short-paper

Open access

Learning Analytics as a Service for Empowered Learners: From Data Subjects to Controllers

Authors:

Carla Barreiros,

Philipp Leitner,

Elisabeth Staudegger,

Stefanie LindstaedtAuthors Info & Claims

LAK21: LAK21: 11th International Learning Analytics and Knowledge Conference

Pages 475 - 481

https://doi.org/10.1145/3448139.3448186

Published: 12 April 2021 Publication History

All formats PDF

Abstract

As Learning Analytics (LA) in the higher education setting increasingly transitions from a field of research to an implemented matter of fact of the learner's experience, the demand of practical guidelines to support its development is rising. LA Policies bring together different perspectives, like the ethical and legal dimensions, into frameworks to guide the way. Usually the first time learners get in touch with LA is at the act of consenting to the LA tool. Utilising an ethical (TRUESSEC) and a legal framework (GDPR), we question whether sincere consent is possible in the higher education setting. Drawing upon this premise, we then show how it might be possible to recognise the autonomy of the learner by providing LA as a service, rather than an intervention. This could indicate a paradigm shift towards the learner as empowered demander. At last, we show how this might be incorporated within the GDPR by also recognising the demand of the higher education institutions to use the learner's data at the same time. These considerations will in the future influence the development of our own LA policy: a LA criteria catalogue.

References

[1]

AI HLEG. 2019. Ethics Guidelines for Trustworthy AI. Retrieved October 25, 2020 from https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

[2]

Article 29 Data Protection Working Party. 2014. Opinion 05/2014 on Anonymisation Techniques. WP 216, Adopted on 10 April 2014.

[3]

Jacqueline Bichsel. 2012. Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations. https://doi.org/10.13140/RG.2.1.1064.6244

[4]

Louise Bunce, Amy Baird, and Siân E. Jones. 2017. The student-as-consumer approach in higher education and its effects on academic performance. Studies in Higher Education 42, 11, 1958–1978. https://doi.org/10.1080/03075079.2015.1127908

[5]

Louise Bunce, Amy Baird, and Siân E. Jones. 2017. The student-as-consumer approach in higher education and its effects on academic performance. Studies in Higher Education 42, 11, 1958–1978. https://doi.org/10.1080/03075079.2015.1127908

[6]

Fred H. Cate and Viktor Mayer-Schönberger. 2013. Notice and consent in a world of Big Data. International Data Privacy Law 3, 2, 67–73. https://doi.org/10.1093/idpl/ipt005

[7]

Andrew N. Cormack. 2016. A Data Protection Framework for Learning Analytics. Journal of Learning Analytics 3, 1. https://doi.org/10.18608/jla.2016.31.6

[8]

Hendrik Drachsler and Wolfgang Greller. 2016. Privacy and analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16. ACM Press, New York, New York, USA, 89–98. https://doi.org/10.1145/2883851.2883893

Digital Library

[9]

European Court of Justice. 2018. Facebook Fanpage. C‑210/16.

[10]

European Court of Justice. 2019. Fashion ID. C-40/17.

[11]

European Data Protection Board. 2020. Guidelines 05/2020 on consent under Regulation 2016/679. Version 1.1 - Adopted on 4 May 2020.

[12]

European Data Protection Board. 2020. Guidelines 07/2020 on the concepts of controller and processor in the GDPR. Version 1.0, Adopted on 02 September 2020.

[13]

European Union. 2016. Consolidated text: Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance). RL 2016/679/EU.

[14]

Tore Hoel, Dai Griffiths, and Weiqin Chen. The influence of data protection and privacy frameworks on the design of learning analytics systems. In Proceedings of the seventh international learning analytics & knowledge conference, 243–252.

[15]

Kyle M. L. Jones. 2019. Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. Int J Educ Technol High Educ 16, 1. https://doi.org/10.1186/s41239-019-0155-0

[16]

Mohammad Khalil and Martin Ebner. 2016. De-Identification in Learning Analytics. Learning Analytics 3, 1. https://doi.org/10.18608/jla.2016.31.8

[17]

LACE. Learning Analytics Community Exchange. 2020. Retrieved October 20, 2020 from https://cordis.europa.eu/project/id/619424

[18]

LEA's Box. LEarning Analytics Toolbox. 2020. Retrieved October 20, 2020 from http://kti.tugraz.at/css/projects/leas-box/

[19]

Philipp Leitner, Mohammad Khalil, and Martin Ebner. 2017. Learning Analytics in Higher Education—A Literature Review. In Learning Analytics: Fundaments, Applications, and Trends, A. Peña-Ayala, Ed. Studies in Systems, Decision and Control. Springer International Publishing, Cham, 1–23. https://doi.org/10.1007/978-3-319-52977-6_1

[20]

Phillip D. Long, George Siemens, Grainne Conole, and Dragan Gašević. 2011. Proceedings of the 1st International Conference on Learning Analytics and Knowledge.

Digital Library

[21]

Alistair McCulloch. 2009. The student as co‐producer: learning from public administration about the student–university relationship. Studies in Higher Education 34, 2, 171–183. https://doi.org/10.1080/03075070802562857

[22]

Elizabeth Nixon, Mike Molesworth, and Richard Scullion. 2010. The marketisation of higher education. The student as consumer. Routledge, Abingdon, Oxon, New York, NY.

[23]

Abelardo Pardo and George Siemens. 2014. Ethical and privacy principles for learning analytics. Br J Educ Technol 45, 3, 438–450. https://doi.org/10.1111/bjet.12152

[24]

Paul Prinsloo. 2018. Student Consent in Learning Analytics. In Learning Analytics in Higher Education, J. Lester, Ed. Routledge, 118–139. https://doi.org/10.4324/9780203731864-6

[25]

Paul Prinsloo and Sharon Slade. 2013. An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge - LAK '13. ACM Press, New York, New York, USA, 240. https://doi.org/10.1145/2460296.2460344

Digital Library

[26]

Lynne D. Roberts, Joel A. Howell, Kristen Seaman, and David C. Gibson. 2016. Student Attitudes toward Learning Analytics in Higher Education: "The Fitbit Version of the Learning World". Frontiers in psychology 7, 1959. https://doi.org/10.3389/fpsyg.2016.01959

[27]

Carol Robinson. 2012. Student engagement. Jnl of Applied Research in HE 4, 2, 94–108. https://doi.org/10.1108/17581181211273039

[28]

Niall Sclater. 2016. Developing a Code of Practice for Learning Analytics. Learning Analytics 3, 1. https://doi.org/10.18608/jla.2016.31.3

[29]

SHEILA Project. 2020. Retrieved October 20, 2020 from https://sheilaproject.eu/

[30]

George Siemens. 2013. Learning Analytics. American Behavioral Scientist 57, 10, 1380–1400. https://doi.org/10.1177/0002764213498851

[31]

George Siemens, Shane Dawson, and Grace Lynch. 2013. Improving the quality and productivity of the higher education sector. Policy and Strategy for Systems-Level Deployment of Learning Analytics. Society for Learning Analytics Research.

[32]

Sharon Slade and Paul Prinsloo. 2013. Learning Analytics. Ethical Issues and Dilemmas. American Behavioral Scientist 57, 10, 1510–1529. https://doi.org/10.1177/0002764213479366

[33]

Sharon Slade and Paul Prinsloo. 2014. Student perspectives on the use of their data: between intrusion, surveillance and care. Challenges for Research into Open & Distance Learning: Doing Things Better – Doing Better Things, 291–300.

[34]

Sharon Slade, Paul Prinsloo, and Mohammad Khalil. 2019. Learning analytics at the intersections of student trust, disclosure and benefit. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge. ACM, New York, NY, USA, 235–244. https://doi.org/10.1145/3303772.3303796

Digital Library

[35]

Sharon Slade and Alan Tait. 2019. Global guidelines: Ethics in learning analytics. Retrieved 25.20.2020 from https://www.icde.org/knowledge-hub/the-aim-of-the-guidelines-is-to-identify-which-core-principles-relating-to-ethics-are-core-to-all-and-where-there-is-legitimate-differentiation-due-to-separate-legal-or-more-broadly-cultural-env-5mppk

[36]

Christina M. Steiner, Michael D. Kickmeier-Rust, and Dietrich Albert. 2016. LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox. Learning Analytics 3, 1. https://doi.org/10.18608/jla.2016.31.5

[37]

TRUESSEC.eu. 2018. Cybersecurity and privacy Criteria Catalogue for assurance and certification. Retrieved October 25, 2020 from https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5be07569f&appId=PPGMS

[38]

Yi-Shan Tsai, Pedro M. Moreno-Marcos, Ioana Jivet, Maren Scheffel, Kairit Tammets, Kaire Kollom, and Dragan Gašević. 2018. The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics. Learning Analytics 5, 3. https://doi.org/10.18608/jla.2018.53.2

[39]

George Siemens. 2012. Learning analytics: envisioning a research discipline and a domain of practice. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK '12). Association for Computing Machinery, New York, NY, USA, 4–8. https://doi.org/10.1145/2330601.2330605

Digital Library

[40]

Charles Lang, Leah P. Macfadyen, Sharon Slade, Paul Prinsloo, and Niall Sclater. 2018. The complexities of developing a personal code of ethics for learning analytics practitioners: implications for institutions and the field. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK '18). Association for Computing Machinery, New York, NY, USA, 436–440. https://doi.org/10.1145/3170358.3170396

Digital Library

[41]

Leah Macfadyen. 2017. What Does a Learning Analytics Practitioner Need to Know? MLA/BLAC@LAK.

Cited By

Prinsloo PSlade SKhalil M(2023)Multimodal learning analytics—In‐between student privacy and encroachment: A systematic reviewBritish Journal of Educational Technology10.1111/bjet.1337354:6(1566-1586)Online publication date: 21-Aug-2023
https://doi.org/10.1111/bjet.13373
Barreiros CLeitner PEbner MVeas ELindstaedt S(2023)Students in Focus – Moving Towards Human-Centred Learning AnalyticsPracticable Learning Analytics10.1007/978-3-031-27646-0_5(77-94)Online publication date: 31-Mar-2023
https://doi.org/10.1007/978-3-031-27646-0_5
Alachiotis NKotsiantis SSakkopoulos EVerykios V(2022)Supervised machine learning models for student performance predictionIntelligent Decision Technologies10.3233/IDT-21025116:1(93-106)Online publication date: 18-Apr-2022
https://doi.org/10.3233/IDT-210251
Show More Cited By

Learning Analytics as a Service for Empowered Learners: From Data Subjects to Controllers
1. Applied computing

Recommendations

Learning Analytics Solution for Reducing Learners' Course Failure Rate
T4E '15: Proceedings of the 2015 IEEE Seventh International Conference on Technology for Education (T4E)

In recent years, learning analytics solutions have highly appealed to the higher education community who mainly focuses on improving the learning process, self-regulated learning skills, and learners' success rate. Learning analytics has to deal with ...
Supporting learners' self-regulated learning in Massive Open Online Courses
Abstract
In MOOCs, learners are typically presented with great autonomy over their learning process. Therefore, learners should engage in self-regulated learning (SRL) in order to successfully study in a MOOC. Learners however often struggle to self-...
Highlights
- Learners struggle to regulate their learning in massive open online courses (MOOCs).
- A self-regulated learning (SRL) intervention was implemented in three MOOCs.
- Learners' SRL was measured with trace data variables.
- ...
Meeting Learners' Needs through Project-Based Learning

There is a significant divide between what students want and what students receive in the classroom. Students increasingly lack interest and motivation to participate and learn. Instructional practices within and across schools vary widely, creating ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

LAK21: LAK21: 11th International Learning Analytics and Knowledge Conference

April 2021

645 pages

ISBN:9781450389358

DOI:10.1145/3448139

Program Chairs:
Maren Scheffel
Ruhr University Bochum, Germany
,
Nia Dowell
University of California, Irvine, USA
,
Srecko Joksimovic
University of South Australia, Australia
,
George Siemens
University of Texas, Arlington, USA & University of South Australia, Australia

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Short-paper
Research
Refereed limited

Conference

LAK21

LAK21: 11th International Learning Analytics and Knowledge Conference

April 12 - 16, 2021

CA, Irvine, USA

Acceptance Rates

Overall Acceptance Rate 236 of 782 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
839
Total Downloads

Downloads (Last 12 months)177
Downloads (Last 6 weeks)25

Reflects downloads up to 12 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Prinsloo PSlade SKhalil M(2023)Multimodal learning analytics—In‐between student privacy and encroachment: A systematic reviewBritish Journal of Educational Technology10.1111/bjet.1337354:6(1566-1586)Online publication date: 21-Aug-2023
https://doi.org/10.1111/bjet.13373
Barreiros CLeitner PEbner MVeas ELindstaedt S(2023)Students in Focus – Moving Towards Human-Centred Learning AnalyticsPracticable Learning Analytics10.1007/978-3-031-27646-0_5(77-94)Online publication date: 31-Mar-2023
https://doi.org/10.1007/978-3-031-27646-0_5
Alachiotis NKotsiantis SSakkopoulos EVerykios V(2022)Supervised machine learning models for student performance predictionIntelligent Decision Technologies10.3233/IDT-21025116:1(93-106)Online publication date: 18-Apr-2022
https://doi.org/10.3233/IDT-210251
Mutimukwe CViberg OOberg LCerratto‐Pargman T(2022)Students' privacy concerns in learning analytics: Model developmentBritish Journal of Educational Technology10.1111/bjet.1323453:4(932-951)Online publication date: 19-May-2022
https://doi.org/10.1111/bjet.13234
Veljanova HBarreiros CGosch NStaudegger EEbner MLindstaedt S(2022)Towards Trustworthy Learning Analytics Applications: An Interdisciplinary Approach Using the Example of Learning DiariesHCI International 2022 Posters10.1007/978-3-031-06391-6_19(138-145)Online publication date: 16-Jun-2022
https://doi.org/10.1007/978-3-031-06391-6_19

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents