Recent advances in information and communication technologies (ICTs) have fostered the development of new methods and tools for exploring the increasingly large amounts of data that come from pedagogical domains [1–5]. These data have the potential to transform education into a personalized experience [6, 7] that meets the needs of each individual student [8]. 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) [14–16]. Educational data is also the basis for learning analytics [17–19], with an increasing focus on the way educational data is presented [20–22], how users interact with the data [23–26], and data privacy and security [27–30]. 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 is collected from collaborative learning environments [32–34], 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. When combined with others factors such as timing and context, these factors play an important role in understanding how students learn [42], the settings in which they learn [43], and the effectiveness of the educational approaches [44]. Extracting information from data to ultimately turn it into knowledge [45, 46]can contribute to draw a more comprehensive picture of student’s learning, which can empower students, parents, and educators as well as education stakeholders and policymakers [47]. Educational data usability and accessibility is even more relevant in the context of the global pandemic due to the SARS-CoV-2 virus, which causes COVID-19 disease. This situation is having an unprecedented impact on education. According to UNESCO [48], in the firsts months of 2020, the pandemic has affected 91.3% of the total number of students enrolled worldwide: over 1.5 billion people have been unable to receive face-to-face instruction because of the closure of schools and universities [49]. The consequences are more severe in emerging countries [50, 51] and to families affected by poverty and risk exclusion [52], presenting digital inequalities [53], and causing exclusion and inequality situations in vulnerable groups, ethnic minorities, and people with disabilities [54]. Significant challenges have been reported in the online transformation of educational activities [55, 56], particularly assessment processes [57]. Consequently, it is vital to improve access to educational technologies and reduce gaps in use and literacy [58]. A multi-disciplinary approach is required to deploy technological ecosystems [59, 60] that favor blended or online training, teacher and student training for the efficient use of educational technologies [61], and policies for both government and academic leaders to define strategies and manage uncertain scenarios [62]. In the context of educational data access, it is critical to ensure transparency, ethics, and individuals’ rights. This UAIS special issue builds on the work started in a number of previous special issues [63–66] and two international events:

/pdf/new-methods-and-technologies-for-enhancing-usability-and-3ir8ctj6ib.pdf

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

Internet of Things (IoT) has become a fundamental content of any engineering program due to the emerging need of experts in this field. However, the complexity of technologies that interact in IoT environments and the amount of different professional profiles required to design, implement and manage IoT environments, considering cybersecurity as a must, has led to a huge challenge in the educational world. This paper proposes an integral pedagogical strategy for learning IoT cybersecurity structured in three different stages, in a higher education institution. These stages focus not only on the content about IoT and cybersecurity but also on the competencies to acquire, the most suitable learning methodologies and the expected learning outcomes. The association of these concepts in each stage is detailed. Examples of courses are explained, the related competencies and learning outcomes are specified, and the contents and methodologies to achieve the expected results are described. An analysis of student results and stakeholder evaluations is provided to verify if the pedagogical strategy proposed is suitable. Furthermore, students’ feedback is included to corroborate the innovation, the suitability of the acquired skills, and the overall student satisfaction with the related courses and consequently with the proposed IoT cybersecurity pedagogical strategy.

https://www.mdpi.com/1424-8220/20/14/3970/pdf

An Integral Pedagogical Strategy for Teaching and Learning IoT Cybersecurity

In face-to-face learning environments, instructors (sub)consciously measure student engagement to obtain immediate feedback regarding the training they are leading. This constant monitoring process enables instructors to dynamically adapt the training activities according to the perceived student reactions, which aims to keep them engaged in the learning process. However, when shifting from face-to-face to synchronous virtual learning environments (VLEs), assessing to what extent students are engaged to the training process during the lecture has become a challenging and arduous task. Typical indicators such as students’ faces, gestural poses, or even hearing their voice can be easily masked by the intrinsic nature of the virtual domain (e.g., cameras and microphones can be turned off). The purpose of this paper is to propose a methodology and its associated model to measure student engagement in VLEs that can be obtained from the systematic analysis of more than 30 types of digital interactions and events during a synchronous lesson. To validate the feasibility of this approach, a software prototype has been implemented to measure student engagement in two different learning activities in a synchronous learning session: a masterclass and a hands-on session. The obtained results aim to help those instructors who feel that the connection with their students has weakened due to the virtuality of the learning environment.

/pdf/a-data-driven-approach-to-quantify-and-measure-students-2zm4h1cm.pdf

A Data-Driven Approach to Quantify and Measure Students’ Engagement in Synchronous Virtual Learning Environments

La toxoplasmosis es una enfermedad parasitaria en humanos, su agente causal, Toxoplasma gondii, un parasito protozoo intracelular obligado, es capaz de infectar a los animales de sangre caliente —incluyendo a los seres humanos—, quienes actuan como su huesped intermediario. El objetivo de este estudio fue detectar anticuerpos anti-toxoplasma IgG e IGM, para lo cual se utilizo el metodo de estudio prospectivo observacional con donadores voluntarios que acudieron al banco de sangre; el estudio de la deteccion del suero positivo a anticuerpos anti-toxoplasma IgG e IgM se realizo por metodo de quimioluminiscencia. En los resultados se encontraron personas positivas a los anticuerpos IgG e Ig M respectivamente, adultos y de ambos generos, los cuales tienen gatos y perros en sus casas y son residentes de Tijuana que viven en el area urbana. Debido a la seroprevalencia encontrada se recomienda mayor vigilancia con respecto a la presencia de este parasito en hemoderivados.

https://dialnet.unirioja.es/descarga/articulo/5662129.pdf

Anticuerpos Anti-Toxoplasma gondii en donadores voluntarios de sangre en Hospital General de Tijuana, México

The introduction of cloud technology in educational settings over the past ten years has enabled organizations to embrace a data-driven decision-making paradigm. Schools and colleges are undergoing rapid digital updating procedures due to the use of outside technological solutions in the cloud, affecting how students learn and are taught. Regarding data, teaching and learning processes are improved by technology that gathers and analyzes student data to present useful information. With this technological shift comes the pervasiveness of data thanks to cloud storage. This means that in many instances, outside the purview of schools and universities, certain actors may gather, manage, and treat educational data on private servers and data centers. This privatization allows data leaks, record manipulation, and unwanted access. To help primary and secondary schools understand what data-driven decision-making entails for an educational institution and what problems with data fragility are related to current educational technology and data academic management, the current paper outlines the main goals of the SPADATAS project, its organizational structure, and its key issues. It also offers tools and frameworks to safeguard the privacy, security, and confidentiality of students’ data. 

Security and Privacy in Academic Data Management at Schools: SPADATAS Project

During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—ranging from engineering to business, including computer science, networking, or analytics among others—which complicate the conception and deployment of academic programs and methodologies able to effectively train students in this discipline. The purpose of this paper is to propose a learning and teaching framework committed to train masters’ students in Big Data by conceiving an intelligent tutoring system aimed to (1) automatically tracking students’ progress, (2) effectively exploiting the diversity of their backgrounds, and (3) assisting the teaching staff on the course operation. Obtained results endorse the feasibility of this proposal and encourage practitioners to use this approach in other domains.

Xavier Solé-Beteta

Papers

Automatic tutoring system to support cross-disciplinary training in Big Data