903 research outputs found
Extending a dashboard meta-model to account for users’ characteristics and goals for enhancing personalization
[EN]Information dashboards are useful tools for exploiting datasets and support decision-making processes. However, these tools are not trivial to design and build. Information dashboards not only involve a set of visualizations and handlers to manage the presented data, but also a set of users that will potentially benefit from the knowledge generated by interacting with the data. It is important to know and understand the requirements of the final users of a dashboard because they will influence the design processes. But several user profiles can be involved, making these processes even more complicated. This paper identifies
and discusses why it is essential to include the final users when modeling a dashboard.
Through meta-modeling, different characteristics of potential users are structured, thus obtaining a meta-model that dissects not only technical and functional features of a dashboard (from an abstract point of view) but also the different aspects of the final users that will make use of it. By identifying these user characteristics and by arranging them into a meta-model, software engineering paradigms such as model-driven development or software product lines can employ it as an input for generating concrete dashboard products. This approach could be useful for generating Learning Analytics dashboards that take into account the users' motivations, beliefs, and knowledge
Capturing high-level requirements of information dashboards' components through meta-modeling
[EN]Information dashboards are increasing their sophistication to match new necessities and adapt to the high quantities of generated data nowadays.These tools support visual analysis, knowledge generation, and thus, are crucial systems to assist decision-making processes.However, the design and development processes are complex, because several perspectives and components can be involved.Tailoringcapabilities are focused on providing individualized dashboards without affecting the time-to-market through the decrease of the development processes' time. Among the methods used to configure these tools, the software product lines paradigm and model-driven development can be found. These paradigms benefit from the study of the target domain and the abstraction of features, obtaining high-level models that can be instantiated into concrete models. This paper presents a dashboard meta-model that aims to be applicable to any dashboard. Through domain engineering, different features of these tools are identified and arranged into abstract structuresand relationships to gain a better understanding of the domain. The goal of the meta-model is to obtain a framework for instantiating any dashboard to adapt them to different contexts and user profiles.One of the contexts in which dashboards are gaining relevance is Learning Analytics, as learning dashboards are powerful tools for assisting teachers and students in their learning activities.To illustrate the instantiation process of the presented meta-model, a small example within this relevant context (Learning Analytics) is also provided
Specifying information dashboards’ interactive features through meta-model instantiation
[EN]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 context
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 information
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 application
within the context of Learning Analytics is presented to demonstrate the
viability of this approach
Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning
[EN] Data analysis is a key process to foster knowledge generation in particular domains
or fields of study. With a strong informative foundation derived from the analysis of
collected data, decision-makers can make strategic choices with the aim of obtaining
valuable benefits in their specific areas of action. However, given the steady growth
of data volumes, data analysis needs to rely on powerful tools to enable knowledge
extraction.
Information dashboards offer a software solution to analyze large volumes of
data visually to identify patterns and relations and make decisions according to the
presented information. But decision-makers may have different goals and,
consequently, different necessities regarding their dashboards. Moreover, the variety
of data sources, structures, and domains can hamper the design and implementation
of these tools.
This Ph.D. Thesis tackles the challenge of improving the development process of
information dashboards and data visualizations while enhancing their quality and
features in terms of personalization, usability, and flexibility, among others.
Several research activities have been carried out to support this thesis. First, a
systematic literature mapping and review was performed to analyze different
methodologies and solutions related to the automatic generation of tailored
information dashboards. The outcomes of the review led to the selection of a modeldriven
approach in combination with the software product line paradigm to deal with
the automatic generation of information dashboards.
In this context, a meta-model was developed following a domain engineering
approach. This meta-model represents the skeleton of information dashboards and
data visualizations through the abstraction of their components and features and has
been the backbone of the subsequent generative pipeline of these tools.
The meta-model and generative pipeline have been tested through their
integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully
integrated with other meta-model to support knowledge generation in learning
ecosystems, and as a framework to conceptualize and instantiate information
dashboards in different domains.
In terms of the practical applications, the focus has been put on how to transform
the meta-model into an instance adapted to a specific context, and how to finally
transform this later model into code, i.e., the final, functional product. These practical
scenarios involved the automatic generation of dashboards in the context of a Ph.D.
Programme, the application of Artificial Intelligence algorithms in the process, and
the development of a graphical instantiation platform that combines the meta-model
and the generative pipeline into a visual generation system.
Finally, different case studies have been conducted in the employment and
employability, health, and education domains. The number of applications of the
meta-model in theoretical and practical dimensions and domains is also a result itself.
Every outcome associated to this thesis is driven by the dashboard meta-model, which
also proves its versatility and flexibility when it comes to conceptualize, generate, and
capture knowledge related to dashboards and data visualizations
Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning
Information dashboards are sophisticated tools. Although they
enable users to reach useful insights and support their decisionmaking
challenges, a good design process is essential to obtain
powerful tools. Users need to be part of these design processes,
as they will be the consumers of the information displayed. But
users are very diverse and can have different goals, beliefs,
preferences, etc., and creating a new dashboard for each
potential user is not viable. There exist several tools that allow
users to configure their displays without requiring programming
skills. However, users might not exactly know what they want to
visualize or explore, also becoming the configuration process a
tedious task. This research project aims to explore the automatic
generation of user interfaces for supporting these decisionmaking
processes. To tackle these challenges, a domain
engineering, and machine learning approach is taken. The main
goal is to automatize the design process of dashboards by
learning from the context, including the end-users and the target
data to be displayed
Conceptual framework for process-oriented feedback through Learning Analytics Dashboards
The number of students enrolled in online higher education courses is
increasing, and as a result, more data on their learning process is being generated.
By exploring this student behavior data through learning analytics, both student
and teacher can be provided with process-oriented feedback in the form of
dashboards. However, little is known about the typology of relevant feedback in
the dashboard to different learning objectives, students and teachers. Although
most dashboards and the feedback they provide are based solely on student
performance indicators, research shows that such feedback is not sufficient. This
article attempts to define a conceptual model that visualizes the relationships
between the design of a Learning Analytics Dashboard (LAD) and the concepts
of learning science in order to provide process-oriented feedback that supports
the regulation of learning. The aim of the work is not to propose a specific design
of the LAD to provide feedback, but rather a conceptual framework for the choice
of concepts for that design, and therefore to help understand future data needs as
a basis for the educational feedback of the dashboards. As a conclusion of our
research, we can say that having LADs adapted to any profile (student, teacher,
etc.) can improve decision-making processes by showing each user the
information that interests them most in the way that best enables them to
understand it
A meta-model to develop learning ecosystems with support for knowledge discovery and decisionmaking processes.
There are software solutions to solve most of the
problems related to information management in any company or
institutions, but still, there is a problem for transforming
information into knowledge. Technological ecosystems emerge as
a solution to combine existing tools and human resources to solve
different problems of knowledge management. In particular,
when the ecosystem is focused on learning processes associated
with knowledge are named learning ecosystems. The learning
ecosystem metamodel defined in previous works solves several
problems related to the definition and implementation of these
solutions. However, there are still challenges associated with
improving the analysis and visualization of information as a way
to discover knowledge and support decision making processes.
On the other hand, there is a metamodel proposal to define
customized dashboards for supporting decision-making
processes. This proposal aims to integrate both metamodels as a
way to improve the definition of learning ecosystems
A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare
[EN]Knowledge management is one of the key priorities of many organizations.
They face di erent challenges in the implementation of knowledge management processes,
including the transformation of tacit knowledge—experience, skills, insights, intuition, judgment and
know-how—into explicit knowledge. Furthermore, the increasing number of information sources
and services in some domains, such as healthcare, increase the amount of information available.
Therefore, there is a need to transform that information in knowledge. In this context, learning
ecosystems emerge as solutions to support knowledge management in a di erent context. On the
other hand, the dashboards enable the generation of knowledge through the exploitation of the
data provided from di erent sources. The model-driven development of these solutions is possible
through two meta-models developed in previous works. Even though those meta-models solve
several problems, the learning ecosystem meta-model has a lack of decision-making support. In this
context, this work provides two main contributions to face this issue. First, the definition of a holistic
meta-model to support decision-making processes in ecosystems focused on knowledge management,
also called learning ecosystems. The second contribution of this work is an instantiation of the
presented holistic meta-model in the healthcare domain
Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards
[EN]Information dashboards are everywhere. They support knowledge discovery in a huge
variety of contexts and domains. Although powerful, these tools can be complex, not only for the
end-users but also for developers and designers. Information dashboards encode complex datasets
into different visual marks to ease knowledge discovery. Choosing a wrong design could
compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or
configuration for each potential context, user, or data domain is a crucial task. For these reasons,
there is a necessity to automatize the recommendation of visualizations and dashboard
configurations to deliver tools adapted to their context. Recommendations can be based on different
aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or
carried out through the visualizations. This work presents a dashboard meta-model that abstracts
all these factors and the integration of a visualization task taxonomy to account for the different
actions that can be performed with information dashboards. This meta-model has been used to
design a domain specific language to specify dashboards requirements in a structured way. The
ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any
context, such as the educational context, in which a lot of data are generated, and there are several
actors involved (students, teachers, managers, etc.) that would want to reach different insights
regarding their learning performance or learning methodologies
A Dashboard to Support Decision-Making Processes in Learning Ecosystems
There are software solutions to solve most of the problems related to information management in any company or institutions, but still, there is a problem for transforming information into knowledge. Technological ecosystems emerge as a solution to combine existing tools and human resources to solve different problems of knowledge management. In particular, when the ecosystem is focused on learning processes associated with knowledge are named learning ecosystems. The learning ecosystem metamodel defined in previous works solves several problems related to the definition and implementation of these solutions. However, there are still challenges associated with improving the analysis and visualization of information as a way to discover knowledge and support decision making processes. On the other hand, there is a metamodel proposal to define customized dashboards for supporting decision-making processes. This proposal aims to integrate both metamodels as a way to improve the definition of learning ecosystems
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