Taking advantage of the software product line paradigm to generate customized user interfaces for decision-making processes: a case study on university employability

[EN]University employment and, specifically, employability has gained relevance since research in these fields can lead to improvement in the quality of life of individual citizens. However, empirical research is still insufficient to make significant decisions, and relying on powerful tools to explore data and reach insights on these fields is paramount. Information dashboards play a key role in analyzing and visually exploring data about a specific topic or domain, but end users can present several necessities that differ from each other, regarding the displayed information itself, design features and even functionalities. By applying a domain engineering approach (within the software product line paradigm), it is possible to produce customized dashboards to fit into particular requirements, by the identification of commonalities and singularities of every product that could be part of the product line. Software product lines increase productivity, maintainability and traceability regarding the evolution of the requirements, among other benefits. To validate this approach, a case study of its application in the context of the Spanish Observatory for University Employability and Employment system has been developed, where users (Spanish universities and administrators) can control their own dashboards to reach insights about the employability of their graduates. These dashboards have been automatically generated through a domain specific language, which provides the syntax to specify the requirements of each user. The domain language fuels a template-based code generator, allowing the generation of the dashboards' source code. Applying domain engineering to the dashboards' domain improves the development and maintainability of these complex software products given the variety of requirements that users might have regarding their graphical interfaces

A Literature Review on Intelligent Services Applied to Distance Learning

Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio

A Semantics-based User Interface Model for Content Annotation, Authoring and Exploration

The Semantic Web and Linked Data movements with the aim of creating, publishing and interconnecting machine readable information have gained traction in the last years. However, the majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way in which humans interact with information. Semantic structuring of content on the other hand provides a wide range of advantages compared to unstructured information. Semantically-enriched documents facilitate information search and retrieval, presentation, integration, reusability, interoperability and personalization. Looking at the life-cycle of semantic content on the Web of Data, we see quite some progress on the backend side in storing structured content or for linking data and schemata. Nevertheless, the currently least developed aspect of the semantic content life-cycle is from our point of view the user-friendly manual and semi-automatic creation of rich semantic content. In this thesis, we propose a semantics-based user interface model, which aims to reduce the complexity of underlying technologies for semantic enrichment of content by Web users. By surveying existing tools and approaches for semantic content authoring, we extracted a set of guidelines for designing efficient and effective semantic authoring user interfaces. We applied these guidelines to devise a semantics-based user interface model called WYSIWYM (What You See Is What You Mean) which enables integrated authoring, visualization and exploration of unstructured and (semi-)structured content. To assess the applicability of our proposed WYSIWYM model, we incorporated the model into four real-world use cases comprising two general and two domain-specific applications. These use cases address four aspects of the WYSIWYM implementation: 1) Its integration into existing user interfaces, 2) Utilizing it for lightweight text analytics to incentivize users, 3) Dealing with crowdsourcing of semi-structured e-learning content, 4) Incorporating it for authoring of semantic medical prescriptions

Perspectives on aesthetic computing

The authors present an introduction to the new interdisciplinary area of aesthetic computing and proceed to define this area with examples from each of their own disciplines, practices and research. While several decades of publication and work have resulted in significant advancements in art as implemented through technology, less emphasis has been placed on studying the converse issue of art's effect on computing, or "aesthetic computing." The authors present their individual work in this area and then follow with brief criticism of one another's work to elucidate different perspectives on the idea. By approaching the topic of aesthetic computing in this manner, the paper serves as an introduction to and survey and analysis of the field

Extracting Knowledge Bases from table-structured Web resources applied to the semantic-based requirements engineering methodology softwiki

Projecte realitzat mitjançant programa de mobilitat. Universität Leipzig. Fakultät für Mathematik und Informatik Institut für Informatik Betriebliche InformationsssystemeOver the last years the use of the Internet by users has evolved drastically from just consulting to publishing, sharing and modifying contents, turning the Internet into a social net in which the possibilities to collaborate and communicate grow every day bigger. A good example are the Wiki systems, which are collaborative, content-focused platforms in which the work of a community is the key to its good performance. Another of the biggest web technology developments of the Internet nowadays is the so-called Semantic Web, a Web in which every piece of data has its context clearly specified and machines are able to understand it. The OntoWiki project merges both Semantic Web and Wiki technology, enabling the definition, modification and visualization of agile, distributed knowledge engineering scenarios. Profiting from the complex extension system of OntoWiki, the SoftWiki platform was born. Thanks to this tool and the associated Agile Requirements Engineering methodology, potentially very large and spatially separate stakeholder groups are able to gather, semantically enrich, classify and aggregate software requirements in an easy manner. Originally created from the desire to import non-semantic requirement data from the Google Code Issues platform to SoftWiki, the CSVLoad extension for OntoWiki enables importing plain data out of CSV table files into OntoWiki with the help of an administrator-defined RDF semantic template, defined with a modified subset of the Turtle (N3) language with support of input and mapping values. The use of CSVLoad and the already defined Google Code Issues Template makes importing the requirements of a project hosted in Google Code into SoftWiki (in other words, into a SWORE ontology) very easy. Some platforms permit exporting only a part (or in some cases none) of their information in standard formats like CSV or RDF. Instead they just show their data in HTML documents, which makes creating general, effective plain-to-semantic importing tools an extremely difficult (and in some cases impossible) task, forcing developers to build custom-made tools. The Gcode extension is a tool specifically built to extract additional requirements information from the Google Code Issues platform HTML code and, together with the CSVLoad tool, it turns importing all the requirements information from Google Code Issues into SoftWiki into an easy, automatic process. By comparing both extensions, their input data and features, the advantages of using structured, view-independent data compared to view-representation-embedded data (e.g. data in a HTML document) become clear. But this data needs a next step, the semantic mark-up, so that computers are able to know the context of the information in an expandable, flexible environment

Integrating Technology With Student-Centered Learning

Reviews research on technology's role in personalizing learning, its integration into curriculum-based and school- or district-wide initiatives, and the potential of emerging digital technologies to expand student-centered learning. Outlines implications

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

Stavovi studenata prema primjeni mobilne proširene stvarnosti u visokom obrazovanju

Virtual (VR) and augmented reality (AR) have emerged from the framework of academic and industrial laboratories and have acquired global attention. Currently, the focus shifted from the technologies themselves to finding adequate teaching and learning applications. In this paper, the students\u27 attitudes toward the application of mobile AR (MAR) in higher education (HE) were researched (with a focus on augmented textbooks). The results showed that the students have a mostly positive attitude, and it is concluded that there was no statistically significant difference between the STEM (science, technology, engineering, and mathematics) and non-STEM students\u27 opinions regarding this topic. Based on the results, the further research and integration of this technology into HE settings can be suggested.Virtualna i proširena stvarnost izašle su iz okvira akademskih i industrijskih laboratorija i stekle su globalnu pozornost. Trenutačno se fokus pomaknuo sa samih tehnologija prema pronalaženju odgovarajuće primjene u poučavanju i učenju. U ovom radu istraživani su stavovi studenata o primjeni mobilne proširene stvarnosti u visokom obrazovanju (s naglaskom na augmentiranim udžbenicima). Rezultati su pokazali da studenti imaju uglavnom pozitivan stav, a zaključeno je i da ne postoji statistički značajna razlika u mišljenju između STEM (znanost, tehnologija, inženjerstvo i matematika) i ne-STEM studenata o ovoj temi. Na temelju rezultata istraživanja mogu se predložiti daljnja istraživanja i integracija ove tehnologije u postavke visokog obrazovanja

A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation

Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback