Adaptive E-Learning Technologies for

Sustained Learning Motivation in Engineering Science

Acquisition of Motivation through Self-Reports and Wearable Technology

Mathias Bauer, Cassandra Bräuer, Jacqueline Schuldt and Heidi Krömker

Technische Universität Ilmenau, Department Media Production, Gustav-Kirchhoff-Str. 1, Ilmenau, Germany

Keywords: Adaptive E-Learning, Motivation, Self-Regulated Learning, Sensory Data, Technology Enhanced Learning

and Teaching, Wearable Technology.

Abstract: Surveys show besides the number of students also the drop-out rates are increasing, especially in early phases

of studying natural or engineering sciences. The research project “SensoMot - Sensor Measures of Motivation

for Adaptive Learning” tries to counter this development by means of improving the quality of teaching in

the department of micro technology with the help of an adaptive e-learning system. For that purpose, the

mediated learning content should be better adapted to the individual prior knowledge, competencies and

motivational profiles of the learners. Furthermore, the continuous sensory data acquisition of physiological

parameters of the learner shall be accomplished by current wearable technology. The paper presents first

results in the form of conceptual determinations concerning self-reports and physiological measures,

instructional design and adaptation techniques and further includes the early involvement of the subsequent

users in the development process through an iterative, formative evaluation of prototypical solutions.

1 INTRODUCTION

Motivated learning is the prerequisite for a deep

processing of learning content and a long retention

performance (Schneider et al., 2017), as well as the

basis for joy of learning and persistent interest

(Schiefele, 2009). Conversely, disturbances of

motivation can lead to superficial learning processes

or even to learning blocks. The SensoMot project

investigates the detection of critical motivational

incidents by two different approaches: first, through

self-reporting methods and second, through sensory

data acquisition with wearables, in the sense of fitness

trackers or smartwatches. These motivational

incidents will be used to adapt learning content at

runtime and thus enhances motivation. Such

increased learning motivation could lead to better

learning outcomes in technology-based learning and

teaching scenarios (Hartnett, 2016; Schneider et al.,

2017). The intention of the paper is to mark a first step

towards the examination whether learning motivation

can be positively affected by means of adaptive e-

learning. Therefore, first insights regarding the

potential of sensor measures combined with self-

report data for adaptive e-learning will be provided.

Accordingly, the theoretical framework is outlined

first and the potentials of wearable technology in

contexts of educational science are pictured second.

Chapter four introduces the study setting for

formative evaluation of the prototypical solutions.

Chapter five concludes with a summary.

2 THEORETICAL FRAMEWORK

This research focuses on observing motivation in self-

regulated e-learning sessions. Therefore, the

following chapter will bridge the domains of learning

motivation and adaptive educational systems relying

on established frameworks and concepts.

2.1 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 and Vollmeyer, 2012). Consequently,

motivation should be able to explain the direction,

persistence and intensity of behavior.

418

Bauer, M., Bräuer, C., Schuldt, J. and Krömker, H.

Adaptive E-Learning Technologies for Sustained Learning Motivation in Engineering Science.

DOI: 10.5220/0006787104180425

In Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), pages 418-425

ISBN: 978-989-758-291-2

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 (see

Rheinberg et al., 2000).

Besides demographic variables and prerequisite

domain-knowledge, several motivational person

factors are included like self-efficacy beliefs

(Bandura, 1977), domain-specific interests (Krapp et

al., 1992) and two forms of incentives in the form of

intrinsic and extrinsic motivational orientation

(Rheinberg and Vollmeyer, 2012).

Situational factors address the instructional

design of e-learning environments that 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 (Keller, 2007; Niegemann et al., 2008). The

mediating variables focus on the learner´s emotional

functional state due to 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 operationalized 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 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 or via physiological

parameters (Engeser, 2005).

2.2 Acquisition of Critical Motivational

Incidents

Motivation is a broad construct that influences the

behaviour of a person for long periods of time.

Psychology relies on two methods for surveying

constructs such as that. The most widespread method

is self-reporting in the form of questionnaires.

Figure 1: Operationalized framework for learning motivation and its effects on self-regulated learning (in accordance to

Rheinberg et al., 2000).

Adaptive E-Learning Technologies for Sustained Learning Motivation in Engineering Science

419

As a result of advances in technology and

neurosciences the acquisition of physiological data

and its interpretation for psychological phenomenon

is getting more popular.

As mentioned before the SensoMot project is

utilizing both methods to identify motivational

incidents in learning processes. This paper focusses

on the self-reporting methods, but a brief overview of

the acquisition of physiological data shall be given.

2.2.1 Sensory Data Acquisition of

Physiological Parameters

The measurement of physiological data used to

involve medical equipment and an experimental

setting in a laboratory. Due to the enhances in mobile

technology, unobtrusive sensors in the form of

wearables, such as smartwatches and fitness trackers,

became available (Park et al., 2014).

One group within the SensoMot project is

currently investigating their potential to replace

expensive equipment and allow the study of

physiological data in non-laboratory settings. The aim

of their research is to first, identify appropriate

physiological indicators for motivation and second, to

determine suitable devices for their measurement.

Available physiological indicators range from

electroencephalography (EEG), electrocardiography

(ECG), pulse, blood pressure, electrodermal activity,

electromyography (EMG) to sugar and oxygen levels

in the blood. A comprehensive overview of these

indicators, their measurement as well as the

advantages and disadvantages, can be found in

(Sullivan, 2017; Hou et al., 2015; Trepte and

Reinecke, 2013). This kind of data in conjunction

with the tracking of useage data would allow the

localisation of critical motivational incidents in time

as well as in space. Due to the continous survey of the

physiological data, it would be possible to identify a

specific point in time when a change in motivation

happened. Combined with logs of the current position

in a learning segment, it would be possible to

determine which content is responsible for this

change. First studies with wearables in the project did

not produce stable and reliable results.

Furthermore, current studies hint at an insufficient

quality of the physiological data acquired by

wearables (see in comparison Arriba-Pérez et al.,

2016). A recent similar project proposal reports about

the problems and issues on the use of commercial

wrist wearables in education and about supporting

technologies for smart education in the context of

new smart universities (Arriba-Pérez et al., 2017).

2.2.2 Self-Reporting Methods

Self-reporting data can enhance the findings achieved

with the physiological data. As mentioned before the

data acquired with wearables can provide insights

into when and in which context motivation changes.

But it does not explain why these changes happen.

Self-reports can fill this gap. They can offer an

understanding of the reasons why critical

motivational incidents arise.

Gathering physiological data also contributes to

an advanced implementation of self-report data. Due

to the continuous measurement of physiological data,

patterns in motivational changes become apparent.

This helps to identify appropriate time intervals in

which questionnaires should be administered.

Since this methodology is the main focus of the

current paper, a detailed explanation of suitable self-

report instruments can be found in chapter four

Methodology.

2.3 Adaptive Educational Systems

There are two ways in which a system can adapt to

the user. First, users can change settings that

influence presentation and content themselves. In this

case the system is adaptable. The other way for a

system to react to user needs is by forming a user

model, which predicts demands. According to these

predictions the system follows a set of rules to change

its appearance and content. This is meant by

adaptivity. (Mödritscher, 2007)

The user model can contain different information

about the person interacting with the system. It is

continuously updated and serves as a basis for all

adaptation. Once the system registers a change in the

user model, it will react and try to adapt itself

according to the changes.

Kobsa et al. (2001) categorize the information in

a user model as user characteristics, usage data and

environment data. Environment data is gaining

relevance due to the variety of devices on which

systems can operate today. Not only does the screen

size impact a system, but also the environment in

which it is being used. Furthermore, systems can

collect different information about when, how long or

how often a user is interacting with them. This usage

data can provide insights into problematic

interactions. (Kobsa et al., 2001)

The heart of a user model consists of the user

characteristics which describe the user’s needs and

preferences. These information change according to

the type of adaptive system and its goals.

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Rachbauer mentions the following five groups for

educational systems (see Table 1).

Table 1: Information that could be included in a user model

(Rachbauer, 2009).

individual characteristics

examples

cognitive capabilities

learning, intelligence,

concentration, memory

knowledge

domain specific

knowledge, general

knowledge

goals

learning goals

preferences

learning styles

physical characteristics

disabilities

Adaptation can be achieved by different methods

and techniques. Methods are more abstract than

techniques. While techniques include an adaptation

algorithm, methods represent a general concept.

(Koch, 2000)

Brusilovsky distinguishes two methods for

adaptation. Adaptive presentation describes all

changes to the content and the presentation of a

system. This includes the usage of images, videos or

animations as well as text. On the other hand,

adaptive link support guides the user through a

system by changing links. (Brusilovsky, 1998) Each

of these methods encompasses multiple techniques.

An overview of all techniques can be found in

(Brusilovsky, 1998) and (Knutov et al., 2009).

For this paper the link annotation technique is

especially relevant. This technique highlights

appropriate links to relevant content for the user. This

highlighting could be implemented as different

colours, small icons or additional tooltips.

3 POTENTIALS OF WEARABLE

TECHNOLGY IN CONTEXTS

OF EDUCATIONAL SCIENCE

To exclude usability of wearables as an influencing

variable on the learning process, a first formative

usability evaluation of current consumer-wearables

was conducted in 2016 at the Ilmenau University of

Technology. In this study 30 students tested four

fitness-trackers and one smartwatch. The user study

evaluated the devices in terms of usability and user

experience by means of self-reports, focus groups and

requirements definition (Schneider et al., 2017).

The usability tests were based on a functional

analysis for identifying benchmark tasks that served

for the creation of representative user tasks with focus

on performance, ease of use as well as usability

metrics like efficiency and effectiveness (Hartson and

Pyla, 2012). The usability of all devices was rated

very positive. Especially the smartwatch exceeds the

fitness tracker in terms of user experience, because of

the wider scope of functionality and the more

appealing user interface. As other studies have

shown, present wristband-wearables currently seem

to be a convenient extension to a user´s smartphone

(Min et al., 2015). Most wearable users seem to be

early adopters (Lyons, 2015). Trends like quantified

self in combination with revealing a true added value

could have the potential to increase user acceptance.

4 METHODOLOGY

Engineering studies are characterized by extensive

basic knowledge, which is acquired in the first

semesters, and then in the following semesters should

be linked with domain-specific knowledge.

Therefore, three chairs of the Ilmenau University of

Technology worked together with associations and

companies to develop an e-learning platform for

micro-nano-integration. The goal was to make the

difficult to grasp area of micro- and nanotechnology

available for education and training. The resulting

NanoTecLearn platform was intended to specifically

support companies in qualifying employees in the

field of micro-nano-integration (Krömker et al., 2017,

2015).

This knowledge platform serves as a case study

for the SensoMot project. It is intended to shift the

application area towards university learning. Within

this scope the platform will be transformed into an

adaptive version that adjusts its instruction based on

the current learning motivation (see Figure 2).

Figure 2: SensoMot process chain: Sensor data is

condensed by means of pattern recognition to indicate

motivational states, which are used for the adaption of

learning content (Schneider et al., 2017, p.269).

Adaptive E-Learning Technologies for Sustained Learning Motivation in Engineering Science

421

NanoTecLearn provides a very good foundation

for an adaptive system since it offers learners

different approaches according to their knowledge

background and learning preferences.

Learners with a stronger inclination towards

physics and mathematics can use interactive formulas

to get a deeper understanding of a topic. Users with a

higher interest in chemical and biological examples

can view and interact with samples (see Figure 3).

Figure 3: Formula, Sample and Text in NanoTecLearn.

NanoTecLearn was also evaluated in terms of

usability. Especially the design of the platform was

received very positively. Therefore, negative effect

on motivation by design error can be eliminated.

4.1 Studies for Identifying “Critical

Motivation-Incidents” During an

E-Learning Session

Before updating the conventional NanoTecLearn

platform to an adaptive educational system, the

motivational disposition of the learner population, the

motivational status quo of the instructional design and

motivationally critical instants of time during an e-

learning session should be examined. Several studies

will be conducted. The main study will be oriented

towards Rheinberg´s framework (see section 2.1) and

will focus on task specific aspects of current

motivation like interest or challenge that are capable

of predicting the learning outcome in self-regulated

comprehension-learning settings (Rheinberg et al.,

2001). According to the framework these aspects are

said to affect learning outcome indirectly through the

mediating variable emotional functional state

(Rheinberg et al., 2000).

The learner´s current emotional condition will be

measured at central transitions during the e-learning

session as micro-analytic experience sampling-like

procedure (Csikszentmihalyi and Larson, 1987).

Emotions are measured according to Schallberger´s

dimensional model (Schallberger, 2005).

Also, qualitative interviews will be conducted

after the e-learning session to further investigate

motivationally critical phases and possible reasons

for their appearance. The data will form an important

foundation for the design and implementation of the

adaptive version of NanoTecLearn. The study will

also evaluate the current e-learning regarding the

ARCS-components. Additionally, a guideline

inspection considering ARCS-strategies will be

performed in cooperation with e-learning experts.

In a parallel online study the learner population

will be examined concerning their overall study

interest, aspects of learning strategies (Schiefele and

Wild, 1994) and the prevalent dominance of intrinsic

or extrinsic motivation.

The identification of critical motivation-incidents

shall assure a high-quality adaptation in terms of

providing an added value regarding tutorial support

of the e-learning platform that meets the users´ needs.

Being a key factor in education, fostering learners´

motivation can be an important first step in the

personalisation of learning systems (Baumstark and

Graf, 2014).

4.2 Prototypical Design of the Adaptive

NanoTecLearn Platform

The e-learning system described in this paper will use

adaptive link annotation according to changes in the

motivation of the learner. As a starting point, a

prototype was created that represents a segment of the

NanoTecLearn platform.

The prototype was implemented with a

prototyping tool for websites and mobile apps (see

https://www.justinmind.com/).

Since there is currently no functional adaptation

algorithm that can detect motivational changes in

sensor data, the prototype uses self-reports via

buttons as indicators for motivation. The user will

need to choose, whether her interest and self-efficacy

are increasing or decreasing (see Figure 4 and 5). The

buttons are linked to parts of the booklet that should

either improve or maintain the current level in interest

and competence.

At this point several questions need to be

approached. The current prototype will react if one

and only one of the factors interest or competence

changes.

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422

Figure 4: Schematic structure of the NanoTecLearn-

Prototype.

Figure 5: Example for the integrated self-report-mechanism

within the NanoTecLearn-Prototype.

Ideally the system should also adapt if both

change in the same direction. This could be achieved

by adding scores for interest and competence.

Depending on the final score, different links are

being highlighted. This process would lead to

problems, if interest and competence change in

different directions and each change would require a

different content section to compensate for that

change.

Another challenge to be discussed is when and

how many links are going to be highlighted. Some

chapters on the NanoTecLearn platform are long and

might take a learner several minutes to read. If a

change in the user model occurs and the system reacts

immediately, the learner might leave a section

without completely reading it. This way important

information could be missed. Therefore, the system

should only act once a section has been finished.

This requires signals that indicate a user is no

longer working on a section. Average reading time

and the current display might be such signals.

Once the user has finished a section she will

consider which section to work on next. This is when

link annotations will help to find relevant information

according to the user’s current motivation. In the

NanoTecLearn platform this is achieved by

presenting different approaches to knowledge. Either

the user reads a text explaining a theory or she could

interact with samples or formulas. Highlighting all

relevant information will be confusing, so a selection

will be necessary. The optimal solution would be to

just highlight a single link.

Due to the navigational structure of the

NanoTecLearn platform consisting of two layers, it

might be acceptable to allow one annotation per layer.

One of the layers represents different learning

segments and the other one different chapters within

a segment. This allows the system to direct the learner

towards more information on one topic or towards a

completely different topic. Additionally, one can

change between different approaches (formulas or

samples) as mentioned before.

5 SUMMARY AND CONCLUSION

Learning motivation is an essential condition for

successful learning in e-learning environments. Both,

learner characteristics and the design of the learning

situation, determine whether learners are engaged and

focused on a learning activity.

If a lack of motivation or motivational blocks are

detected early on, learning processes can be modified

and learning content can be adapted to the needs of

the learner.

This paper outlines briefly the sensory recording

of motivation indicators by means of wearables for

the adaptive control of learning contents of an e-

learning platform on the one hand and the need of

self-reporting methods on the other hand. Wearables

are used to collect physiological data of the learners,

which allow conclusions about psychological

parameters such as stress or motivation. The future

algorithm of the educational software will perform

various adaptations based on this data collection. For

example, the form of learning or the presentation of

the learning content can be adapted. Corresponding

learning scenarios are prototypically developed and

evaluated for university teaching using the example

of "nanotechnology" as well as for vocational

distance learning in technical education in

"mechanical engineering".

Adaptive E-Learning Technologies for Sustained Learning Motivation in Engineering Science

423

The usability and user experience of current

available wearables have proven to be good in user

tests. The acceptance and potential of the technology

is focussing in the opinion of the users particularly on

the fitness and lifestyle aspect of the devices. The

examination of the suitability and acceptance in the

learning context is still pending and will depend on

the meaningful embedding of the technology in the

learning process. The benefit of physiological

measurements is the production of unbiased,

quantitative outcomes. Currently, however, there is

no conventional market sensor available, that fully

meets the requirements of the project. Although the

wearables market is growing rapidly, those with open

interfaces tend to decline.

For this reason, another approach through self-

reporting methods is necessary to fully understand the

reasons why critical motivational incidents arise in e-

learning. This approach was particularly addressed in

this paper in order to be able to offer highly accurate

adaptation in the future and to estimate whether

adaptation can yield to better learning outcome.

In following steps of the project there should be a

shift within the acquisition of motivational data from

self-reports towards unobtrusive physiological

measures via suitable wearable sensors requiring a

functioning mechanism of machine learning-based

pattern recognition. Due to the analysis of studies

performed by another project partner in the SensoMot

project, it will be possible to identify suitable

physiological indicators for motivation. This will

then allow the selection of a wearable that allows the

measurement of this indicator. Moreover, improving

the NanoTecLearn platform regarding critical

motivational incidents and their reasons as well as

designing the adaptation and evaluating its effects on

learning motivation is of central scope.

In the long run, the overall objective is to increase

learning success through a higher learning motivation

in adaptive e-learning environments and to lower

dropout rates in engineering science.

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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