technique that highlights appropriate subsequent
sections of a chapter based on the motivation based
self-reports that have to be filled at the end of every
section. The direct guidance leads the user through
relevant content. Besides the visual clue in the form
of a subtle highlighting there is also a verbal coding
naming the recommended section. Being a
recommendation system the user is free to choose
whether to follow these hints. Completely ignoring
the hints, it would thus be possible to experience a
totally self-regulated learning as in the non-adaptive
version. This was one specifically named requirement
during the conception-phase that learners want to
have full control over their learning process.
The motivation based self-report mechanism of
the platform relies on two elicited parameters of
current motivation, being situation-specific interest
and confidence or competency expectation
(Vollmeyer & Rheinberg, 2003). The mechanism
included two items, one for each parameter, and a
three-point scale telling the system whether the user
is still interested or confident or not or whether the
state remains unchanged. Recommendations that
should foster the learning motivation are only given
if there is a decrease in one of the two parameters.
Otherwise, the user will be guided hierarchically
through the chapter. For every chapter and section
there is a decision-table in the backend for suitable
subsequent contents that might increase the learning
motivation. This was also conceptualised throughout
the already mentioned focus groups.
4.1 Study Design
The laboratory study was conducted in January 2019
and consisted of a sample of 64 students of the
Technische Universität Ilmenau. The study design
and included survey instruments were based on
Rheinberg´s motivation model that was described in
section 2.1. Figure 6 shows the operationalised
framework. Elicited person variables were
demographic and study related variables as well as
domain specific knowledge and self-efficacy beliefs.
Situation variables were the motivationally aspects of
the instructional design according to Keller´s already
mentioned ARCS model. The four factors of the
model were measured with the aid of the Instructional
Materials Motivation Survey (IMMS) (Keller, 2010).
The current motivation was conceptualised according
to Rheinberg on the basis of four factors and
measured with the belonging survey called FAM
(Rheinberg et al., 2001). As a mediating variable
served the emotional functional state that was
measured with a short survey for eliciting positive
and negative arousal as well as valence called
PANAVA-KS (Schallberger, 2005).
There were four measurements of the emotional
state throughout the e-learning session – one directly
after the first section, the second one after the theory-
section, the third one after the first interaction with
the samples and the last one after the interactive
Figure 6: Operationalised framework for learning motivation and its effects on self-regulated learning (in accordance to
Rheinberg et al., 2000).
CSEDU 2020 - 12th International Conference on Computer Supported Education
formula. During the study, the students had to work
through one predefined chapter of the platform called
“Wechselwirkungen an Grenzflächen” (interactions
at boundary layers). Also, the individual user paths
through the learning material and the self-reports of
motivation within the platform was gathered with the
aid of log files. After completing the chapter there
was a test to record the learning results.
The experiment took about 50-60 minutes. Every
participant worked on two screens, one depicting
NanoTecLearn and one depicting the survey to
prevent users from overlooking relevant parts of the
instruction. The study was conducted in a computer
lab and a maximum of six students could be tested
simultaneously. The participation was also rewarded
with a remuneration of 20 Euros.
4.2 Data Analysis and Results
4.2.1 Description of the Sample
The sample consisted of 39 male and 25 female
students that were between 19 and 35 years old
(M=24.31, SD=3.80). With a number of 38, the
majority of students were matriculated in Bachelor
study programs. The content of the platform aimed at
students of engineering sciences but the study was
also open to students without prior knowledge.
Nevertheless, the test of the prior knowledge that
came with six achievable points showed an overall
medium domain-specific knowledge (M=3.97,
4.2.2 Description of the Process of Learning
Motivation with Log File Analysis
Research Question (a): Can a Change of Learning
Motivation be Measured During on e-Learning
Figure 7 shows the process of the learning motivation
measured via the self-report mechanism of the
platform during the e-learning session. Table 1
additionally defines the encoding of the parts of the
chapter and specifies the means of the motivation
rating. Every chapter of the platform had an assigned
number. For the relevant chapter of the study this
number was 34.
The visualisation of the process shows that there
was a decrease in learning motivation during the text-
heavy parts like theory and application. The
interactive parts of the platform like the samples and
formulas were able to increase the motivation
noticeable. Compared to the prototypical
implementations that were tested in an experimental
laboratory study before (Bauer et al., 2019b) the
repetition could enhance the motivation even more.
This may indicate that the improvements regarding
the insertion of interactive tests and quizzes could
lead to a better learning motivation.
Figure 7: Process of learning motivation measured via the
self-report mechanism during the e-learning session.
In sum over all participants the system made 284
suggestions of suitable subsequent sections on the
basis of the self-report based adaptive navigation
support. 217 of these suggestions were followed by
the users which means that around three-fourths of
the system-based suggestions were likely seen as
useful. In comparison, for the Link Annotation
approach of the previous study, only 65% of the
suggestions were followed (Bauer et al., 2019b).
Table 1: Encoding of the sections of the chapter and means
of the motivation rating.
Section of the
Encoding Mean of the
motivation rating
Orientation 34.1 3.59
Theory 34.2 3.47
Application 34.3 2.71
Interaction 34.4 3.26
Repetition 34.5 3.46
References 34.6 2.00
4.2.3 Analysis of the Elicitation of the
Instructional Design
Research Question (b): Does the Instructional
Design has an Influence on the Learning
To investigate this research question, correlation and
regression analyses were conducted for the
instruments measuring current motivation (FAM) and
the instructional design (IMMS).
Statistically significant results could be found for
situational interest and relevance (r=0.365, p=0.001),
situational interest and confidence (r=0.289, p=0.010)
and situational interest and satisfaction (r=0.338,
Evaluation of Learning Motivation within an Adaptive e-Learning Platform for Engineering Science
The factor of situational interest thus is positively
correlated with three out of the four examined factors
of the instructional design, which means that higher
values of situational interest probably will lead to a
more positive judgement of the instructional design.
For the factor challenge and the instructional design
no significant correlations could be found. The factor
confidence in success reveals statistically significant
positive correlations with the factors relevance
(r=0.251, p=0.023), confidence (r=0.403, p<0.001)
and satisfaction (r=0.212, p=0.046) of the
instructional design. The factor fear of failure only
showed a statistically significant positive correlation
for relevance (r=0.259, p=0.019). The positive
direction of this correlation was unexpected since the
conceptually opposed concept of confidence in
success also showed a positive correlation. This may
be due to the common distinction between these two
approaches as key motives of achievement
motivation (hope for success vs. fear of failure)
(Schiefele & Schaffner, 2015).
For examining the influence of the instructional
design on the learning motivation multiple linear
regression analyses were conducted.
For the factor situational interest, the model
showed a moderate explained variance (Cohen, 1988)
with an adjusted R²=0.155. The factor situational
interest is predicted statistically significant by the
instructional design, F(4,59)=3.882, p=0.007.
Therein, relevance was the only significant predictor
(β=0.271, p=0.036). For confidence in success the
model showed also a moderate explained variance
with an adjusted R²=0.196. The factor confidence in
success is predicted statistically significant by the
instructional design, F(4,59)=4.840, p=0.002. There
were two significant predictors within the model,
confidence and attention. Confidence had the
strongest influence (β=-0.460, p=0.001). Attention
also predicted the motivation factor significantly (β=-
0.265, p=0.038) although the direction of the
coefficient was surprising as it indicates that an
increase of attention would lead to less confidence in
For the factors challenge and fear of failure no
statistically significant influences could be found
within the models.
4.2.4 Analysis of the Mediation-effect of
Current Motivation Mediated through
Emotional State on the Test-results
Research Question (c): How Does the Learning
Motivation Affect the Learning Results?
The already described process model of Rheinberg et
al. (2000) postulates an indirect influence of the
current motivation on the learning results. Therefore,
a mediation analysis was conducted, relying on the
mediation model displayed in figure 8. The measures
of the emotional states two, three and four were used
since these served as the mediators in the first study
with the non-adaptive version of the platform (Bauer
et al., 2018c). Measure one was excluded since it was
not part of the actual learning session during the first
study and instead was measured before the orientation
as a baseline.
Before conducting the mediation analysis, the
descriptive statistics of the parts of the analysis are
examined. Overall, the students showed a moderate
situational interest (M=4.38, SD=0.82) and
confidence in success (M=4.58, SD=1.02). The
material was rated challenging (M=5.32,
Figure 8: Mediation model of current motivation, emotional
state and test results.
SD=0.72) but there was no distinct fear of failure
(M=3.20, SD=1.15).
The overall ratings of the emotional state of the
measures showed a moderate positive arousal
=4.23, SD
=0.93; M
=4.23, SD
=4.37, SD
=0.89) a minor negative arousal
=2.81, SD
=1.01; M
=2.75, SD
=2.64, SD
=1.01) and rather positive valence
=4.90, SD
=1.01; M
=4.99, SD
=5.27, SD
Both instruments come with a 7-point-likert scale
with values from one to seven.
Also, before the mediation analysis an ANOVA
for repeated measurements was conducted. For the
three included measures two, three and four no
significant differences could be found. With a
Greenhouse-Geisser correction the mean positive
arousal levels for all four measures showed a
statistically significant difference between measures,
F(2.32, 78.97)=5.14, p=0.006, partial η²=0.131.
Statistically significant differences were found
between measures one and two (p=0.025) and
between measures one and three (p=0.002). In both
cases there was a significant decrease in the positive
arousal; that means during the text-heavy sections of
CSEDU 2020 - 12th International Conference on Computer Supported Education
the platform the positive arousal decreases, which
could also be shown for the process of learning
motivation (research question A).
For the actual mediation no significant paths from
current motivation over the emotional state-measures
to the score of the domain specific test could be found
which is in contrast to the findings of the study with
the non-adaptive platform. There, for example the
assumption of the model worked perfectly for the
situational interest of current motivation and the
positive arousal of the three measures.
In the current study, there were statistically
significant paths from the three mediators to the test
results for the models of valence and each of the four
factors of the current motivation (the path
d1d3b3 of the model). In no account there were
significant paths between the motivation factors and
the emotional state (the “a”-paths of the model) which
was in contrast to the study with the non-adaptive
platform as mentioned before. What remained the
same for both studies was the non-significant path c
in any combination of factors. Current motivation
therefore does not seem to be a significant predictor
of the test results. This suggests that the model
assumption of an indirect effect might be true but
maybe emotional state in this study was not the
appropriate mediator in contrast to the study with the
non-adaptive platform.
4.3 Limitations of the Study
The data acquisition of the emotional states during the
e-learning session was a much bigger challenge
compared to the study with the non-adaptive platform
were the students had to go through the material
stepwise from orientation to repetition. Because of
the recommended following sections each learning
path could differ from one another. Therefore, the
emotional state was captured via pop-up windows
that were attached to the end of the relevant sections
orientation, theory, samples and formulas.
Unfortunately, the pop-ups were not displayed
correctly for every participant, which led to data
losses (measure two had 20 missing data sets,
measure three and four had 13 and 15 missing data
The domain specific test during the study with the
non-adaptive platform was paper and pencil-based.
The test at the end of the current study was directly
implemented in the online-survey and thus contained
some other task-types like drag and drop. The test
seemed to have a much higher level of difficulty since
the average score (M=1.64, SD=1.13) was quite low
compared to the moderate score of the test of the prior
knowledge. For a maximum of six points this result is
not acceptable. The average score of the test of the
study with the non-adaptive platform was distinctly
higher (M=4.23, SD=1.21).
Another limitation is that more students from non-
engineering science programs participated in this
study compared to the first one, which also could be
an explanation for the worse test results. The sample
size itself was rather small and was also determined
by the research economic framework conditions. A
major challenge was that the platform could not be
integrated into a specific lecture since the samples of
the addressed study programs that get in contact with
micro- and nanotechnologies are too small for
meaningful results.
In summary, the existence of the platform itself was
seen as a motivational factor and added value for
many of the participants. This was clearly shown in
the commentary sections at the end of the survey.
Many students stated that they wished to have this or
a similar platform for their study-relevant contents to
support their self-regulated learning as an addition to
the actual lectures.
However, the implementation of adaptive
navigation support does not seem to support the
students in a way that it is truly fostering learning
motivation. Compared to the results of the study
conducted with the non-adaptive version of the
platform the results actually deteriorated as for
example the score of the domain specific test could
not be predicted by current motivation and emotional
state anymore. Therefore, it should carefully be
considered whether the additional effort of
conceptualizing and implementing adaptive
components on top of the already time- and effort-
consuming implementation of a learning platform is
Possible alternatives may be the integration of
short learning videos, interactive tests or gamification
elements, which also were named to foster learning
motivation in the commentary sections of the current
study. Furthermore, direct guidance facilitates the
entry into complex topics, especially when previous
knowledge is low.
Future studies in this field should try to
implement working with e-learning platforms into
“real” study-settings during one semester with an
actual exam. Since many lectures are relevant for a
variety of study programs it could also be possible to
shift the adaptation from adaptive navigation support
Evaluation of Learning Motivation within an Adaptive e-Learning Platform for Engineering Science
to adaptive presentation like study-specific contents
and case-studies. Also, other potential mediators
affecting current motivation and learning results
should be examined like flow, concentration, time on
task or alike.
Part of the authors’ work has been supported by the
German Federal Ministry for Education and Research
(BMBF) within the joint project SensoMot under
grant no. 16SV7516, within the program “Tangible
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Evaluation of Learning Motivation within an Adaptive e-Learning Platform for Engineering Science