=Paper=
{{Paper
|id=Vol-2671/paper06
|storemode=property
|title=Specifying information dashboards’ interactive features through meta-model instantiation
|pdfUrl=https://ceur-ws.org/Vol-2671/paper06.pdf
|volume=Vol-2671
|authors=Andrea Vázquez-Ingelmo,Francisco-J. García Peñalvo,Roberto Theron,Alicia García-Holgado
|dblpUrl=https://dblp.org/rec/conf/lasi-spain/Vazquez-Ingelmo20
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==Specifying information dashboards’ interactive features through meta-model instantiation==
https://ceur-ws.org/Vol-2671/paper06.pdf
Specifying information dashboards’ interactive features
through meta-model instantiation
Andrea Vázquez-Ingelmo1[0000-0002-7284-5593], Francisco J. García-Peñalvo1[0000-0001-9987-
5584]
, Roberto Therón1,2[0000-0001-6739-8875] and Alicia García-Holgado1[0000-0001-5881-7775]
1
GRIAL Research Group, Computer Sciences Department, Research Institute for Educational
Sciences, University of Salamanca, Salamanca, Spain
2
VisUSAL Research Group. University of Salamanca. Salamanca, Spain
{andreavazquez,fgarcia,theron,aliciagh}@usal.es
Abstract. Information dashboards1 can be leveraged to make informed decisions
with the goal of improving policies, processes, and results in different contexts.
However, the design process of these tools can be convoluted, given the variety
of profiles that can be involved in decision-making processes. The educative con-
text is one of the contexts that can benefit from the use of information dashboards,
but given the diversity of actors within this area (teachers, managers, students,
researchers, etc.), it is necessary to take into account different factors to deliver
useful and effective tools. This work describes an approach to generate infor-
mation dashboards with interactivity capabilities in different contexts through
meta-modeling. Having the possibility of specifying interaction patterns within
the generative workflow makes the personalization process more fine-grained,
allowing to match very specific requirements from the user. An example of ap-
plication within the context of Learning Analytics is presented to demonstrate the
viability of this approach.
Keywords: Information Dashboards, Meta-model, Information Visualization,
Interactions, MDA, SPL.
1 Introduction
Information dashboards have increased in popularity and relevance in several fields.
They foster knowledge generation by presenting complex datasets through visual ar-
rangements and encodings. These tools support informed decision-making processes,
and data-driven approaches to carry out complex decision flow [1, 2].
1
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)
48
However, carrying out data-driven decision making is not a trivial task. First, be-
cause a significant amount of data is needed to generate knowledge. And second, be-
cause the processes of analyzing such data require that the person leading the decision
making or analysis be able to understand and interpret data sets that are often complex
and extensive.
But thanks to the evolution of technologies, these analysis tasks are now available
to less technical profiles. There are tools that facilitate analysis and knowledge genera-
tion from data sets. One of the most popular tools are dashboards [3].
Having a dashboard does not guarantee knowledge generation, though. It is neces-
sary to take into account the audience and the profile of the users who will use these
tools. There may be users who can understand complex visualizations, while others will
need other visual metaphors to correctly understand their data sets [4].
Especially, there are some contexts in which putting the focus on the audience is
crucial. The educative context is one of them. In this context, there are several actors
that play crucial roles: teachers, managers, students, etc.
Learning Analytics (LA) dashboards provide a display in which different indicators
regarding learners, learning processes, and/or learning contexts are arranged into a set
of visualizations [5]. However, the design process of LA dashboards is crucial to lev-
erage their capabilities [6]. It is necessary to take into account the data, the user, and
the goals of the dashboard to get the most out of these tools.
In previous works, a dashboard meta-model has been developed [7-9] to specify and
instantiate dashboards within any context. The dashboard meta-model defines high-
level classes that depict abstract concepts from the domain, such as the elements that
compose information visualizations (channels, visual marks, axes, users’ goals, varia-
bles, datasets, etc.).
The dashboard meta-model can be derived into concrete models to build specific
dashboards through a generator by using a high-level syntax. However, there is a di-
mension that must also be taken into account: the potential user interactions with the
dashboard. Interaction patterns help users to interact directly with the data displayed on
their screens. Users could highlight data points, select them, filter them, etc., through
different events (clicking, hovering, brushing, etc.). These patterns need to be repre-
sented within the meta-model at a high-level to allow their instantiation and, subse-
quently, to obtain fully interactive dashboards.
This work describes the introduction of interaction patterns within a dashboard
meta-model and presents an example of application in the context of LA through a
generative pipeline and a DSL (Domain Specific Language).
The rest of this paper is organized as follows. Section 2 provides a background on
educational and learning analytics dashboards. Section 3 depicts the methodology em-
ployed to carry out the research. Section 4 describes the meta-model structure regarding
interaction patterns. Section 5 presents a dashboard instantiation framed in the Learning
Analytics context. Finally, sections 6 and 7 discuss the results and conclude the work,
respectively.
49
2 Background
As mentioned in the introduction, dashboards are increasingly popular tools because of
their usefulness in supporting the visual analysis of complex datasets. The educational
context is one of the contexts in which these tools can bring significant benefits since
the use of data to make decisions regarding educational processes can improve learning
outcomes [10-12].
Educational dashboards [13] are instruments that allow their users to identify pat-
terns, relationships, relevant data, etc., among a set of learning variables. However, the
diversity of roles in this context makes the design of educational dashboards a chal-
lenge.
In [5], it was found that the majority of users are usually teachers, but students,
administrators, and researchers are also among the main users of these tools. Educa-
tional dashboards are also diverse in terms of their objectives; self-monitoring, moni-
toring of other students, and administrative monitoring [5]. This type of research allows
us to observe that dashboards are very diverse in the educational context, both in their
functionalities and in their design.
Due to these factors, some methods have been sought to design educational and
learning analytics dashboards, so that they can be adapted according to their purposes
and audience because there is no one-size-fits-all approach [14].
Dashboards should be customized to provide the necessary information in the most
effective way. In fact, a study by Roberts et al. confirmed the widespread desire of
students for control panels that can be customized, giving them the option of configur-
ing them to display the information that interests them most or that they find most useful
[15].
Thus, it is not only the variety of user roles in the educational context but the variety
of objectives and profiles among users with the same role, which makes the develop-
ment of learning analytics dashboards an elaborate activity. In addition, the amount of
data generated and its complex structure can make the process of knowledge discovery
even more difficult for less technical profiles.
For these reasons, models have been proposed to try to adapt these tools using con-
ceptual models that take into account the indicators, the description, and needs of the
users, their preferences, their knowledge of the domain, etc. In [16], a generator of
learning analytics dashboards is presented. This generator takes into account the above-
mentioned information by structuring it in models that feed a dashboard generator.
As can be seen, control panels in the educational context have increased in popular-
ity due to the benefits that the use of data can bring to decision making. However, to
take advantage of them, it is necessary to take into account the users and the context in
which they will be employed.
3 Methodology
The methodology employed relies on two paradigms: model-driven development [17,
18] and the software product line paradigm [19, 20].
50
Model-driven development leverages high-level models (meta-models) to obtain an
abstract representation of systems. Meta-models do not only help in the conceptualiza-
tion of information systems but are also powerful artifacts that support a whole pipeline
for developing such systems. These abstract models can be mapped to concrete prod-
ucts, according to the OMG four-layer meta-model architecture [21]: meta-meta model
layer (M3), meta-model layer (M2), user model layer (M1) and user object layer (M0).
Concepts of the domain are captured through meta-models and arranged as a set of
classes and relationships, yielding a simplified representation of the problem’s domain.
This representation is structured, thus supporting the processing of meta-models
through computational methods.
In previous works, a dashboard meta-model has been presented [7-9, 22]. The dash-
board meta-model includes three main parts involved in the development of infor-
mation dashboards: the user, the layout, and the components of the dashboard.
This work is focused on addressing the specification and instantiation of interaction
patterns among dashboard components to obtain interactive and functional information
dashboards. A set of core assets have been developed following the SPL paradigm to
support a connection between meta-model instantiations and final products. The SPL
provides a framework to create components that can be configured and customized with
almost no effort (the main effort is made during the development of these core assets)
to meet specific requirements.
4 Modeling interaction patterns
Interaction patterns are highly diverse. They can involve the user clicking some parts
of the dashboard. They can also involve hovering, brushing, etc. And, on the other hand,
they can provoke different effects, such as highlighting some point, showing a tooltip,
filter the data, etc.
All these possibilities must be captured through the meta-model in an abstract and
coherent manner. Following a domain engineering approach [23, 24], a set of concep-
tual classes have been identified across dashboards from different domains to model
interaction patterns. These classes are depicted in Fig. 1.
Fig. 1. Meta-model section regarding interaction patterns.
51
Information visualizations are composed of different elements. Mainly, these visu-
alizations are composed of basic primitives, like visual marks, axes, scales, visual chan-
nels, etc. When interacting with a visualization, these primitives will be affected, for
example, by changing their colors to highlight them or by showing a tooltip.
Three classes have been identified to reflect interactions in the meta-model. The In-
teraction class, which represents the interaction pattern to be applied to a specific prim-
itive of the visualization. This class is abstract and can be of two types: an event or an
effect. This distinction is necessary to represent which events to capture and which ef-
fects to apply to the visualization's primitives. For example, clicking in one of the bars
from a bar chart is an event, and highlighting that bar (by varying its style) when se-
lected is an effect. With these conceptual classes, it is possible to combine different
specifications to obtain fully functional and interactive dashboards.
5 Learning Analytics Dashboard Example
A simple Learning Analytics information dashboard has been instantiated to demon-
strate the viability of the generative workflow in this context. To do so, an instance of
the Ecore meta-model was developed through EMF (Eclipse Modeling Framework,
https://www.eclipse.org/modeling/emf/). This framework provides several features to
support model-driven approaches: from meta-model editors to code-generation facili-
ties. In this case, two visualizations are specified: a scatter plot and a parallel coordi-
nates plot (Fig. 2).
Fig. 2. An excerpt from the meta-model instance.
52
The elements of these plots encode the values of the input dataset through different
channels. These channels are based on scales that map the dataset variables to values
that are encoded through the position, color, size, etc. of the visual marks.
The employed dataset to test this application is the Open University Learning Ana-
lytics Dataset [25], which contains data from courses presented at the Open University
(OU): assessments, scores, students' clickstreams, etc. In this case, the dashboard in-
stance will employ the student ID, assessment ID, and assessment score variables from
the dataset.
The instance is then handed to a Python generator [26], which "translates" the struc-
ture of the XMI (XML Metadata Interchange) into React code through a set of custom
and low-level components.
Fig. 3. An excerpt from a visualization's channels and scales.
For example, Fig. 4 presents a generated code excerpt from the dashboard. This code
fragment is part of the props that are passed down to a specific React component. In
this case, these props specify that the component will attend hovering and clicking
events, and, if a data point is selected, it will be affected by highlighting that data point
through a custom style.
53
Fig. 4. React code fragment from the generated dashboard.
The outcome of this process is a React application that hosts the dashboard. The
instantiated dashboard shown in Fig. 2 is presented in Fig 5. This dashboard holds two
information visualizations. A scatter plot that represents each student on the y-axis and
their scores in different assessments on the x-axis. On the other hand, a parallel coordi-
nates plot shows the relationship between students, assessments, and scores, allowing
them to detect patterns regarding these variables.
Fig. 5. Screenshot of the generated dashboard.
54
As shown in Fig. 4, some interaction patterns have been included in the generated
dashboard. For example, the scatter plot component lets users hover on data points,
provoking a highlight effect (as previously defined in the dashboard instance). Due to
this configuration, when a user hovers on a data point in the first visualization, that data
point is highlighted throughout the dashboard (Fig. 6).
This approach also allows the combination of different interaction patterns. For ex-
ample, users can use a brush to select points on the parallel coordinates plot, affecting
the scatter plot (Fig. 7).
Fig. 6. Example of the addition of interaction patterns: hovering.
Fig. 7. Example of the addition of interaction patterns: brushing.
55
6 Discussion
Creating an information dashboard is not a trivial task; it involves the study of the do-
main, of the users, of the data, as well as the subsequent development process of the
designed display. Given the current necessity to rely on data to make better decisions,
it is important to have tools that ease knowledge discovery and insight delivery.
A dashboard meta-model has been developed to tackle the personalization of these
tools. The meta-model could be seen as a conceptual artifact that supports the design
and development processes of dashboards. However, in this case, the meta-model is
used as an input for an automatic generative process of information dashboards.
The meta-model can be instantiated through the Eclipse Modeling Framework
(EMF), thus obtaining a concrete model of a concrete Learning Analytics dashboard
(an M1 model, Fig. 8).
Fig. 8. Meta-model organization following the OMG architecture.
The obtained instance is the input for a dashboard generator, which arranges a set of
software assets to compose a dashboard that matches the instance characteristics and
features.
This work is focused on the definition of interaction patterns through the meta-model
to obtain fully interactive dashboards. Interaction patterns are necessary to deliver good
levels of user experience, as well as to improve the visual analysis process [27].
Interactions have been included in the meta-model through two conceptual classes,
Event, and Effect, which define the events that the component will be listening to and
the effects that the data point selections will have on their visual representation.
Adding interaction patterns to the instantiation process allows a more fine-grained
definition of the dashboard features, which can be modified depending on different fac-
tors, such as the user expertise [28-30], the data domain [31], the analytical tasks and
goals [32, 33], etc.
In this case, a Learning Analytics dashboard has been instantiated. This dashboard
takes information regarding students’ assessments and their scores. However, as stated
in previous sections, it is straightforward to adapt the dashboard instance to other da-
tasets with different variables. This is possible because the meta-model includes entities
related to the datasets, their variables and potential operations that could transform data,
56
meaning that they can be configured to support and generate indicators that allow dash-
board users to reach meaningful insights related to their information goals.
Learning Analytics dashboards are very diverse, and the context and actors involved
in each particular situation are crucial to building useful tools. Having the possibility
of configuring information dashboards in a straightforward way allows for shifting the
focus from the development process and giving more relevance to the design phases of
the dashboard; Which analytic tasks will allow the user to reach his or her information
goals? Which data variables should the dashboard display? How many views should
the dashboard present? Which visual encodings allow better understanding given the
target user expertise? Which interaction patterns would be more useful to support the
user's analytical tasks?
Information dashboards are everywhere, but that does not mean that they are useful
for everyone. It is crucial to find the best visual encodings, view arrangements, and
interaction patterns based on each user's goals, characteristics, and datasets.
Designing a meta-model is a preliminary step to identify which features make a
dashboard useful, effective or efficient. The abstract classes and structures of the meta-
model can be used as inputs or outputs of external algorithms (for example, machine
learning algorithms [31, 34]). Subsequent research will use the identified structures to
build algorithms that maximize the usability, reliability, efficiency, etc., of these tools.
The model-driven development paradigm has been combined with a software prod-
uct line approach to obtain a complete generative pipeline: starting from a conceptual
phase (meta-model), software assets (core assets of the product line) were created to
support the automatic generation of dashboards.
7 Limitations
Information dashboards usually have several intertwined features, elements and inter-
action patterns involved. This meta-model tries to capture the majority of them. How-
ever, dashboards are indeed very diverse, hampering the abstraction process. The cur-
rent version of the meta-model supports dashboards based on different views and fo-
cused on structured data, but the visualization realm can also involve infographics, re-
ports and other analysis assets that are not, at this time, supported by the presented
meta-model.
8 Conclusions
A meta-model for information dashboards has been presented. This meta-model not
only includes the visual elements of dashboards (visual marks, channels, axes, etc.) but
also intangible components such as interaction patterns.
Including interaction patterns in the meta-model allows a more fine-grained specifi-
cation and configuration of tailored information dashboards, meaning that not only the
visual display can be customized, but also the methods in which users interact with
datasets.
57
An example of application in the context of Learning Analytics has been carried out.
This context involves very different actors and stakeholders, which might need differ-
ent features from dashboards. Relying on the dashboard meta-model can make the de-
velopment of these tools a more straightforward task.
Future research lines will involve the refinement of the meta-model to include rules
and constraints, as well as in-depth user testing to test the usability of different interac-
tion patterns and designs.
Acknowledgments
This research work has been supported by the Spanish Ministry of Education and Vo-
cational Training under a FPU fellowship (FPU17/03276). This work has been partially
funded by the Spanish Government Ministry of Economy and Competitiveness
throughout the DEFINES project (Ref. TIN2016-80172-R).
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