Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

Abstract

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 Meta-modeling Approach to Take into Account Data Domain Characteristics and Relationships in Information Visualizations

“…Both experiments are focused on the automatic visualization generation. The generator use code templates based on a meta-model to define dashboards [2][3][4] and a Python script in which the different parameters are tuned to get a set of visualizations. The script processes the dataset changing a set of characteristics and provide a HTML and JavaScript file with the visualizations.…”

“…Following this approach, a dashboard meta-model was developed in previous studies, obtaining a set of abstract elements and relationships to define specific products [2][3][4]. A fragment of the dashboard meta-model is shown in Fig.…”

“…Official data source from the Madrid's government open data portal: https://datos.comunidad. madrid/catalogo/dataset/covid19_tia_muni_y_distritos/resource/f22c3f43-c5d0-41a4-96dc-719214d56968 2. Official data source from the Spanish National Institute of Statistics: https://www.ine.es/jaxiT3/ Tabla.htm?t=31097.…”

Smart Learning

“…Im et al [31] make a study to define the affective level of a given set of paragraphs and attempts to analyze the perceived trust of the methodologies in regard to usability. Vázquez-Ingelmo et al [32] define an information dashboard [33] metamodel that abstracts all these factors and integrates a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This metamodel may be used to design a domain-specific language to specify dashboards' requirements in a structured way [34].…”

New methods and technologies for enhancing usability and accessibility of educational data

“…Educational data research is becoming highly relevant in massive online courses [9], especially MOOCs (Massive Open Online Courses) [10-13] and SPOCs (Small Private Online Courses) [14][15][16]. Educational data are also the basis for learning analytics [17][18][19], with an increasing focus on the way educational data are presented [20][21][22], how users interact with the data [23][24][25][26], and data privacy and security [27][28][29][30].There are many types of data that can support student's learning [31], but the type and nature of the data, how they can be accessed, and who can access them, vary significantly. Whether educational data are collected from collaborative learning environments [32][33][34], course management systems [35,36], gamified training applications [37,38], or administrative systems from schools and universities [39-41], valuable properties, patterns, and insights often emerge.…”