Personal Data Broker: A Solution to Assure Data Privacy in EdTech

Fonseca, David; Alier, Marc; García-Peñalvo, Francisco José; Guerrero, María José Casany; Alsina, María Inmaculada Bordas

doi:10.1007/978-3-030-21814-0_1

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…Most LMS installations are not handling the digital identity and confidentiality of the students properly [11]. Such is the case of Moodle, which provides limited support for the GDPR [12]. In 2018, the 3.4 release of Moodle included support for compliance with the GDPR.…”

Section: Introductionmentioning

confidence: 99%

Protected Users: A Moodle Plugin To Improve Confidentiality and Privacy Support through User Aliases

et al. 2020

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The privacy policies, terms, and conditions of use in any Learning Management System (LMS) are one-way contracts. The institution imposes clauses that the student can accept or decline. Students, once they accept conditions, should be able to exercise the rights granted by the General Data Protection Regulation (GDPR). However, students cannot object to data processing and public profiling because it would be conceived as an impediment to teachers to execute their work with normality. Nonetheless, regarding GDPR and consulted legal advisors, a student could claim identity anonymization in the LMS, if adequate personal justifications are provided. Per contra, the current LMSs do not have any functionality that enables identity anonymization. This is a big problem that generates undesired situations which urgently requires a definitive solution. In this work, we surveyed students and teachers to validate the feasibility and acceptance of using aliases to anonymize their identity in LMSs as a sustainable solution to the problem. Considering the positive results, we developed a user-friendly plugin for Moodle that enables students’ identity anonymization by the use of aliases. This plugin, presented in this work and named Protected users, is publicly available online at GitHub and published under GNU General Public License.

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Section: Introductionmentioning

confidence: 99%

Protected Users: A Moodle Plugin To Improve Confidentiality and Privacy Support through User Aliases

et al. 2020

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“…The analytic approaches to educational data must fulfil the requirements of both kinds of data. The origin of this data can be either internal or external depending on the EdTech services that are applied [61]. For example, in times of evaluation, there is a need to know the student's status and other data coming from different academic, learning and behavioral interactions; this data can be hosted both internal platforms and in third-party services available in the company's cloud.…”

Section: Basic System Architecturementioning

confidence: 99%

Educational Warehouse: Modular, Private and Secure Cloudable Architecture System for Educational Data Storage, Analysis and Access

et al. 2021

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Data in the educational context are becoming increasingly important in decision-making and teaching-learning processes. Similar to the industrial context, educational institutions are adopting data-processing technologies at all levels. To achieve representative results, the processes of extraction, transformation and uploading of educational data should be ubiquitous because, without useful data, either internal or external, it is difficult to perform a proper analysis and to obtain unbiased educational results. It should be noted that the source and type of data are heterogeneous and that the analytical processes can be so diverse that it opens up a practical problem of management and access to the data generated. At the same time, ensuring the privacy, identity, confidentiality and security of students and their data is a “sine qua non” condition for complying with the legal issues involved while achieving the required ethical premises. This work proposes a modular and scalable data system architecture that solves the complexity of data management and access. On the one hand, it allows educational institutions to collect any data generated in both the teaching-learning and management processes. On the other hand, it will enable external access to this data under appropriate privacy and security conditions.

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“…Current research in data fragility analyzes solutions in cloud computing or the blockchain, either centralized or distributed databases, but always outside the control of educational institutions [18,19]. Outsourced solutions in the cloud computing may present some benefits that allow antifragility into educational institutions, as stated by Gong et al [20] when describing cloud computing as "service-oriented, loose coupling, strong fault-tolerant, business model and eas[y to] use".…”

Section: Local Instead Of Outsidementioning

confidence: 99%

A Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Study

et al. 2021

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Educational institutions are transferring analytics computing to the cloud to reduce costs. Any data transfer and storage outside institutions involve serious privacy concerns, such as student identity exposure, rising untrusted and unnecessary third-party actors, data misuse, and data leakage. Institutions that adopt a “local first” approach instead of a “cloud computing first” approach can minimize these problems. The work aims to foster the use of local analytics computing by offering adequate nonexistent tools. Results are useful for any educational role, even investigators, to conduct data analysis locally. The novelty results are twofold: an open-source JavaScript library to analyze locally any educational log schema from any LMS; a front-end to analyze Moodle logs as proof of work of the library with different educational metrics and indicator visualizations. Nielsen heuristics user experience is executed to reduce possible users’ data literacy barrier. Visualizations are validated by surveying teachers with Likert and open-ended questions, which consider them to be of interest, but more different data sources can be added to improve indicators. The work reinforces that local educational data analysis is feasible, opens up new ways of analyzing data without data transfer to third parties while generating debate around the “local technologies first” approach adoption.

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Personal Data Broker: A Solution to Assure Data Privacy in EdTech

Cited by 12 publications

References 12 publications

Protected Users: A Moodle Plugin To Improve Confidentiality and Privacy Support through User Aliases

Protected Users: A Moodle Plugin To Improve Confidentiality and Privacy Support through User Aliases

Educational Warehouse: Modular, Private and Secure Cloudable Architecture System for Educational Data Storage, Analysis and Access

A Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Study

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