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Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34
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Item Personal Data Broker Instead of Blockchain for Students’ Data Privacy Assurance(Springer, 2019-04-01) Amo, D.; Fonseca, D.; Alier, M.; García-Peñalvo, F. J.; Casañ, M. J.Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is showcased in recent cases seen in media of bad use of students’ personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solu-tion. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker.Item Learning Analytics as a Breakthrough in Educational Improvement(Springer Singapore, 2020-05-12) García-Peñalvo, F. J.Learning analytics has become a reference area in the field of Learning Technologies as the mixture of different technical and methodological approaches in the capture, treatment and representation of educational data for later use in decision-making pro-cesses. With approximately ten years of development, it can be considered that learn-ing analytics have abandoned their stage of dispersion and are heading towards a state of maturity that will position them as a fundamental piece in educational practice mediated by technology. However, it cannot be ignored that the power and good-ness of these analytics must be channelled to improve learning itself and, therefore, the learning-teaching process, always acting from an ethical sense and preserving the privacy of the people who participate because it is straightforward to invade personal spaces in favour of the objectives sought. This chapter presents, from a conceptual perspective, the reference models that support the creation of educational strategies based on learning analytics that integrate the most current trends in the field, defined from a critical perspective that balances the undoubted benefits with the potential risksItem GDPR Security and Confidentiality compliance in LMS’ a problem analysis and engineering solution proposal(ACM, 2019-10-16) Amo, D.; Alier, M.; García-Peñalvo, F. J.; Fonseca, D.; Casany, M. J.We have studied the main Learning Management Systems (LMSs) to comprehend how personal data is processed and stored. We found that all the users' personal information, activity, and logs are stored unencrypted on the server filesystem and databases. A user with access to such resources may have full access to all the personal information and meta-information stored. Therefore, the LMSs are very vulnerable to information leaks in front of targeted hacker attacks due to weak GDPR compliance. In this paper, we analyze this problem from a technical and operational perspective for the open-source market leader LMS Moodle, and we propose a solution and a prototype of implementation.Item Clickstream for learning analytics to assess students’ behavior with Scratch(2019-01-01) Amo Filvà, D.; Alier Forment, M.; García-Peñalvo, F. J.; Fonseca Escudero, D.; Casañ, M. J.The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. The field of learning analytics has been a common practice in research since last years due to their great possibilities in terms of learning improvement. Both, Big and Small data techniques support the analysis cycle of learning analytics and risk of students’ failure prediction. Such possibilities can be a strong positive contribution to the field of computational practice such as programming. Our main objective was to help teachers in their assessments through to make those possibilities effective. Thus, we have developed a functional solution to categorize and understand students’ behavior in programming activities based in Scratch. Through collection and analysis of data generated by students’ clicks in Scratch, we proceed to execute both exploratory and predictive analytics to detect patterns in students’ behavior when developing solutions for assignments. We concluded that resultant taxonomy could help teachers to better support their students by giving real-time quality feedback and act before students deliver incorrectly or at least incomplete tasks.Item Using web analytics tools to improve the quality of educational resources and the learning process of students in a gamified situation(IATED Academy, 2018-03-05) Amo Filva, D.; Valls, A.; Alier Forment, Marc; Canaleta, X.; García-Peñalvo, F. J.; Fonseca, D.; Redondo, E.In this paper we propose a businessification approximation to measure and analyse students' engagement in a gamified learning context. Gamification in education is used to enhance students experience and improve learning outcomes. Its technics such as points, leaderboard, badges or ranking are also used in learning instructions with the aim to improve students' engagement. This engagement can be considered as the metric to measure the success of gamified instructions. The gamification model can also be used in an online learning environment. In this virtual context teachers have to have some tools to see what happens during the learning process. Such virtual context is usually web based. In this specific context the resources used such as images, videos or audios are fundamental to engage students. In order to help teachers to enhance engagement we propose the use of web analytical tools in such web based gamified learning contexts to track, analyse and finally enhance such resourcesItem Comparing Hierarchical Trees in Statistical Implicative Analysis & Hierarchical Cluster in Learning Analytics(ACM, 2017-10-18) Pazmiño-Maji, R. A.; García-Peñalvo, F. J.; Conde-González, M. Á.Learning Analytics has been and is still an emerging technology in education; the amount of research on learning analysis is increasing every year. The integration of new open source tools, analysis methods, and other calculation options are important. This paper aims to compare hierarchical trees in Statistical Implicative Analysis (SIA) and some hierarchical clusters in Learning Analytics. To this end, we must use a quasi-experimental design with random binary data. A comparison is about the time it takes to evaluate the function for execute the four cluster algorithms: cohesion tree (ASI), similarity tree (ASI), agnes (cluster R package) and hclust (R base function). This paper provides an alternative hierarchical cluster used in Statistical Implicative Analysis that is possible to use in Learning Analytics (LA). Also, provides a comparative R-program used and identifies future research about software performance.