=Paper=
{{Paper
|id=Vol-1238/paper4
|storemode=property
|title=Application overlapping user profiles to foster reflective learning at work
|pdfUrl=https://ceur-ws.org/Vol-1238/paper4.pdf
|volume=Vol-1238
|dblpUrl=https://dblp.org/rec/conf/ectel/FesslWL14
}}
==Application overlapping user profiles to foster reflective learning at work==
https://ceur-ws.org/Vol-1238/paper4.pdf
Application overlapping user profiles to foster
reflective learning at work
Angela Fessl, Gudrun Wesiak, and Granit Luzhnica
Know-Center
Inffeldgasse 13
A - 8010 Graz
(afessl, gwesiak, gluzhnica)@know-center.at
Abstract. Reflective learning is an important activity of knowledge-
workers in order to improve future working-behaviours. The insights
gained by reflective learning are based on re-experiencing and re-evaluating
past working situations. One time- and cost-effective way to support re-
flective learning is the employment of applications that collect data about
working processes, store the data in user profiles, and visualise it in order
to provide timely feedback to the employees. However, a single applica-
tion can only capture part of the data that might be relevant for reflec-
tion and the parallel use of several applications leads to high demands
on the user regarding the interpretation of relationships between sev-
eral single visualizations. A combined visualisation of data captured by
different apps should enhance the support for reflection about the work-
ing behaviour and experiences. This paper introduces an overlapping
user profile application, which combines and aggregates data captured
by various applications. The goal of this overlapping application is to
provide higher-level reflection possibilities by combining visualisations of
different application data in order to better induce and support reflective
learning at work. A first proof-of-concept of such an approach indicates
that a combined user profile application and especially it’s visualisations
can be beneficial with regard to reflective learning and can enhance the
awareness about the multiple aspects of a user’s work life.
Keywords: Work-place learning, reflective learning, awareness, user pro-
files, reflective data analytics
1 Introduction
Today’s work environments are constantly becoming more complex, globally
integrated, and knowledge-centric. This simultaneously leads to a stronger need
of employees who are motivated and capable to reflect upon their activities and
as a consequence adjust their working practices to new demands. Especially for
knowledge-workers, reflective learning is an important activity to re-experience
past situations during work and to learn from them in order to improve their
future working-behaviour [2]. One possibility to motivate knowledge workers
to become reflective practitioners is to support them with corresponding tools
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or applications, which could be easily integrated into their daily work-life [14].
These applications have the task to gather data from work processes and to
provide guidance for reflection in form of raising awareness and offering triggers
with regard to unusual or extraordinary work related experiences or situations.
In contrast to formal learning settings, reflective learning at the workplace deals
with informal and self-regulated learning, where challenges like no additional
working effort, easy integration in daily working routines as well as a clear benefit
for knowledge workers have to be considered right from the beginning.
In order to support reflective learning at work, within the EU-funded project
MIRROR (http://www.mirror-project.eu) several applications have been devel-
oped, which aim at motivating and activating users to reflect upon their indi-
vidual working experiences. After the reflection process itself, knowledge work-
ers should have gained some benefits or insights for themselves and as con-
sequence derive and apply behavioural changes for future working situations.
These changes should permanently improve and facilitate the handling of up-
coming similar situations or experiences.
The applications developed within the MIRROR project have been applied
within a wide range of working environments (e.g. care homes, hospitals, IT com-
panies, and emergency situations) and support various sets of professionals (e.g.
knowledge-workers, nurses, physicians and carers as well as emergency workers).
Each of the developed applications collects and gathers different kinds of data
and stores them in their corresponding user profiles. This data encompasses on
the one hand information about the user. On the other hand it consists of in-
formation on users’ work processes, which is captured automatically or inserted
manually during the user’s work. Examples are application switches, application
usage and documents used while working on a PC as well as manually inserted
data such as the current mood status of the user, individual notes, feedback
on different working situations, ratings, scores of serious games or quiz results.
The collected data is stored within the applications themselves and for some
applications additionally in the so-called MIRROR Spaces Framework [20], an
underlying data storage system for exchanging data between applications. In the
spaces framework, the user’s data is stored in the user profile and is accessible
only by its owner. Each of these single applications visualises the data for the
user in a sophisticated way with the goal to trigger reflective learning. However,
user studies conducted in different environmental settings (e.g. [9], [16]) showed
that single applications can only capture part of the data that might be relevant
for reflective learning. Participants of these studies asked on the one hand for a
better guidance to interpret the data in order to initiate reflection. On the other
hand they wanted to see a clearer benefit for themselves, which would serve as
motivational trigger to use the application and to reflect about the captured
data. Thus, similar to research outcomes from the field of learning analytics, we
found that a combination of data is often more adequate for successfully support-
ing users. Whereas learning analytics addresses self-reflective learning mostly as
important aspect of self-regulated learning in formal learning environments, we
focus on work-related reflective learning in informal learning environments.
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With this paper we want to present a first approach on how to meaningfully
combine and visualize data captured by different applications. The goal is to
provide a greater variety of reflective learning opportunities in order to facilitate
deeper insights on one’s working experiences. We are aware that this approach
raises privacy and security issues which need to be carefully considered when
employing the app in a real working environment. However, for this first approach
privacy and security were only of secondary interest, but will of course be treated
in upcoming research settings.
Therefore, we developed the so-called ”MIRROR Integrated User Profile”
application (MUP App) which has the task to integrate, summarise, analyse,
and visualise data captured by several different applications in order to induce
and support reflective learning at work. For a first proof-of-concept, we used
two different applications in parallel, namely KnowSelf and the MoodMap App.
KnowSelf automatically captures work activities on a PC, whereas the MoodMap
App allows knowledge workers to easily state their moods during a working day.
We collected, aggregated, and visualised data from a small sample of knowledge
workers to to get a first impression of users’ interest and motivation and the
app’s usefulness. From this we derived the following three research questions:
– RQ1: Are participants interested and willing to use more than one applica-
tion in parallel with regard to reflective learning?
– RQ2: Does the MUP App as overlapping application facilitate reflection
about users’ working experiences and contribute to raising awareness of mul-
tiple aspects of their work life?
– RQ3: Do participants perceive any individual insights or benefits for them-
selves?
2 Related Work
2.1 User Profiles
Since the terms user profile and user model are not always used in exactly the
same way, it is essential to clarify our understanding and usage of the term user
profile, which we base on existing theories regarding user models and on our un-
derstanding in MIRROR described by [15]. User models in general are models
that computer systems have about their users. The data in such user models is
automatically captured by the system and is mainly used in information retrieval
and intelligent tutoring systems or user-adaptive learning systems (see e.g., [10,
1]). User models, which are utilised in learning environment systems for mod-
eling the learner and the corresponding learning activities, are called learner
models. These types of user models are created by the systems automatically
and are not directly accessible by the users via user interfaces. Furthermore
they are used to adapt teaching strategies or to inform the learner about the
learning progress as basis for reflective learning. Additionally [3] suggested that
learner models should keep data like knowledge, interests, goals, background,
and individual traits, thus abstract concepts relevant for learning. In order to
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apply a user model or learner model as basis for reflection on one’s own learning
activities, achievements, or progress towards the individual learning goals, it is
necessary to make the models accessible and manageable for the user, which was
explicitly suggested by [12] and mentioned in [4, 5, 13].
In MIRROR we prefer the term user profile (UP). Although the MIRROR
user profile (MUP) is based on theory and research of user models, the term user
profile better reflects its mission in MIRROR. First, the purpose of the MUP is
to guide and support reflection by mirroring user data in the form of activities,
experiences or artefacts of work, notes and insights, moods, work practices, and
other concrete data sources back to the user. Secondly, we intended these user
profiles to be created and maintained by a mixture of automated methods and
manual management, where the process of editing or updating the data may also
explicitly trigger reflection.
2.2 Learning Analytics
Although learning analytics is not in focus of our work, several approaches,
methodologies and technologies of this research area are closely linked to reflec-
tive learning. Learning analytics deals with methods for analysing and detecting
patterns within data collected from educational settings or learning environ-
ments about the learner, and leverage those methods to support adaptation,
personalisation, recommendation, and also reflection. Siemens [21] defined learn-
ing analytics as ‘the use of intelligent data, learner-produced data, and analysis
models to discover information and social connections, and to predict and advise
on learning’. The focus of learning analytics is on the support of the learner in
formal learning setting, while in our work the focus is to support the knowl-
edge worker in an informal learning setting. Nevertheless, the parallel to our
work is evident. Also approaches like learning dashboards for example described
in [8, 19] present an overview of the learner’s own learning activities and learning
progress, and in relation to colleagues at one glance. Such combined visualisa-
tions support self-monitoring for learners and awareness for teachers as well as
empowers the learners to reflect on their own activity, and that of their peers.
Explicit traces (e.g. the learner’s entries in a chat or a discussion forum) and im-
plicit traces (e.g. the learner entering a course or clicking on a document) stored
in the corresponding learner profiles serve here as basis for the aggregation and
visualisation of the gathered data.
The main focus of learning analytics is to support the learner while learning
in an educational setting or learning environment. Although learning analytics
includes also reflective learning approaches (e.g. [18, 17]), our work can be clearly
distinguished from these approaches by focusing on knowledge workers in real
working environments and and to support reflection on working experiences or
working artifacts in order to learn from them to improve future work.
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2.3 Reflective Learning
Individual reflection takes normally place in every day’s life and obviously also
during work or work- related situations. Reflection may be triggered by different
reasons for example by conflicts or problems, by unexpected experiences or by
a person acting in a complete different way in comparison with the individual
(external trigger). But also if an individual feels uncomfortable, for something
bothers her or an inner voice is nagging, without being able to make this feeling
external (internal trigger). As reaction, a reflection process may be triggered with
or without the awareness of the person. This reflection should lead to an individ-
ual insight or outcome which may be used to guide or adapt future behaviour.
Within MIRROR we follow the definition of [2], who define reflective learning as
‘those intellectual and affective activities in which individuals engage to explore
their experiences in order to lead to new understandings and appreciations’.
Bringing this together, reflection is both a crucial part of learning and a re-
sponse to past work experiences. These experiences as well as the behaviours of
the individual engaged serve as starting point for the reflective process. The de-
sired outcomes of reflection may lead to personal synthesis, integration of knowl-
edge (internalisation), validation of personal knowledge, a new affective state or
the decision to take on actions for future events. To achieve these results the
characteristics of the individual (learner) have to be taken into account as well
as the intention of the individual self. Individual reflection may occur sponta-
neous and unconsciously and in any possible situation especially then when it is
not expected [6]. Of course it can also be consciously triggered by peers super-
visors or by meeting created specifically for that purpose [7]. Within MIRROR
we focus to initiate reflective practices with the support of technologies, which
might automatically detect unusual working patterns and working behaviours
and by making the worker aware of them in form of reflection triggers or explicit
reflection guidance e.g. by means of prompts.
3 Examples of MIRROR Applications
In the scope of the MIRROR project a series of applications supporting indi-
vidual reflection have been developed and evaluated in different settings [16, 9].
Some of the user studies showed that gathering and visualising data captured
by single applications is not always enough to initiate reflective learning. To il-
lustrate how the MIRROR applications support reflective learning, we want to
shortly introduce two applications, namely KnowSelf and the MoodMap App.
The same two applications will later be used as example for a possible combined
usage and integration via the MUP App.
KnowSelf automatically captures work activities (used applications and
resources together with the exact time of use) on a PC, provides simplistic project
and task recording and presents an overview as well as different visualisations
of the captured data [16]. Providing these visualisations regarding time use at
work should lead to reflection on personal time management and potentially
motivate to consider improvements in this respect. The user profile of KnowSelf
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Fig. 1. KnowSelf (left) and MoodMap App (right)
is not a conventional user profile, because it consists only of user activities, but
not of information about the users themselves. The application stores all work
activities captured on the user’s computer, including window focus and title, if
applicable the system location (path) of the resource, focus switches, and idle
time. Additionally the user can manually record time spent on projects or tasks
and save observations. The collected information is displayed on a timeline and
as statistics in the form of pie charts.
The MoodMap App is a web-based application, which allows knowledge
workers to track their mood during a working day or virtual meeting and recapit-
ulate their work experiences afterwards. The MoodMap App provides an easy-
to-understand user interface to state individual mood points by simply clicking
on a bi-dimensional coloured map. Each mood is composed of two dimensions,
namely valence (negative to positive feelings) and arousal (low to high energy)
based on the model proposed by [11]. Additionally, it provides several visualisa-
tions on an individual as well as collaborative level, to make users reflect on the
mood development over time or to provide easy comparison possibilities of one’s
own mood with the mood of others for example colleagues or team members
of the same team. The application related user profile stores information about
the user, sharing settings for security and privacy issues as well as individual
email settings. Furthermore, individual moods and inherent notes, correspond-
ing meetings, context information of a day or meeting, as well as personal diary
entries are stored in the internal user profile of the application.
4 The MIRROR Integrated User Profile (MUP)
Insights from evaluations conducted separately for KnowSelf and the MoodMap
App led to the conclusion that single applications capture only part of the data
that might be relevant for reflection [9, 16]. Although the developed applications
proved to have high potential to trigger reflection at work, we wanted to go
one step further. As a first step, we made triggers from different sources easily
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Application overlapping user profiles to foster reflective learning at work - ARTEL14
accessible to the users, in order to further facilitate the reflective learning experi-
ence and help users to get more insights at one glance. Thereupon we developed
the MIRROR User Profile (MUP) concept, which focuses on the combination
of the captured data and corresponding sophisticated visualisations. An early
prototype of the MIRROR User Profile Application (MUP App) was realised
and tested with a small sample of knowledge workers.
4.1 Prerequisites for the MIRROR User Profile
In order to efficiently implement a common MIRROR user profile, it is of crucial
relevance to use data captured and gathered by several MIRROR applications
and not only by a single one. To achieve this, we employed the MIRROR Spaces
Framework, an underlying data storage system developed within the MIRROR
project to store and exchange data of the applications. For the development of
a common user profile based on the MIRROR spaces the following prerequi-
sites have to be considered: assumptions regarding (i) data, (ii) reusability, (iii)
sharing, (iv) privacy and security, and (v) accessibility by the user interfaces.
Data stored in the MUP can be divided into three different types, namely
personal data about the user, private data, and shared data. Personal data about
the user consists of general information about the user (e.g. name or email ad-
dress) and login information. For the data implicitly captured by the MIRROR
applications (e.g. work history in KnowSelf) as well as data explicitly inserted
by the user (e.g. mood in the MoodMap) it is essential that the user has full
control over her data by deciding for each type of captured data, whether it is
private or can be shared.
Reusability is one of the major potential benefits of the MUP. By storing
the data according to a predefined data format, applications are able to reuse
not only their data but the data captured by other applications and other users
as well. Account information can be stored once in the user profile and then be
used by all MIRROR applications.
Sharing data is of major relevance for reflection in order to provide possibil-
ities for comparing one’s own data with that of colleagues or a whole team. To
account for different levels of sharing, settings (e.g. anonymised, sharing within
the same team or department) should be very fine-grained.
As mentioned above privacy and security are a major concern when storing
data in the MIRROR Spaces Framework. It has to be ensured that the privacy
settings defined by the users via different applications are always met by all
applications, aggregations, and visualisations.
Sharing, privacy and security settings along with other data gathered either
explicitly or implicitly by applications, should be accessible and modifiable
by user-friendly interfaces and visualisations provided by each MIRROR appli-
cation. This has the advantage, that the user has full control about the data and
has the potential to decide on a very fine-grained level, which data she wants to
share with whom and which data should be kept private only.
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4.2 The MIRROR Integrated User Profile Application
The MIRROR Integrate User Profile Application (MUP App) serves as a bridge
between the MIRROR Spaces Framework and various MIRROR applications.
It provides services for data administration as well as for directly supporting
reflective learning. The latter is achieved by making users aware of unusual or
significant behavioural patterns. The MUP App’s service can be used by other
applications to show and promote reflective learning by presenting combined
data aggregated by different applications or from different users.
The tasks of MUP App are two-fold, providing (a) access to the data stored in
the corresponding user profiles per user within the MIRROR Spaces Framework
and (b) a data analysis service, which aggregates data from different applica-
tions (on an individual level) and/or from different users (on a collaborative or
organisational level). The aggregated data can be used to raise awareness on
relationships between data captured from different applications, make compar-
isons along a timeline or among different users, and finally detect patterns that
are relevant for individual or collaborative reflection. Reasons for reflection can
encompass the need for problem solving, decision-making, emotion regulation,
or detection of significant deviations between the individual user and a team.
In this first phase of the development we pursue a more general approach
directed towards basic types of data that are comparable across different ap-
plications. We mostly focus on statistical analysis to extract information on for
example the number of different applications used by an individual, on providing
a chronological overview of the applications used, on presenting the number of
entries in various diaries, and on general information (e.g. when, how often or
which data) was captured by each application. The data is presented on different
types of charts, which can be selected by the user in order to ensure that the
chart fits to the available data. In addition, the user may visualise her data in
direct comparison with the data to other users (e.g. her team-members).
For the second phase, we will be concentrating on the different types of
data captured by various applications, in order to provide analysis on the com-
bined data. For instance, combining the usage of the MoodMap App with data
captured by KnowSelf, might show a relationship between moods and specific
working tasks. This would lead to new insights that may be the basis for ini-
tiating reflective learning. As an example, the left of Fig. 2 shows the hourly
application usage history of a single user for both KnowSelf and the MoodMap
App. The picture on the right of Fig. 2 visualises combined application specific
data, namely the number of hourly switches between tasks or resources captured
by KnowSelf and the corresponding mood of a user, depicted as separate lines
for arousal (mood.energy) and valence (mood.feel) by the MoodMap App. For
this visualization, mood values from the MoodMap App depicted in Fig. 1 are
expressed as numbers between 0 and 100.
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Fig. 2. MUP application with MoodMap and KnowSelf data for a single user
5 Proof of concept
In order to investigate the potential of a common MIRROR user profile as sup-
port for reflective learning, we conducted a small combined user study employing
the KnowSelf and the MoodMap App in parallel. Although we used only two
application for this first evaluation, the MUP App is able to handle all applica-
tions that store their data in the common user profile. Based on what we have
learned from the separate evaluations, we see this study as first proof-of-concept
for the MUP App. The goal was to find out whether a combined analysis of data
from both user profiles will i) be accepted by the users, ii) enhance the boost of
reflective learning, and iii) provide clearer insights or benefits for the single user.
5.1 Setting
The participating team consisted of 6 knowledge workers (3 women, 3 men), on
average aged between 30 and 40, all of them mostly doing computer work. They
used the KnowSelf and the MoodMap App in parallel for two weeks during work.
Each day, either in the morning or in the evening they were asked to re-evaluate
and reflect about their captured data and write down their insights and thoughts
directly within one of the two applications. User activities automatically logged
by KnowSelf could only be analysed for 5 persons due to technical reasons on
one of the PC’s. At the end of the trial the participants were asked to fill in a
questionnaire and to take part in a semi-structured interview.
The questionnaire covered information regarding features and functionalities
of the applications, usage, and reflective learning. During the interview, com-
bined statistics (see Fig. 2) of the captured data were presented and discussed
in order to find out the insights and benefits gained for the individual user.
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5.2 Results & Discussion
The analysis of the log data of both applications is depicted in Fig. 3. Because
of the small sample size only descriptive statistics are presented. As measure
of central tendency the median is used for the same reasons. Each data point
represents the average mood values (in terms of valence and arousal) of one
participant in relation to the application usage and working activities (switching
frequency and used resources). Whereas there is no trend to be derived from this
small sample for the active use of KnowSelf, the number of moods entered per
day seems to increase with higher valence and higher arousal values indicated
by the participants (i.e. with a more positive mood). Switching frequency was
measured in seconds between switching from one resource to another. Fig. 3
(bottom) shows that higher reported valence seems to be connected to longer
times between switches (that is a lower switching frequency) and fewer resources
used. Interestingly, the arousal level increases with the number of used resources.
Fig. 3. Relationship between mood (as valence and arousal), application usage and
working activities
Analysing the data collected via questionnaires and interviews, we can give
first answers to the research questions:
RQ1: Are participants interested and willing to use more than one applica-
tion in parallel with regard to reflective learning? Ratings from 6 participants
answering the questionnaire (using 5pt. agreement scales) indicate that there
is an interest in getting support for time-management (Md (median) = 4) as
well as in capturing one’s working activities, own mood, and the team mood
(all Md =3.5). Participants found the applications easy to use, liked their visu-
alisations, rated the presentation of information as comprehensible (all Md =4),
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generally liked using the applications and would recommend them to colleagues
(for both items Md =4 for KnowSelf and Md =3.5 for MMA, respectively).
RQ2: Does the MUP App as overlapping application facilitate reflection about
users’ working experiences and contribute to raising awareness of multiple aspects
of their work life? The interview results revealed that the combination of data
has high potential to trigger reflective learning although we have ambiguous
statements in which way. One participant reflected mainly on the number of
used applications and its relation to how the level of arousal developed over the
day. Another participant mentioned that combined data helped her to detect a
working pattern, which occurred especially in the morning. After reading emails
the application switches and the arousal level increases, thus she knows that
she started to work. Similarly, one of the participants observed that her arousal
level is very low in the morning and increases during the day. This was a trigger
to compare her arousal level to the average level of her colleagues and reflect
upon eventual differences between them. An important feature mentioned by
more than one participant was the overlapping visualisation of captured data
on the timeline chart. Here the data was understood at one glance, which can
facilitate reflective learning and enhance awareness of the multi aspects of their
work life. Despite of the different approaches to reflect, for all participants the
combination of data captured by both applications was important to understand
the relationship between their working activities and moods.
RQ3: Do participants perceive any individual insights or benefits for them-
selves? Besides the findings already described in relation to RQ2, participants
reported some additional insights they gained by reflecting on the captured data
provided by the MUP. One participant stated that her arousal level fluctuates
during the day. By becoming aware of the falling arousal level she decided to
take smaller breaks to better recover during the day. Further insights concerned
participants’ self-estimations of how they spend their working day. Whereas one
participant stated that the captured data confirmed how she estimated the rela-
tionship between working activities and mood development, another participant
was rather surprised in the first place. Although she was six to seven hours in the
office she spent only four hours in front of her computer. Only after comparing
this awareness with her dates in her calendar, she could reproduce her day and
explain why this happened.
General discussion. In general, this first proof-of concept of the MIRROR In-
tegrated User Profile indicates that such overlapping visualisations can facilitate
individual reflective learning and raise overall awareness of users’ work life. All six
participants used the combined data to reflect on how their working activities are
related to mood changes and could gain some individual insights. Nevertheless
there a still some points which need further discussion. While KnowSelf captures
automatically the resources and applications used on the PC, the moods need
to be inserted manually. Having to repeatedly insert a mood in a web based
application can distract from the normal working process. One recommenda-
tion to alleviate this distraction was to add five different smileys in the system
tray to facilitate the mood capturing. Another point for consideration is the
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optimal time for reflection. All of the participants perceived the combination of
the data captured by the MoodMap App and KnowSelf as useful, because they
could check at all times what they were doing during work and how they felt.
However, one participant stated that it was not very useful to reflect on how
she felt three days ago, but that it was more interesting to become aware of
her mood in relation to her work directly while working. For other participants
especially the knowledge of how they felt for example three days ago was very
important. Especially when the mood could be directly related to the mood
note, used applications or used resources. A rather interesting statement from
one of the participants was that her working tasks did not influence her mood
at all. With respect to the visualisations, the interviews showed that different
types of aggregating the data would be useful, so that users could indicate their
individual preferences, e.g. to visualise the data along a timeline, to aggregate
on an hourly basis, or to offer a summarising view in form a pie chart.
In summary the MUP App provides new visualisations based on data cap-
tured by different applications, and therefore offers a multitude of new possibili-
ties for individual interpretations. In our proof of concept, we only combined data
of two applications, but also within this small setting we received different ap-
proaches on how the participants interpreted the captured data for themselves
and what they learned from it. We also mentioned some shortcomings which
must be taken into consideration when proceeding with the development of the
MUP App. Nevertheless, our findings encourage the assumption that combining
data of more than two applications, leads to more meaningful possibilities to
interpret the data and to gain more diverse insights for oneself.
6 Conclusions and Outlook
In this paper we presented the new MIRROR integrated User Profile Applica-
tion, which aims at supporting reflective learning at work. Based on the results
from previous user studies, which evaluated single applications, we derived es-
sential requirements for the development of the MUP App and implemented a
first prototype. Results from a first small evaluation regarding the parallel usage
of two applications indicate that combining data captured by different appli-
cations, analysing and visualising them together can further facilitate reflective
learning. Furthermore, it can also enhance awareness of the work life by leading
the users to get more diverse insights about themselves. Of course, after this first
proof-of-concept, user-studies with larger samples and more applications need to
follow. Thus, our future work will focus on the integration of further applica-
tions developed within the MIRROR project into the MUP App. The goal is to
provide different variants of visualising combined data and more sophisticated
ways to provide guidance for reflective learning. For example, Fig. 4 combines
KnowSelf data with corresponding geo location data captured by another MIR-
ROR App. Thus it makes you aware of your working activities in relation to
your working places (e.g. customer visits or travel activities) and can provide
more triggers for reflective learning.
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Application overlapping user profiles to foster reflective learning at work - ARTEL14
Fig. 4. Further sophisticated visualisations for the MUP App
7 Acknowledgement
The project “MIRROR - Reflective learning at work” is funded under the FP7
of the European Commission (project number 257617). The Know-Center is
funded within the Austrian COMET Program - Competence Centers for Ex-
cellent Technologies - under the auspices of the Austrian Federal Ministry of
Transport, Innovation and Technology, the Austrian Federal Ministry of Econ-
omy, Family and Youth and by the State of Styria. COMET is managed by the
Austrian Research Promotion Agency FFG.
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