Connecting domain-specific features to source code: towards the automatization of dashboard generation

Abstract

Dashboards are useful tools for generating knowledge and support decision-making processes, but the extended use of technologies and the increasingly available data asks for user-friendly tools that allow any user profile to exploit their data. Building tailored dashboards for any potential user profile would involve several resources and long development times, taking into account that dashboards can be framed in very different contexts that should be studied during the design processes to provide practical tools. This situation leads to the necessity of searching for methodologies that could accelerate these processes. The software product line paradigm is one recurrent method that can decrease the time-to-market of products by reusing generic core assets that can be tuned or configured to meet specific requirements. However, although this paradigm can solve issues regarding development times, the configuration of the dashboard is still a complex challenge; users' goals, datasets, and context must be thoroughly studied to obtain a dashboard that fulfills the users' necessities and that fosters insight delivery. This paper outlines the benefits and a potential approach to automatically configuring information dashboards by leveraging domain commonalities and code templates. The main goal is to test the functionality of a workflow that can connect external algorithms, such as artificial intelligence algorithms, to infer dashboard features and feed a generator based on the software product line paradigm.

Avoiding the Dark Side of Digital Transformation in Teaching. An Institutional Reference Framework for eLearning in Higher Education

“…Academic and learning analytics are crucial for decision-making processes at both institutional [74] and faculty levels [75,76]. Powerful dashboards [77] should be included in the technological ecosystems of the institution [78]. However, access to personal data must always be under the institutional regulations' ethical umbrella [79].…”

Smart Learning

“…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]. Grivokostopoulou et al [35] model 3D virtual worlds the domain of environmental engineering and energy generation, including intelligent pedagogical agents, to enhance students' interaction and improve their overall learning experience.…”

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.…”

“…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].…”