Privacy Requirements for Learning Analytics – from Policies to Technical Solutions (original) (raw)
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The influence of data protection and privacy frameworks on the design of learning analytics systems
Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017
Learning analytics open up a complex landscape of privacy and policy issues, which will influence how learning analytics systems and practices are designed. Research and development is governed by regulations for data storage and management, and by research ethics. Consequently, when moving solutions out the research labs implementers meet constraints defined in national laws and justified in privacy frameworks. This paper explores how the OECD, APEC and EU privacy frameworks seek to regulate data privacy, with significant implications for the discourse of learning, and ultimately, an impact on the design of tools, architectures and practices that now are on the drawing board. A detailed list of requirements for learning analytics systems is developed, based on the new legal requirements defined in the European General Data Protection Regulation, which from 2018 will be enforced as European law. The paper also gives an initial account of how the privacy discourse in Europe, Japan, South-Korea and China is developing and reflects upon the possible impact of the different privacy frameworks on the design of LA privacy solutions in these countries. This research contributes to knowledge of how concerns about privacy and data protection related to educational data can drive a discourse on new approaches to privacy engineering based on the principles of Privacy by Design. For the LAK community, this study represents the first attempt to conceptualise the issues of privacy and learning analytics in a cross-cultural context. The paper concludes with a plan to follow up this research on privacy policies and learning analytics systems development with a new international study. CCS Concepts Security and privacy → Privacy protections • General and reference~Design • Security and privacy~Social aspects of security and privacy • Security and privacy~Privacy protections • Applied computing~E-learning
Privacy in Learning Analytics – Implications for System Architecture
2015
This paper explores the field of ICT standardisation related to learning analytics, a new class of technologies being introduced to schools, universities and further education as a consequence of increased access to data from learning activities. Learning analytics has implication for how the individual manages data and knowledge about herself and her learning, highlighting issues of privacy, ownership of data, and consent to share and use data, – issues that are not yet been fully discussed in the field of learning technology development in general, and standardisation of learning technologies in particular. What do these issues mean for standardisation and design of LA architectures? Based on requirements of open architecture, transparency and trust, and ownership and consent this paper proposes a search architecture for learning analytics based on open and linked data. The proposed middle layer highlights dynamic usage agreements and student agency and represents an alternative a...
Journal of Learning Analytics, 2016
Studies have shown that issues of privacy, control of data, and trust are essential to implementation of learning analytics systems. If these issues are not addressed appropriately systems will tend to collapse due to legitimacy crisis, or they will not be implemented in the first place due to resistance from learners, their parents, or their teachers. This paper asks what it means to give priority to privacy in terms of data exchange and application design and offers a conceptual tool, a Learning Analytics Design Space model, to ease the requirement solicitation and design for new learning analytics solutions. The paper argues the case for privacy-driven design as an essential part of learning analytics systems development. A simple model defining a solution as the intersection of an approach, a barrier, and a concern is extended with a process focussing on design justifications to allow for an incremental development of solutions. This research is exploratory of nature, and furthe...
Ethical and privacy issues in the design of learning analytics applications
Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16, 2016
Issues related to Ethics and Privacy have become a major stumbling block in application of Learning Analytics technologies on a large scale. Recently, the learning analytics community at large has more actively addressed the EP4LA issues, and we are now starting to see learning analytics solutions that are designed not only as an afterthought, but also with these issues in mind. The 2 nd EP4LA@LAK16 workshop will bring the discussion on ethics and privacy for learning analytics to a the next level, helping to build an agenda for organizational and technical design of LA solutions, addressing the different processes of a learning analytics workflow.
Research and Practice in Technology Enhanced Learning
Privacy and data protection are a major stumbling blocks for a data-driven educational future. Privacy policies are based on legal regulations, which in turn get their justification from political, cultural, economical and other kinds of discourses. Applied to learning analytics, do these policies also need a pedagogical grounding? This paper is based on an actual conundrum in developing a technical specification on privacy and data protection for learning analytics for an international standardisation organisation. Legal arguments vary a lot around the world, and seeking ontological arguments for privacy does not necessarily lead to a universal acclaim of safeguarding the learner meeting the new data-driven practices in education. Maybe it would be easier to build consensus around educational values, but is it possible to do so? This paper explores the legal and cultural contexts that make it a challenge to define universal principles for privacy and data protection. If not universal principles, consent could be the point of departure for assuring privacy? In education, this is not necessarily the case as consent will be balanced by organisations' legitimate interests and contract. The different justifications for privacy, the legal obligation to separate analysis from intervention, and the way learning and teaching works makes it necessary to argue data privacy from a pedagogical perspective. The paper concludes with three principles that are proposed to inform an educational maxim for privacy and data protection in learning analytics.
LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox
Journal of Learning Analytics, 2016
To find a balance between learning analytics research and individual privacy learning analytics initiatives need to appropriately address ethical, privacy and data protection issues and comply with relevant legal regulations. A range of general guidelines, model codes, and principles for handling ethical issues and for appropriate data and privacy protection exist, which may serve the consideration of these topics in a learning analytics context. The importance and significance of data security and protection are also reflected in national and international laws and directives, where data protection is usually considered as a fundamental right. Existing guidelines, approaches and relevant regulations served as a basis for elaborating a comprehensive privacy and data protection framework for the LEA’s BOX project. It comprises a set of eight principles to derive implications for ensuring an ethical treatment of personal data in a learning analytics platform and its services. The priv...
The privacy paradox and its implications for learning analytics
Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 2020
Learning analytics promises to support adaptive learning in higher education. However, the associated issues around privacy protection, especially their implications for students as data subjects, has been a hurdle to wide-scale adoption. In light of this, we set out to understand student expectations of privacy issues related to learning analytics and to identify gaps between what students desire and what they expect to happen or choose to do in reality when it comes to privacy protection. To this end, an investigation was carried out in a UK higher education institution using a survey (N=674) and six focus groups (26 students). The study highlight a number of key implications for learning analytics research and practice: (1) purpose, access, and anonymity are key benchmarks of ethics and privacy integrity; (2) transparency and communication are key levers for learning analytics adoption; and (3) information asymmetry can impede active participation of students in learning analytics. CCS CONCEPTS • Applied computing → Computer-assisted instruction; • Humancentered computing → Empirical studies in HCI .
Privacy engineering for learning analytics in a global market
Campus-wide Information Systems, 2019
Purpose-Privacy is a culturally universal process; however, in the era of Big Data privacy is handled very differently in different parts of the world. This is a challenge when designing tools and approaches for the use of educational Big Data and learning analytics in a global market. The purpose of this paper is to explore the concept of information privacy in a cross-cultural setting to define a common point of reference for privacy engineering. Design / Methodology / Approach-The paper follows a conceptual exploration approach. Conceptual work on privacy in educational big data and learning analytics in China and the West is contrasted with the general discussion of privacy in a large corpus of literature and recent research. As much of the discourse on privacy has an American or European bias, intimate knowledge of Chinese education is used to test the concept of privacy and to drive exploration of how information privacy is perceived in different cultural and educational settings. Findings-The findings indicate that there are problems using privacy concepts found in European and North-American theories to inform privacy engineering for a cross-cultural market in the era of Big Data. Theories based on individualism and ideas of control of private information do not capture current global digital practice. The paper discusses how a contextual and culture-aware understanding of privacy could be developed to inform privacy engineering without letting go of universally shared values. The paper concludes with questions that need further research to fully understand information privacy in education. Originality / value-As far as we know, this paper is the first attempt to discuss-from a comparative and cross-cultural perspective-information privacy in an educational context in the era of Big Data. The paper presents initial explorations of a problem that needs urgent attention if good intentions of privacy supportive educational technologies are to be turned 2 into more than political slogans.
Ethical and privacy principles for learning analytics
He is working on research projects exploring how technology can be used to understand and influence human behavior. He has experience in the use of digital devices in areas such as behavioral analytics, social networks, computer-supported collaboration, personalization and technology-enhanced learning. George Siemens is the Executive Abstract The massive adoption of technology in learning processes comes with an equally large capacity to track learners. Learning analytics aims at using the collected information to understand and improve the quality of a learning experience. The privacy and ethical issues that emerge in this context are tightly interconnected with other aspects such as trust, accountability and transparency. In this paper, a set of principles is identified to narrow the scope of the discussion and point to pragmatic approaches to help design and research learning experiences where important ethical and privacy issues are considered. Introduction: privacy in learning environments The use of information and communication technology has significantly changed how learning experiences are conceived and deployed. The widespread use of various digital devices together with cloud computing allows for learning scenarios not previously considered. Students are now able to access a myriad of learning resources, interact with applications focusing on a specific topic, enhance their experience in virtual environments, augment reality and connect with others through social networks. The progress of technology evolves together with the capacity to record the events occurring in a learning environment. Every interaction and resource accessed can be captured and stored. As a consequence, learning scenarios can now be analyzed using big-data analytics techniques. Although the use of new technology is shaping the way we learn, a more significant change may derive from the use of big-data analytics (Siemens & Long, 2011).
Ethical and privacy issues in the application of learning analytics
Proceedings of the Fifth International Conference on Learning Analytics And Knowledge - LAK '15, 2015
The large-scale production, collection, aggregation, and processing of information from various learning platforms and online environments have led to ethical and privacy concerns regarding potential harm to individuals and society. In the past, these types of concern have impacted on areas as diverse as computer science, legal studies and surveillance studies. Within a European consortium that brings together the EU project LACE, the SURF SIG Learning Analytics, the Apereo Foundation and the EATEL SIG dataTEL, we aim to understand the issues with greater clarity, and to find ways of overcoming the issues and research challenges related to ethical and privacy aspects of learning analytics practice. This interactive workshop aims to raise awareness of major ethics and privacy issues. It will also be used to develop practical solutions to advance the application of learning analytics technologies.