Providing Personalised Recommendations of Critical Incident Narratives
in a Cross-platform Mobile Application
Tim Wenzel
, Doris Fetscher
, Wolfgang Golubski
, Susanne Klein
and Rainer Wasinger
1 a
Faculty of Physical Engineering/Computer Sciences, University of Applied Sciences, Zwickau, Germany
Faculty of Applied Languages and Intercultural Communication, University of Applied Sciences, Zwickau, Germany
Personalisation, User Modelling, Mobile Applications, HCI, Prototyping, Critical Incident Narratives.
This work describes the design and implementation of a cross-platform mobile application that has been cre-
ated to provide users with personalised recommendations of Critical Incident (CI) narratives. CIs provide
brief descriptions of situations in which misunderstandings arise as a result of the cultural differences of the
interacting parties. They are useful for increasing intercultural awareness. This paper describes the design and
implementation of the mobile application called ‘Nils2Go’. The main focus is the personalisation strategy that
is employed in the recommendation of CIs, and the identification and use of user demographic characteristics
that can be utilised to further personalise a retrieved list of CI narratives.
Various studies show that the use of mobile applica-
tions often drops sharply within the first few weeks
and months after installation. For example, (Yan and
Chen, 2011) show how the average usage drops by
about 50% over a period of three months. Although
also dependant on the type of application (e.g. com-
munication applications are usually used for signifi-
cantly longer than games that are used exclusively for
entertainment (Li et al., 2020)), ‘user experience’ has
been shown to influence long-term application usage
(Kujala et al., 2011).
This paper focuses on the question “How can CI
recommendations in a mobile application be person-
alised?”. This is important because personalisation
has been shown to lead to an improvement in user ex-
perience in the past. To achieve this in the context of
a mobile application, a user model is created, and per-
sonalisation in the form of a recommender system is
carried out to present users with a list of relevant CIs.
This paper starts with a definition of Critical In-
cidents. Following this, Section 2 outlines the back-
ground concepts of personalisation, user modelling,
and recommender systems, and also provides an out-
line of the Network Intercultural Learning and Sensi-
tivity (NILS) website. In Section 3, the low-fidelity
prototype and the implemented mobile application
called Nils2Go
are presented, as too the personal-
isation strategy and technical implementation of the
application. Finally, in Section 4, our conclusions and
directions for future work are provided.
1.1 Critical Incidents
Critical Incidents (CIs) are brief descriptions of situ-
ations in which a misunderstanding, problem, or con-
flict arises as a result of the cultural differences of the
interacting parties (Apedaile and Schill, 2008). CIs
form an important tool for increasing our awareness
and understanding of human attitudes, expectations,
behaviours, and interactions. Some examples of CIs
are provided in Figures 1, 2B, and 3.
In addition to the narrative, different metadata
about the CI is also stored. For example, it can be rele-
vant where the information came from, who recorded
it, and who the actors are. A reflection (i.e. a retro-
spective view of the past situation) by the author from
today’s perspective, is also often included in a CI.
The mobile application described in Section 3 in-
corporates both the CI narrative and its associated
metadata into the presentation of CIs to the user. This
metadata is of decisive importance in the personali-
sation of the CIs that are returned to the user and is
described in more detail in Section 2.4.1.
Nils2Go App: Available at and
Wenzel, T., Fetscher, D., Golubski, W., Klein, S. and Wasinger, R.
Providing Personalised Recommendations of Critical Incident Narratives in a Cross-platform Mobile Application.
DOI: 10.5220/0011528400003323
In Proceedings of the 6th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2022), pages 137-144
ISBN: 978-989-758-609-5; ISSN: 2184-3244
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
In this section, the concepts of personalisation, user
modelling, and recommender systems are outlined, as
too the NILS website. These concepts form the basis
on which this work is then built upon.
2.1 Personalisation
There are various approaches to increasing the user
experience in the area of mobile applications. One of
these approaches is the personalisation of the software
used. In this context, personalisation involves assign-
ing relevant characteristics to individual users. Based
on these characteristics, different users will have dif-
ferent experiences when using the software. The ex-
periences are adapted to the preferences and needs of
the user. The work in this paper focuses on how per-
sonalisation can be used in the presentation of Critical
Incident narratives.
A sub-area of the personalisation of software is
represented by so-called recommendation systems.
These systems suggest specific information to the user
based on matches to his or her personal interests. In
general, personalisation deals with the discrepancy
between general-purpose applications, which achieve
the highest possible benefit in a broad field with the
least possible development effort, and specialised ap-
plications, which should fulfil an individual-specific
benefit (Kuo, 2013).
2.1.1 Advantages and Disadvantages of
The development of personalised applications has a
number of advantages. One clear advantage, espe-
cially for software development companies, is driven
by finance. In (Gavril and Ionescu, 2017), the authors
demonstrate that personalised applications generate
16.5% to 24% higher revenues. Another advantage
that concerns the end user is the increase in user satis-
faction. In (Liang et al., 2006), the authors show that
personalisation, especially in the form of recommen-
dation systems, has a positive effect on user satisfac-
tion. The authors in (Tong et al., 2012) also demon-
strate the positive influence that personalisation has
on user satisfaction in software systems. Another
positive aspect is that personalised applications often
stand out from non-personalised applications (Arora
et al., 2008).
In addition to the advantages mentioned, there are
also various disadvantages. The disadvantages in-
clude the higher development costs and the increased
complexity of the development of an application, but
these are often offset by higher revenues (Blech-
schmidt et al., 2005). Another possible disadvantage,
especially for the end user, is data privacy, as personal
data is often collected and used for user modelling
(Blechschmidt et al., 2005). It is however often the
case that the positive aspects outweigh the negative
ones. In the Nils2Go application described in this pa-
per, user data that is collected never leaves the device.
2.2 User Modelling
Whereas personalisation is the process of providing
individually relevant and interesting information for
individual users of an application, user modelling
refers to the construction of a User Model (UM) that
incorporates user-specific data. This model is gener-
ated and managed by means of corresponding soft-
ware components. The collected and managed data
can refer to the demographic characteristics of the
user being modelled. Furthermore, contextual data of
the user can also be relevant. The resulting model re-
flects assumptions of the system regarding the mod-
elled user and can often be retrieved in the form of
a user profile. Depending on the application, such a
model can also be designed for reuse in other systems
(Kay, 1998).
Examples of user model data relevant to the
Nils2Go application that is described in this work in-
clude demographic data (e.g. age, gender, countries
of interest, spoken languages, and country of resi-
dence), general pre-defined topics of interest that the
user can select from (e.g. education, food, family,
holidays, and travel), and specific interests provided
in the form of user-defined keywords. This data is
shown in Figure 5D.
In (Vu and Proctor, 2011; Hothi and Hall, 1998),
the following types of User Model are outlined:
Static UMs: This is the simplest type of UM.
Once the necessary data and information has been
provided, the UM remains unchanged for entirety
of the application’s remaining use. Changes in
user interests and preferences are not registered
or updated in the model.
Dynamic UMs: Dynamic user models can ac-
commodate for changes in a user’s interests and
preferences. In this way, there is always an up-to-
date model of the user. Furthermore, a user’s in-
teractions with the application can also flow into
the model. Depending on the use-case, it is also
possible to manually adjust the model.
Stereotype-based UMs: Stereotype-based UMs
enable a much more anonymous way of generat-
ing and maintaining user models. Here, the inter-
ests and characteristics of the individual user are
CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications
not part of the modelling. Rather, users are clas-
sified into common stereotypes. General informa-
tion about these groups is then used to generate
preferences for the user. The basis for these as-
sumptions are primarily driven by statistics that
have been compiled for the respective groups of
Highly Adaptive UMs: Highly adaptive UMs
realise the counterpart to stereotype-based mod-
elling. Here, great amounts of information about
a specific user are collected and generated, such
that almost all anonymity of the user is lost. One
advantage of this UM is that user profiles are ex-
tremely specific and accurate. As a result, cor-
responding applications can be particularly well
adapted to the current user and the user receives
the best possible user experience.
Hybrid UMs: Hybrid UMs attempt to combine
the advantages of both static and dynamic UMs,
such that a balance is found between good perfor-
mance with low memory requirements and good
personalisation with detailed user models. Often,
as is the case in (Billsus and Pazzani, 2000), the
UM consists of sub-models that take both the cur-
rent point in time as well as a longer period of
time into account. This allows information about
a user to be collected in order to first generate a
static user model, while also allowing for dynamic
changes in the interests over a longer period of
As described later in Section 3.4 the mobile ap-
plication in this work incorporates a hybrid UM ap-
2.3 Recommender Systems
In (Melville and Sindhwani, 2010), a Recommender
System is defined as a software system that provides
relevant and meaningful recommendations or sugges-
tions of different products or information items to a
number of users who may be interested in them. Rec-
ommender systems usually make use of either col-
laborative filtering, contextual filtering, or a combi-
nation of both. For the purpose of brevity, the reader
is directed to (Aggarwal, 2016) for further details on
recommender systems. The mobile application de-
scribed in this work incorporates a content-based fil-
tering approach as is described in Section 3.4.
2.4 NILS: Network Intercultural
Learning and Sensitivity
Network Intercultural Learning and Sensitivity
(NILS) is a research project of the University of Ap-
plied Sciences Zwickau. The work focuses on a sci-
entifically innovative preparation of critical incident
narratives (Fetscher and Klein, 2020). In addition
to gaining new insights into intercultural communi-
cation, the aim of the project is to sensitise users by
helping them to question cultural stereotypes. It is as-
sumed that this goal can be achieved through the in-
teractive browsing of CIs (Fetscher and Klein, 2020).
The NILS website
has been primarily designed
for research, teaching, and learning purposes. It al-
lows researchers (as well as other registered users)
to enter CI narratives into the system. It also al-
lows users of the website to retrieve CIs and filter the
CIs based on keyword searches. There are currently
around 200 CIs available through the website.
Data entry and retrieval of CI narratives is accessi-
ble via the project’s website, with the work described
in this paper providing the missing link to further view
CIs in a personalised manner via a cross-platform mo-
bile application that has been developed for Android
and iOS smartphones and published on the platforms’
respective App store fronts. An example CI narrative
from the website is shown below in Figure 1, while
Figures 2B and 3 in Section 3 show further example
narratives available through the mobile application.
Figure 1: NILS website illustrating a search for relevant CIs
based on the keyword “Restaurant”.
NILS Website:
Providing Personalised Recommendations of Critical Incident Narratives in a Cross-platform Mobile Application
2.4.1 Critical Incident Metadata
Each CI has up to 105 attributes associated with it.
These attributes can be grouped into the categories:
textStory, origin, medium, media, hotspots, contact
domains, communication domains, reflection, author,
actors, and location. Each of these categories has mul-
tiple attributes associated with it, e.g. a textStory in-
cludes the narrative (labelled as textStory.story in the
dataset) as well as attributes depicting whether it has
been transcribed, the language it is written in, and de-
tails about the author.
Some of the more important attributes that are col-
lected for each CI include an ID, the title and the ac-
tual story text, a timestamp of when the data was cre-
ated, the origin (i.e. whether the story is a personal
experience, an observation, retelling, or hearsay),
the language in which the content is recorded, the
kind of data (i.e. whether it is primary data or
secondary/edited data), the narrative perspective (i.e.
first or third person), the location of the event, the ac-
tors to whom the narrative refers, and personal data
about the author. Authors also have the ability to re-
flect on their experience by including a reflection at a
later time. Authors can also provide hotspots in the
form of free-text keywords to describe features of the
CI (e.g. humorous, historical).
In general, the CIs can be written in any language.
In addition to German, the current set of CIs con-
tain some narratives in languages such as English,
French, Spanish, and Russian. The actors in the set of
CIs come from various European and non-European
countries and belong to different age groups.
3 Nils2Go MOBILE
3.1 Purpose and Functionality
The mobile application described in this paper has
two main purposes. Firstly, it should allow for the
clear presentation of CI narratives included in the
NILS dataset. Both the actual CI story as well as
relevant metadata (see Section 2.4.1) should be made
available to the user. Secondly, the mobile application
should offer the possibility to personalise all existing
CI narratives within the given framework. In this way,
relevant elements classified as interesting for the user
are selected from the total amount of data (though still
allowing the user to access other CIs via a simple tog-
gle button as shown in Figure 5A).
In contrast to the website, the mobile application
has been kept deliberately simple and lightweight. It
does not provide the ability to add new CIs, and nei-
ther does it allow users to create an account from the
mobile device (which would be required to add new
CIs). Instead, it has been designed to present CIs
quickly and efficiently, and in a manner that is per-
sonalised to a given user’s profile.
3.2 Low-fidelity Prototype
The mobile application was developed using a
User-Centred Design (UCD) approach (Norman,
2013). This process was combined with a semester-
long course at the University of Applied Sciences
that focused on the creation of mobile ap-
plication prototypes for the search and retrieval of
CIs. In particular, the course focused on HCI prin-
ciples in which 13 groups of students designed low-
fidelity prototypes in Balsamiq
and iteratively tested
their designs, first in a formative and later in a sum-
mative manner with 3-5 participants each time. The
evaluation technique used for this process was the
think-aloud technique (Shneiderman, 2016). These
low-fidelity prototypes were used as the starting point
for the design of the current mobile application, which
then additionally incorporated the concept of person-
alisation as shown in Figure 2, with the ability to se-
lect thematic interests (A) and to browse a list of per-
sonalised CIs (B).
Figure 2: Low-fidelity prototype of the mobile application
showing a user’s thematic interests (A) and a personalised
list of CIs (B).
Balsamiq, URL:
CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications
3.3 High-fidelity Mobile Application
The mobile application has two main functions. The
first is to provide access to the list of CIs. These CIs
are packaged locally with the mobile application on
compilation. This means that there is no reliance on
the Internet or the NILS website to access the CIs.
The set of CIs included with the mobile application
are however only a subset of the total available to reg-
istered users of the NILS website. Updates to the in-
cluded list of CIs is accomplished via application up-
dates to the respective App store fronts. Figures 3A,
3B, and 3C show the overview list in which CIs are ar-
ranged vertically in an abbreviated form. This means
that for each CI, the title and the first three lines of the
actual story are visible. Furthermore, the creation date
for each narrative, as well as an information-button
that shows the related metadata is provided. To see
the complete story, the user simply selects the ‘show
more’ button. Feedback is also provided (when in the
personalised mode) to indicate if the match is due to
‘Interest Theme’, ‘Keyword’, or ‘Demographic char-
acteristic’ (Figures 3A, 3B, and 3C).
In addition to the CI narrative, the user also has
access to the metadata associated with each CI (Fig-
ure 3D), which the user can swipe through via a right-
to-left swipe gesture (in this case to see details on the
CI’s Hotspots - Figure 3D top-left) and via a left-to-
right swipe gesture (to see details on the CI’s Author
- Figure 3D top-right). In this view, all of the CI’s ad-
ditional metadata is shown. This information is pre-
sented separately to the main story so that it does not
impact on the readability of the CI narratives in the
overview page.
Also visible in Figure 4 is the ability to view
favourited CIs and to sort CIs, both alphabetically,
and via the manner in which CIs were selected as be-
ing relevant, i.e. based on the ‘Field of interest’, ‘De-
mographic Features’, or ‘Keywords’.
The second function is related to the personalisa-
tion of the retrieved list of CIs. The information con-
tained in the user profile is decisive for the personal-
isation of the application and contains important data
collected about the user.
Figure 5B shows a multi-choice set of topics that
is provided to the user during their first use of the ap-
plication. This allows the application to determine the
user’s thematic interests for the purpose of personal-
isation. The user also has the ability to skip this pro-
cess and configure their profile at a later time via the
application’s side-menu. Also visible in Figure 5A
is the ‘Personalised View’ toggle-switch that allows a
user to see the CIs that are most relevant to them.
The user’s profile page is accessed via the side-
menu and is shown in Figure 5D. This provides a rep-
resentation of the user model, which is statically gen-
erated at the beginning and - if necessary - adapted
over the application’s useful life (see Section 2.2).
The sections on the user profile page are divided into
demographic characteristics and interests. The demo-
graphic characteristics list personal information about
the user including their age, gender, countries that
they affiliate with, spoken languages, and their cur-
rent location of residence. The ability to enter this
data is mainly provided to the user via pre-defined
drop-down lists that limit the amount of free-form text
that the application needs to interpret. The section on
interests provides predefined topics that the user se-
lected on first use of the application, as well as user-
defined keywords that can be provided in free-form.
At the bottom of the screen is also a button that al-
lows the user to delete all of the information that has
been gathered on him or her. If the user selects this
button, their intention to delete their profile must be
confirmed and the user model is subsequently and ir-
revocably removed from the application.
3.4 Personalisation in the Application
Section 2.2 outlined static, dynamic, stereotype-
based, highly customisable, and hybrid approaches to
user modelling. For the Nils2Go mobile application
described in this paper, a hybrid UM approach was
used. The user model is generated during the first use
of the application, similar to a static UM approach.
The user can then ‘manually’ adjust their interests and
demographic characteristics, which makes it possible
to update the UM without complex dynamic adjust-
ment functionalities. Users can voluntarily further
customise their UM with demographic data like age,
gender, and spoken languages.
Users can also decide not to provide such demo-
graphic data, but the list of returned CIs will then
be much less precise as the personalisation strategy
will not incorporate CI metadata based on the actors
and authors with similar demographic attributes to the
user. If the user does initially decline to provide de-
mographic data, they can still decide to add it later via
the application’s side-menu.
The recommender system used in this application
uses a content-based filtering approach. In particu-
lar, recommendations are implemented based on rules
specified in the software. These rules use the Lev-
enshtein string distance algorithm (Navarro, 2001) to
determine similarities between the user’s profile data
(Table 1, left) and the CIs (Table 1, right).
Using the data in Table 1 row 1 to illustrate, one
such rule finds similarities between a user’s thematic
Providing Personalised Recommendations of Critical Incident Narratives in a Cross-platform Mobile Application
Figure 3: The mobile application for Android, showing the user’s personalised list of CIs (A, B, C), and some of the CI
metadata (D).
Figure 4: Methods of sorting CIs, including both alphabetic
(A-Z) and personalised options.
topics of interest and the CI text story and reflection
text. Similarly, if a user provides details on the lan-
guage(s) that they speak, this will be used to help de-
termine relevant CIs based on the language spoken by
the actors, the author, and the language that the CI has
been written in (row 5 in the table).
From a technical perspective, the recommendation
of relevant CIs is based on the Levenshtein string dis-
tance algorithm. This is well suited to applications
in which the objective is to find matches for short
strings (e.g. our pre-defined themes or user-defined
keywords) in longer texts (i.e. our dataset of Criti-
cal Incidents). In this mobile application, Levenshtein
distance thresholds are used to adjust the relevance of
the returned CIs, i.e. if words are less than or equal to
the Levenshtein distance, the CI is more likely to be
deemed relevant to the user. From a code perspective,
Table 1: Recommender system data. Demographic data is
marked with an asterisk (*).
User Profile Critical Incidents (CI)
+ Metadata
Thematic Interests Text story, Reflection
Individual Keywords Title, Text story,
Age* Age (Actors, Author)
Gender* Gender (Actors, Author)
Languages* Language (Actors,
Author), Language (CI)
Countries* Nationality (Actors,
Author), Country (CI)
Place / Region* Place (CI), Region (CI)
this is represented by the following variable:
In addition to the Levenshtein distance threshold
(which can also be adjusted by the user as shown in
Figure 5C), CIs are also determined to be relevant
based on the user’s demographic data (when avail-
able). As outlined in (Beel et al., 2013), the use of de-
mographic data can have a significant impact on the
success of the recommender system. In our mobile
application, it is a combination of several matching
demographic characteristics that is required for a CI
to be marked as being relevant based on the user’s de-
mographic data (Figure 5C).
3.5 Technical Implementation
In this section, we outline the framework and tech-
nologies that were used to build the cross-platform
mobile application.
CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications
Figure 5: The mobile application for Apple iOS, showing the side-menu (A), the user’s thematic interests (B), personalisation
settings (C), and the user profile (D).
For the technical implementation of the mobile
application, the open source framework Flutter
used in conjunction with the programming language
. Flutter is a portable UI toolkit developed
by Google for the development of natively-compiled
applications for mobile platforms (iOS, Android) as
well as desktop (MacOS, Windows, Linux) and web
(Web Apps) (Flutter, 2022). Most importantly, this
means that the framework offers the ability to develop
applications once and then make them available na-
tively on different platforms.
Cross-platform development technologies like
Flutter and Dart allow developers to reuse the same
code across multiple platforms. They also provide ac-
cess to native features of the smartphone, including
local storage, which is relevant for this mobile appli-
The Nils2Go application is based on Flutter v3,
which was released in May 2022. According to a
2022 survey by Statista of over 31,743 software devel-
opers (Vailshery, 2022), the use of the Flutter frame-
work by software developers increased to 42% be-
tween 2019 and 2021 (an increase of 12%). Flutter
is thus now already far ahead of competing frame-
works such as React Native (used by 38% of software
developers in the survey), Cordova (used by 16%),
Ionic (used by 16%) and Xamarin (used by 11%).
Flutter, URL:
Dart, URL:
In comparison to web-based cross-development plat-
forms like React Native, Flutter uses its own high-
performance rendering engine rather than web tech-
nology. The Flutter engine is written in C/C++ and
applications written in the Dart programming lan-
guage are compiled into native code Ahead-of-Time
(AoT) for both iOS and Android
. This focus on
performance means that Flutter applications can run
faster than the competing cross-platform technolo-
A comparison of Flutter applications written in
Dart compared to native Android applications written
in Kotlin and iOS applications written in Swift shows
that there is still a performance penalty when using
Flutter (Olsson, 2020), but the advantage of having
a cross-platform application outweighs the disadvan-
tages for the purposes of this work. In (Olsson, 2020),
it is further stated that from their survey of 39 people
from the IT industry, 74% of end users could not de-
tect the difference in look and feel between a Flutter
and a native mobile application, which provides even
more incentive to use Flutter/Dart.
This work described the design and implementation
of the Nils2Go cross-platform mobile application that
Flutter FAQ, URL:
Providing Personalised Recommendations of Critical Incident Narratives in a Cross-platform Mobile Application
has been created to provide users with personalised
recommendations of CI narratives. The focus was on
the personalisation strategy which was demonstrated
using a hybrid user model and a content-based recom-
mender system that incorporated the Levenshtein dis-
tance as well as demographic user data (when avail-
able). Future work will now be to conduct a user-
study with the mobile application to see if the em-
ployed personalisation strategy does in fact lead to an
increase in user experience for its users as has been
shown by other past research.
A special thank you goes to the PTI06970 HCI stu-
dents (Alec Sichenender, Jonas Langner, Justin Nt-
zold, Konrad Platz) and PTI06640 Mobile Applica-
tions students (Kenny Musterer, Felix Schober, Do-
minik Strer, Tommy-Lee Bannert) at the Westschsis-
che Hochschule Zwickau, who worked on earlier pro-
totypes of this application. Special thanks also go to
the NILS development team (Tobias Haubold, Wolf-
gang Grs, Peter Huster, and Dariya Kapinus) and to
our graphic designer (David Mantilla), who provided
the colour schemes, graphics, and icons.
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CHIRA 2022 - 6th International Conference on Computer-Human Interaction Research and Applications