stARS: Proposing an Adaptable Collaborative Learning Environment to
Support Communication in the Classroom
Tommy Kubica, Ilja Shmelkin, Robert Peine, Lidia Roszko and Alexander Schill
Faculty of Computer Science, Technische Universit
at Dresden, Dresden, Germany
Learning Environment, Audience Response System, Backchannel System, stARS.
The usage of technology provides a powerful opportunity to support classic classroom scenarios. In addition
to improve the presentation of a lecturer’s content, technical tools are able to increase the communication to
the students or between students. Although many approaches exist that are able to support such interactions,
the lecturer has to adjust his/her teaching strategy to the corresponding system. To overcome this problem,
our goal is to allow lecturers to create their personal scenarios in an intuitive manner. As a solution, we
propose an approach called stARS (scenario-tailored Audience Response System) that builds on top of a
uniform (meta-)model. It provides a graphical editor as a user interface to create customized application
models that represent teaching scenarios. In addition to classic Audience Response functions such as learning
or survey questions, collaborative functionality is provided specifically, group formations with associated
interactions within these groups (e.g., discussion or voting functionalities) are examined. In order to evaluate
our approach, in the first step, a user study was conducted to reason about the average user’s modeling abilities
with the graphical editor. Next, we target to evaluate both the functionality and the opportunities of our created
prototype in real-life scenarios.
In recent years, technology has increasingly found its
way into teaching. While technical tools that allow
to present content to the students are omnipresent, the
potential of technology to improve communication in
the classroom is still a subject of research. Although a
lot of investigations have been conducted in the past,
e.g., (Lingnau et al., 2003) or (Dragon et al., 2013),
they were limited to small, non-anonymous scenarios.
Current approaches such as Audience Response
Systems, Classroom Response Systems, or Backchan-
nel Systems overcome those limitations by targeting
to involve students anonymously using their personal
mobile devices. They allow students to answer pre-
pared questions or to ask their own questions during
the ongoing lecture that can be discussed with other
students. A variety of systems exist, e.g., as listed by
(Hara, 2016), (Meyer et al., 2018) or (Kubica et al.,
2019a). As we do not need to distinguish different
types of systems in this paper, we will use the generic
term learning environments from now on.
Although the usage of these systems provides
a lot of promising opportunities to improve classroom
teaching (Nikou and Economides, 2018), they
suffer from heterogeneity. Instead of implementing
their teaching strategy in mind, lecturers have to
adapt their strategy to the system’s limited functional
scope and it’s predefined settings, e.g., the number
of repetitions a student got to answer a question
Accordingly, our goal is to allow lecturers to
configure the system’s functionality to their personal
teaching strategy. As a solution, we present the pro-
totype of an approach called stARS
, which gives lec-
turers the opportunity to (1) adapt function blocks by
different parameters, (2) build sequences of function
blocks and add conditions in order to define differ-
ent learning paths, (3) link function blocks to coop-
erate and (4) support collaboration between students
by introducing novel function blocks for group forma-
tions and associated interactions. A user study will be
discussed, showing that users with different modeling
abilities are able to create customized scenarios. In
addition, the usage within realistic scenarios is mo-
tivated to show the applicability and opportunities of
our presented approach.
The running prototype is provided on: https:// (accessed 3/19/20).
Kubica, T., Shmelkin, I., Peine, R., Roszko, L. and Schill, A.
stARS: Proposing an Adaptable Collaborative Learning Environment to Support Communication in the Classroom.
DOI: 10.5220/0009489103900397
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 2, pages 390-397
ISBN: 978-989-758-417-6
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
The remainder of this paper is structured as fol-
lows. Section 2 presents related work while specif-
ically targeting the customization of functionality in
these systems. In section 3, we present preliminary
work on the (meta-)model and the base on the sys-
tem’s concept. Next, section 4 introduces an infras-
tructure and describes each part of it in more detail.
In addition, the opportunities provided for the lecturer
are motivated. Section 5 presents the results retrieved
from an initial user study and discusses the evalua-
tion within realistic scenarios. Finally, section 6 con-
cludes this paper and discusses the limitations and fu-
ture work to be done.
Current literature presents many systems that tar-
get the support communication in classic classroom
teaching, e.g., as listed by (Hara, 2016), (Meyer et al.,
2018), or (Kubica et al., 2019a). In order to adjust
the functional scope to the lecturer’s personal teach-
ing strategy, different approaches exist, which will be
presented in the following.
The most straightforward way to customize the
range of functions is to select specific functions. E.g.,
in Tweedback
, the lecturer can enable or disable
the features called “Chatwall”, “Quiz” and “Panic-
Buttons”. Furthermore, GoSoapBox
allows a more
fine-granular selection of the system’s functionality
by extending the selection of its core features called
“Barometer”, “Quizzes”, “Polls”, “Instant Polling”,
“Discussion” and “Social Q&A” by additional fea-
tures (or filters), namely a “Profanity Filter”, “Math
Formatting” and “Names Required”. In order to
help the lecturer to choose the system’s functional
scope, ARSnova
allows to select predefined use
cases. E.g., by selecting “Clicker Questions”, all sim-
ple question types such as Multiple Choice, Single
Choice, Yes – No, Likert Scale and Grading are en-
abled. Furthermore, (Kubica et al., 2017) investigates
a proposal-based function selection that is able to es-
tablish a connection between the considered scenario
and the system’s functional scope. The scenario is
characterized by entering values for predefined influ-
ence factors, e.g., the amount of students participating
in the lecture. Afterwards, a suitable functional scope
is proposed that can be adjusted manually to match
the lecturer’s personal preferences.
Although the mentioned function selections help
at targeting the function scope at a certain degree, they
2 (accessed 3/19/20).
3 (accessed 3/19/20).
4 (accessed 3/19/20).
lack at their limited functional scope as well as at pre-
defined limitations, e.g., the number of repetitions to
answer a question or the feedback whether a possible
correct answer is displayed to the students or not.
on, 2016) presents an approach that goes be-
yond a static functional scope by proposing a new
generic model that allows to define customized teach-
ing scenarios. The model consists of objects with at-
tributes, and rules with conditions and actions. Due
to the high generics of the model, almost any scenario
can be created. In MobileQuiz2
, the model was im-
plemented and evaluated in several realistic scenar-
ios. During the evaluation, the modeling task turned
out to be very complex. Although a scenario editor
did help the users to model valid scenarios, it could
not make the modeling process easier to understand.
For this reason, a didactic expert is required to de-
fine the application model of a custom scenario when
MobileQuiz2 is used. Another issue got obvious dur-
ing execution. Due to the problem that the generic
model produces deep-nesting objects, it lacks in per-
formance as soon as the participation count raises.
In summary, two directions can be recognized: On
the one side, approaches exist targeting the system’s
functional scope by function selections. On the other
side, a generic model was proposed that is able to ex-
press scenarios without predefined elements. Nev-
ertheless, both groups have their individual limita-
tions, which motivated us to create an approach that
overcomes those and surpasses existing approaches.
In order to give any lecturer the opportunity to tar-
get the function scope to his/her teaching strategy in
mind, collaborative functional blocks will to be pro-
vided, which have so far only been investigated in
smaller, non-anonymous scenarios, e.g., as presented
by (Lingnau et al., 2003) or (Dragon et al., 2013).
This section presents preliminary work that was done
in advance of the prototype creation. First, the con-
cept of an adaptable learning environment is de-
scribed. Second, the fundament of our concept is pre-
sented, namely a (meta-)model for defining elements,
parameters, and rules.
3.1 Concept of an Adaptable Learning
Our main concept combines the strength of both ap-
proaches, the application models with static func-
5 (accessed 3/19/20).
stARS: Proposing an Adaptable Collaborative Learning Environment to Support Communication in the Classroom
customizedscenario functionalscopescenarioeditor
(meta-)model applicationmodel runtimeenvironment
creates adapts
Figure 1: The concept of an adaptable learning environment that allows to create and execute customized scenarios in order
to support lecturers’ personal teaching strategies. (Kubica, 2019)
tional scopes and the flexible generic models allow-
ing for highly customizable scenarios. In addition, it
focuses on solving the respective limitations of these
approaches. Therefore, it builds on ideas derived from
two concepts, namely Model-Driven Software Devel-
opment (MDSD) and End User Development (EUD).
MDSD is described as the generation of software
from models, whereas the complexity of the model is
significantly easier to understand than the generated
code. The syntax of such models and the interrela-
tionships between elements are typically defined by
(meta-)models. (Stahl et al., 2007)
According to (Sendall and Kozaczynski, 2003),
different types of methods to transform the model to a
running software can be recognized, whereby the in-
termediate representation, meaning that the model is
exported in a standardized form (e.g., XML or JSON)
that can be used by external tools, is the most promis-
ing option for our concept.
Motivated by the results on modeling from
on, 2016), one major goal of our concept will be
to provide an intuitive opportunity for end-users (i.e.,
lecturers with different abilities in computer science)
to customize their scenarios. This is strongly related
to the discipline of EUD, which is defined as “a set
of methods, techniques, and tools that allow users of
software systems, who are acting as non-professional
software developers, at some point to create, mod-
ify or extend a software artifact” (Lieberman et al.,
2006). E.g., instead of entering code as it is done
in classic programming languages, the user will be
able to compose visual elements, e.g., blocks, and
connect or link them, resulting in a reduced complex-
ity. Our concept adopts this idea in a graphical editor
that gives lecturers the opportunity to customize their
teaching scenarios (resulting in application models).
In order to execute these application models, our
concept includes a runtime environment that needs to
interpret those and target its functional scope and set-
tings accordingly. Since performance is key in learn-
ing environments, especially during real-time func-
tionality (e.g., learning or survey questions), it has to
be designed in a way that can handle sudden changes
in resource demand; hence we propose a scalable in-
To summarize, the overall concept consists of differ-
ent components. First, a (meta-)model has to be cre-
ated that defines the system’s elements with their pa-
rameters and rules for relationships. Next, a graphical
editor, which builds on top of the (meta-model) will
support the lecturer in creating customized scenarios
(i.e., application models). Finally, a scalable infras-
tructure has to be created, which is able to interpret
these application models. Figure 1 summarizes the
concept in a graphical manner.
3.2 stARS (Meta-)model
The processes which take place during a lecture are
similar to the concept of workflows. As the lecture
moves on, events happen (e.g., students give answers
to questions, or a group session takes place) that allow
to decide how the lecture is continued.
The previously presented concept is centered
around a (meta-)model, which allows to create work-
flow models. Each workflow (i.e., the derived appli-
cation model) represents one specific lecture and can
be created by the lecturer with the help of a graphi-
cal editor. Which workflow elements exist and how
they connect to each other is defined explicitly by the
(meta-)model. Furthermore, each element has a set of
parameters that describe it more concretely. As work-
flows have similar structural elements across multi-
ple known modeling languages, our (meta-)model in-
corporates those conventions. Therefore each derived
workflow uses exactly one start node and an arbitrary
amount of end nodes. In between, an arbitrary amount
of function blocks can be used to describe a specific
lecture. All elements (i.e., start nodes, end nodes and
function blocks) are connected by transitions, which
determine the order of the activities of a lecture.
During design time, a lecturer cannot foresee how
a lecture will proceed, hence different types of transi-
tions exist:
The OR-fork is used, when multiple paths in the
workflow can be taken based on the outcome of a
connected function block,
AND-forks are used to design parallel activities,
by splitting the control-flow into several sub-flows
CSEDU 2020 - 12th International Conference on Computer Supported Education
and a join connects several sub-flows again.
Each function block represents a unique functionality
of a learning environment. In general, we distinguish
seven groups of functions, each having several sub-
functions and a number of parameters for configura-
learning questions have one or more correct an-
swer(s) and can be solved by the students,
survey questions are used to poll an opinion of the
an open discussion allows students to ask own
questions and discuss them with other students,
closed feedback gives students the opportunity to
provide instant feedback on predefined feedback
group interactions form groups of students and
provide interactions within these groups,
result presentation displays a result on students’
and media presentation is used to display a media
content on those.
Finally, a pause block exists, which allows lecturers
to build breaks into their workflows and enable the
modeling of a complete 90 minute lecture. A more de-
tailed description of the (meta-)model’s structure and
which (sub-)functions and parameters it provides is
presented in (Kubica et al., 2019b).
As motivated by (Bruff, 2019), each lecturer coming
to class has a teaching strategy in mind. This results in
lecturers that want to be able to use the learning envi-
ronment in a way so that it supports this strategy. This
section introduces our stARS prototype that targets to
accomplish this task by implementing the previously
described concept. First, a scalable infrastructure is
presented, which can handle sudden changes in re-
source demand by design. Afterward, each part of
this infrastructure is described in more detail. Last,
the options for the lecturer are summarized.
4.1 Infrastructure
System performance is key to provide a good user ex-
perience. Although teaching scenarios can range from
simple classroom teaching (i.e., 10 to 30 students)
to crowded lectures (i.e., 1000 students or more),
a system has to be able to deliver constant perfor-
mance, even when multiple sessions take place simul-
taneously. Performance from monolithic applications
can suffer during heavy load (i.e., “slash-dot effect”).
Therefore, to provide a good user experience con-
stantly, it is necessary to create a scalable distributed
infrastructure that is able to run multiple application
models simultaneously without interference. The in-
frastructure was proposed in (Kubica et al., 2019b)
and consists out of:
A backend server to provide access to the
(meta-)model, the database and administrative
a graphical editor frontend for the lecturer to cre-
ate application models and start or stop scenarios,
an arbitrary amount of cloud servers which run the
individual application models based on a docker-
ized runtime
and a user interface for the students to participate
in scenarios.
4.2 Backend Server
The backend represents the main entry point of ad-
ministrative components. It allows administrators to
manage the registration of cloud servers, i.e., new
cloud servers can be added, or existing ones can be
removed. Furthermore, it allows lecturers to create
instances of their customized application models (the
result of the modeling task using the graphical edi-
tor frontend). The retrieved model is checked against
the (meta-)model to ensure valid sequences. During
startup, the created instance is executed as a container
on a cloud server. An automatic selection for an ap-
propriate server is performed to avoid an overload of
individual servers. In addition to administrative func-
tions, the backend handles user authentification, i.e.,
the retrieval of user tokens for both backend and run-
ning instances. Last, it connects to a scalable database
that stores data which is generated during the execu-
tion of instances. This ensures that instances can be
resumed on failure, e.g., during server crashes.
4.3 Editor Frontend
The graphical editor frontend serves as a user in-
terface for lecturers. It allows to customize their
scenarios by composing different visual elements
that represent the function blocks introduced by the
(meta-)model. The main focus is the creation of an
intuitive solution that lecturers with varying modeling
abilities are able to use. For this reason, the concept
uses ideas derived from the User-Centered Design
stARS: Proposing an Adaptable Collaborative Learning Environment to Support Communication in the Classroom
Figure 2: The graphical editor to create custom scenarios.
(UCD) approach that describes “design processes in
which end-users influence how a design takes shape”
(Abras et al., 2004). During an initial survey, the
opinion of users for basic components of the editor
(e.g., the position of the main menu or the strategy
for inserting elements) was requested. Based on the
results, a first conceptual prototype was developed.
Open questions were discussed in user interviews,
e.g., how the representation of elements should look
like. The results retrieved from these interviews were
combined in a final concept and implemented using
(a rendering toolkit and web modeler for
BPMN 2.0
), as described by (Roszko, 2019) in more
detail. A screenshot of an extended version of the ed-
itor is displayed in Figure 2.
4.4 Cloud Servers
The application infrastructure supports the integration
of an arbitrary amount of cloud servers whereby the
number of cloud servers increments as the resource
demand increases. Each cloud server is able to run
multiple instances of a runtime as a docker container,
which, in turn, executes an application model spec-
ified by a lecturer. Each runtime allows a set of
specified people (i.e., administrator, lecturer, speci-
fied students) to manage answers, finish currently ac-
tive function blocks or retrieve results for a set of
questions. Each runtime maintains a WebSocket con-
nection to the backend as well as to each connected
user (e.g., lecturer, student) to inform users in real-
time about changes in the currently executed applica-
tion models. While executing an application model,
the runtime processes each function block as well as
each transition individually and decides, based on the
rules provided by the (meta-)model, which functions
have to be executed and provided to the user.
6 (accessed 3/19/20).
Business Process Model and Notation A modeling
language for workflows.
Figure 3: The display of the current scenario with its active
function blocks.
4.5 User Interface
The user interface is the entry point for users to com-
municate with the system. According to the set user
roles, different views are to be differentiated: The lec-
turer and student view.
The lecturer view displays a list of created in-
stances with their respective application models. In
addition, it allows to create new instances using the
editor presented in subsection 4.3. Clicking on a spe-
cific instance will open a dashboard for managing
it. The created application model is displayed on the
top, and the currently active function blocks are high-
lighted in color, as depicted in Figure 3.
In the student view, these currently active function
blocks are displayed. According to the set parameters,
their respective functionality is adapted. E.g., a learn-
ing question with the parameter answerFeedback set
to true will display immediate feedback after an an-
swer has been given, as displayed in Figure 4. If the
parameter is set to false (which is the default value of
it), students do not receive feedback on the correct-
ness of their answers until the lecturer evaluates the
question. Setting these parameters properly enables
the implementation of different teaching strategies.
Figure 4: The answering of two questions with different
answerFeedback parameter set.
CSEDU 2020 - 12th International Conference on Computer Supported Education
Figure 5: The result view of the questions displayed within
Figure 4. Clicking on a specific container will open an ex-
tended view for evaluation.
In the lecturer view, the real-time evaluation of the
currently active function blocks will be presented be-
low the application model. For function blocks that
allow the answering of students, e.g., learning or sur-
vey questions, the results are presented using charts,
as depicted in Figure 5. Clicking on those overview
items will open a modal for the presentation and dis-
cussion of these questions. For other function blocks,
this view varies, e.g., for closed feedback, a modified
student view with additional buttons for managing the
discussions is displayed.
4.6 Options for the Lecturer
In order to configure the learning environment in a
way that it supports the lecturer’s personal teaching
strategy, different options exist and are presented in
the following.
First, lecturers are able to adapt each function
block to their special needs by setting different pa-
rameters. E.g., lecturers that will discuss the results
of a question with their students will disable the an-
swerFeedback, while a lecturer using those questions
as a self-test for students will not.
Second, lecturers can build sequences of function
blocks and define conditions that allow for the realiza-
tion of different learning paths. E.g., if the results of a
question are not satisfying, another question could be
displayed that will uncover the reason for the answers.
Third and as already motivated in the last exam-
ple, function blocks can be linked to cooperate. E.g.,
the results of one or a set of questions can serve as
the condition for different learning paths, or as the in-
put of result blocks that allow to display results on the
students’ devices.
The fourth and final described option is the ad-
dition of novel collaborative functionality, motivated
by the problem that peer or small group discussions
are hard to accomplish in large lectures. We present
an approach to move those discussions to an online
Figure 6: An example for a group discussion with partici-
pants having randomly generated pseudonyms.
learning environment. In addition to the support of
a class-wide open discussion functionality, a group
builder is introduced that allows the formation of
groups based on different algorithms. Within these
groups, different group interactions can be defined,
e.g., group discussions or voting. An example of a
group interaction is depicted in Figure 6 and visual-
izes the anonymous discussion between two students
that answered a previous question differently.
Since this work consolidates multiple different types
of contributions based on several submissions, also
different evaluation strategies apply for each of them.
The following section summarizes those strategies.
5.1 Paper-based Evaluation of the
As the (meta-)model was no fundamentally new idea
but incorporated ideas of the according related work,
the evaluation was primarily conducted to test if the
weak points of prior approaches (i.e., very compli-
cated modeling process, insufficient modeling capa-
bilities) were still present. As the prototypical im-
plementation of the system’s backend and frontend as
well as the graphical editor were still in development,
a paper-based approach was chosen to simulate the
system’s capabilities. The evaluation was executed
by 20 participants who predominantly agreed that the
presented (meta-)model was easy to use and intuitive
and that they therefore would use it in future teaching
scenarios (Kubica et al., 2019b). During the evalu-
ation, it was not feasible to test all functions which
the (meta-)model provides. Therefore, a representa-
tive subset was chosen that was accepted very well by
the participants of the study, although they did not use
similar functions before.
stARS: Proposing an Adaptable Collaborative Learning Environment to Support Communication in the Classroom
5.2 Evaluation of the Prototypical
Graphical Editor
In (Roszko, 2019), the prototype of the graphical edi-
tor was evaluated. The evaluation included five parts
and was executed by 19 participants as follows: First,
the participants were asked to fill in general informa-
tion about their prior knowledge on graphical editors
and the concept of workflows. Second, three different
tasks were presented to the participants, which should
be solved using the prototype. Each task checked spe-
cific abilities: While the participants were asked to
insert, connect and parameterize function blocks in
the first task, they should use abstract function blocks
in a second more complex task and delete and adjust
function blocks in the final task. Third, each compo-
nent of the prototype was rated using a Likert scale.
Fourth, a System Usability Scale (SUS; cf. (Brooke
et al., 1996)) was determined before the fifth part con-
cluded the evaluation with qualitative feedback from
both positive and negative perspectives.
Although participants with different prior knowl-
edge were part of this evaluation, we could not ob-
serve a significant difference in the results between
those groups. Both groups of participants with prior
knowledge and without were able to solve the tasks
of the second part without major difficulties. This is
also reflected in the SUS score, which ranges between
70 and 95. In particular, only 2 out of 19 participants
rated a score under 85. The average score of 88 in-
dicates a good to excellent usability. Moreover, the
third part of our evaluation was able to verify our pro-
posed concept. Only minor changes were necessary
to implement the feedback of the participants, e.g.,
removing an alternative theme or additional buttons
to duplicate or delete an element. Finally, the feed-
back retrieved from the qualitative questions was also
added to the editor before it was integrated within the
overall system.
5.3 Implementation in Realistic
In the next step, we plan to extend these user studies
by implementing our prototype in real scenarios, i.e.,
giving lecturers the prototype on hand and let them
model and execute their customized scenarios. In ad-
dition to monitoring the system’s behavior, interviews
will be conducted to receive feedback from the lectur-
ers. Our goal is to evaluate both the functionality and
the opportunities provided by our prototype. Our cre-
ated prototype is open to use for every lecturer.
In this paper, the prototype of an adaptable learn-
ing environment was presented. It gives lecturers
the opportunity to target the system’s functionality
to their personal teaching strategy. They can adapt
function blocks by different parameters, build condi-
tional sequences of those function blocks, link func-
tion blocks to cooperate, and choose from novel col-
laborative functions as an addition to known Audience
Response, or Backchannel functions. The results re-
trieved from user studies for the (meta-)model and the
graphical editor were presented. Future applications
within real-life scenarios were proposed to evaluate
the overall concept and check the interplay of our
individual components, namely the backend server,
cloud servers, graphical editor frontend and user in-
Due to design, our approach is targeted to lecturer-
paced scenarios, i.e., the lecturer has to unlock the
specific functionality before it can be used by the stu-
dents. Our prototype does not investigate scenarios, in
which the students can iterate the created application
models by themselves. Nevertheless, an extension to
such scenarios will be investigated in the future.
Having in mind that the presented prototype is in
an early stage, improvements regarding the usability
have to be investigated. In order to improve the user
experience for lecturers even more, a proposal-based
function is planned that will suggest the implementa-
tion of appropriate methods during the ongoing exe-
cution within a lecture.
This work is funded by the German Research Foun-
dation (DFG) within the Research Training Group
“Role-based Software Infrastructures for continuous-
context-sensitive Systems” (GRK 1907). Special
thanks to Tenshi Hara for his feedback and proof-
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