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.

1 INTRODUCTION

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

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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?

2 THEORETICAL FRAMEWORK

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

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

anew.

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

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

there

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 RESEARCH DESIGN

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

representations.

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-

Wenzel-Equation”.

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 RESULTS

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,

SD=1.10).

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

Session?

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

chapter

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

Motivation?

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,

p=0.003).

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

success.

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

learning

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

ES2

=4.23, SD

ES2

=0.93; M

ES3

=4.23, SD

ES3

=0.97,

ES4

=4.37, SD

ES4

=0.89) a minor negative arousal

ES2

=2.81, SD

ES2

=1.01; M

ES3

=2.75, SD

ES3

=1.01;

ES4

=2.64, SD

ES4

=1.01) and rather positive valence

ES2

=4.90, SD

ES2

=1.01; M

ES3

=4.99, SD

ES3

=1.13,

ES4

=5.27, SD

ES4

=0.96).

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

d1d3b3 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

sets).

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.

5 CONCLUSIONS

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

reasonable.

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.

ACKNOWLEDGEMENTS

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

Learning”.

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