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The influence of data protection and privacy frameworks on the design of learning analytics systems

Published: 13 March 2017 Publication History

Abstract

Learning analytics open up a complex landscape of privacy and policy issues, which, in turn, 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.

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Cited By

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  • (2024)Preserving Both Privacy and Utility in Learning AnalyticsIEEE Transactions on Learning Technologies10.1109/TLT.2024.339376617(1655-1667)Online publication date: 2024
  • (2024)Navigating the ethical landscape of multimodal learning analytics: a guiding frameworkEthics in Online AI-based Systems10.1016/B978-0-443-18851-0.00014-7(25-53)Online publication date: 2024
  • (2023)Measuring and classifying students' cognitive load in pen‐based mobile learning using handwriting, touch gestural and eye‐tracking dataBritish Journal of Educational Technology10.1111/bjet.1339455:2(625-653)Online publication date: 30-Sep-2023
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LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference
March 2017
631 pages
ISBN:9781450348706
DOI:10.1145/3027385
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 March 2017

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Author Tags

  1. data protection
  2. data protection by default
  3. data protection by design
  4. learning analytics
  5. learning analytics process requirements
  6. learning analytics systems design
  7. personal information
  8. privacy by design
  9. privacy frameworks

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LAK '17
LAK '17: 7th International Learning Analytics and Knowledge Conference
March 13 - 17, 2017
British Columbia, Vancouver, Canada

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LAK '17 Paper Acceptance Rate 36 of 114 submissions, 32%;
Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

View all
  • (2024)Preserving Both Privacy and Utility in Learning AnalyticsIEEE Transactions on Learning Technologies10.1109/TLT.2024.339376617(1655-1667)Online publication date: 2024
  • (2024)Navigating the ethical landscape of multimodal learning analytics: a guiding frameworkEthics in Online AI-based Systems10.1016/B978-0-443-18851-0.00014-7(25-53)Online publication date: 2024
  • (2023)Measuring and classifying students' cognitive load in pen‐based mobile learning using handwriting, touch gestural and eye‐tracking dataBritish Journal of Educational Technology10.1111/bjet.1339455:2(625-653)Online publication date: 30-Sep-2023
  • (2023)Understanding privacy and data protection issues in learning analytics using a systematic reviewBritish Journal of Educational Technology10.1111/bjet.1338854:6(1715-1747)Online publication date: 15-Sep-2023
  • (2023)Culture of Ethics in Adopting Learning AnalyticsAugmented Intelligence and Intelligent Tutoring Systems10.1007/978-3-031-32883-1_52(591-603)Online publication date: 22-May-2023
  • (2022)Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-CensoringProceedings on Privacy Enhancing Technologies10.2478/popets-2022-00442022:2(245-262)Online publication date: 3-Mar-2022
  • (2022)The Benefits of Learning Analytics for Education: An Analysis of the Experiences of Lithuanian General Education School TeachersActa Paedagogica Vilnensia10.15388/ActPaed.2022.48.848(129-142)Online publication date: 7-Nov-2022
  • (2022)Privacy by Design and Software EngineeringProceedings of the XXI Brazilian Symposium on Software Quality10.1145/3571473.3571480(1-10)Online publication date: 7-Nov-2022
  • (2022)Requirements Engineering for Machine Learning: A Review and Reflection2022 IEEE 30th International Requirements Engineering Conference Workshops (REW)10.1109/REW56159.2022.00039(166-175)Online publication date: Aug-2022
  • (2022)Privacy Preservation in eLearning: Exploration and Analysis2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET)10.1109/ITHET56107.2022.10031904(1-8)Online publication date: 7-Nov-2022
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