research-article

Learning analytics at the intersections of student trust, disclosure and benefit

Authors:

Mohammad KhalilAuthors Info & Claims

LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge

Pages 235 - 244

https://doi.org/10.1145/3303772.3303796

Published: 04 March 2019 Publication History

Abstract

Evidence suggests that individuals are often willing to exchange personal data for (real or perceived) benefits. Such an exchange may be impacted by their trust in a particular context and their (real or perceived) control over their data.

Students remain concerned about the scope and detail of surveillance of their learning behavior, their privacy, their control over what data are collected, the purpose of the collection, and the implications of any analysis. Questions arise as to the extent to which students are aware of the benefits and risks inherent in the exchange of their data, and whether they are willing to exchange personal data for more effective and supported learning experiences.

This study reports on the views of entry level students at the Open University (OU) in 2018. The primary aim is to explore differences between stated attitudes to privacy and their online behaviors, and whether these same attitudes extend to their university's uses of their (personal) data. The analysis indicates, inter alia, that there is no obvious relationship between how often students are online or their awareness of/concerns about privacy issues in online contexts and what they actually do to protect themselves. Significantly though, the findings indicate that students overwhelmingly have an inherent trust in their university to use their data appropriately and ethically.

Based on the findings, we outline a number of issues for consideration by higher education institutions, such as the need for transparency (of purpose and scope), the provision of some element of student control, and an acknowledgment of the exchange value of information in the nexus of the privacy calculus.

References

[1]

Abamu, J. 2017, Jun 13. From high school to Harvard, students urge for clarity on privacy rights. EdSurge. https://www.edsurge.com/news/2017-06-13-from-high-school-to-harvard-students-urge-for-clarity-on-privacy-rights?

[2]

Al-Dheleai, Y. M., & Tasir, Z. 2015. Facebook and education: students' privacy concerns. International Education Studies, 8(13), 22--26.

[3]

Bellman, S., Johnson, E.J. and Lohse, G.L. 2001. On site: to opt-in or opt-out?: it depends on the question. Comms of the ACM, 44, 2, (2001), 25--27.

Digital Library

[4]

Bitter, S., & Grabner-Kräuter, S. 2013. Customer engagement behavior: interacting with companies and brands on Facebook. In Advances in Advertising Research (Vol. IV) (pp. 3--17). Springer Fachmedien Wiesbaden.

[5]

Briggs, S. 2014, January 13. Big data in education: Big potential or big mistake? http://www.opencolleges.edu.au/informed/features/big-data-big-potential-or-big-mistake/

[6]

Buccafurri, F., Lax, G., Nicolazzo, S., & Nocera, A. 2015. Comparing Twitter and Facebook user behavior: privacy and other aspects. Computers in Human Behavior, 52, 87--95.

Digital Library

[7]

Chang, Y., Wong, S. F., & Lee, H. 2015. Understanding perceived privacy: A privacy boundary management model. https://pacis2015.comp.nus.edu.sg/_proceedings/PACIS_2015_submission_473.pdf

[8]

Cormack, A. 2016. A data protection framework for learning analytics.

[9]

Crawford, K., & Schultz, J. 2014. Big data and due process: Toward a framework to redress predictive privacy harms. BCL Rev., 55, 93--128.

[10]

Fodor, M., & Brem, A. 2015. Do privacy concerns matter for Millennials? Results from an empirical analysis of Location-Based Services adoption in Germany. Computers in Human Behavior, 53, 344--353.

Digital Library

[11]

GDPR. 2018. GDPR Key Changes. https://eugdpr.org/the-regulation/

[12]

Gump, J.E. 2001. The readability of typefaces and the subsequent mood or emotion created in the reader, Journal of Education for Business, 76, 5, (2001), 270--273.

[13]

Hauff, S., Veit, D., & Tuunainen, V. 2015. Towards a taxonomy of perceived consequences of privacy-invasive practices. In Proceedings of 23rd European Conference on Information Systems (ECIS), Münster, Germany, May 26-29, 2015.

[14]

Henman, P. 2004. Targeted!: Population segmentation, electronic surveillance and governing the unemployed in Australia. International Sociology, 19, 173--191.

[15]

Jutting, C. 2016, Aug 3. Universities are tracking their students. Is it clever or creepy? The Guardian. https://www.theguardian.com/higher-education-network/2016/aug/03/learning-analytics-universities-data-track-students

[16]

Kitchin, R. 2013. Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography, 3, 262--267.

[17]

Knijnenburg, B. P., & Kobsa, A. 2014. Increasing sharing tendency without reducing satisfaction: Finding the best privacy-settings user interface for social networks. http://aisel.aisnet.org/icis2014/proceedings/ISSecurity/4/

[18]

Kokolakis, S. 2015. Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & Security, 1--13.

Digital Library

[19]

Krasnova, H., Kolesnikova, E., & Guenther, O. 2009. "It won't happen to me!": Self-disclosure in online social networks. Amcis 2009 Proceedings, 343, 1--9.

[20]

Lagoze, C. 2014. Big Data, data integrity, and the fracturing of the control zone. Big Data & Society, 1(2), 1--11.

[21]

Lee, C. H., & Cranage, D. A. 2011. Personalisation--privacy paradox: The effects of personalisation and privacy assurance on customer responses to travel Web sites. Tourism Management, 32(5), 987--994.

[22]

Madden, M., & Rainie, L. 2015. Americans' attitudes about privacy, security and surveillance. http://www.pewinternet.org/files/2015/05/Privacy-and-Security-Attitudes-5.19.15_FINAL.pdf

[23]

Miltgen, C. L., & Smith, H. J. 2015. Exploring information privacy regulation, risks, trust, and behavior. Information & Management, 52(6), 741--759.

Digital Library

[24]

Nissenbaum, H. 2015. Respecting context to protect privacy: Why meaning matters. Science and Engineering Ethics, 1--22.

[25]

Qiu, J. L. 2015. Reflections on Big Data: 'Just because it is accessible does not make it ethical'. Media, Culture & Society, 37(7), 1089--1094.

[26]

Open University. 2014. Ethical use of student data for learning analytics policy. http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student-data-learning-analytics-policy

[27]

Payne, R. 2014. Frictionless sharing and digital promiscuity. Communication and Critical/Cultural Studies, 11(2), 85--102.

[28]

Prinsloo, P., & Slade, S. 2015. March. Student privacy self-management: implications for learning analytics. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 83--92). ACM.

Digital Library

[29]

Rainie, L., Kiesler, S., Kang, R., & Madden, M. 2013. Anonymity, privacy, and security online. http://www.pewinternet.org/2013/09/05/anonymity-privacy-and-security-online/

[30]

Raths, D. 2018, Feb 5. When learning analytics violate student privacy. CampusTechnology. https://campustechnology.com/articles/2018/05/02/when-learning-analytics-violate-student-privacy.aspx

[31]

Siemens, G. 2013. Learning analytics: The emergence of a discipline. American Behavioural Scientist, 57(10), pp. 1380--1400.

[32]

Slade, S., & Prinsloo, P. 2013. Learning analytics ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510--1529.

[33]

Slade, S., & Prinsloo, P. 2014. Student perspectives on the use of their data: between intrusion, surveillance and care. http://oro.open.ac.uk/41229/

[34]

Soczka, L., Brites, R., & Matos, P. (2015, July). Personal Information Disclosure and Perceptions About Data Usage by Facebook. In Proceedings of the 2nd European Conference on Social Media 2015: ECSM 2015 (p. 413). Academic Conferences Limited.

[35]

Solove, D.J. 2004. The digital person. Technology and privacy in the information age. New York, NY: New York University Press.

Digital Library

[36]

Solove, D. J. 2012. Introduction: Privacy self-management and the consent dilemma. Harv. L. Rev., 126, 1880--1902.

[37]

Solove, D.J. 2013. Introduction: Privacy self-management and the consent dilemma. Harvard Law Review 1880 (2013); GWU Legal Studies Research Paper No. 2012-141

[38]

Sourial, N., Wolfson, C., Zhu, B., Quail, J., Fletcher, J., Karunananthan, S. & Bergman, H. 2010. Correspondence analysis is a useful tool to uncover the relationships among categorical variables. Journal of clinical epidemiology, 63(6), 638--646

[39]

Thebault, R. 2015, August 2. Student data give colleges new tools. The Columbus Dispatch. http://www.dispatch.com/content/stories/local/2015/08/02/student-data-give-colleges-new-tools.html

[40]

Trepte, S., Teutsch, D., & Masur, P. 2014. Do people know about privacy and data protection strategies? Paper delivered at the 7th International Conference on Computers, Privacy and Data Protection, January 23rd, Brussels, Belgium.

[41]

Vitak, J., Shilton, K., & Ashktorab, Z. 2015. Beyond the Belmont principles: Ethical challenges, practices, and beliefs in the online data research community. Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing. New York: ACM

Digital Library

[42]

Westin, A. F. 2003. Social and political dimensions of privacy. Journal of social issues, 59(2), 431--453.

[43]

White, D. S., & Le Cornu, A. 2011. Visitors and Residents: A new typology for online engagement. First Monday, 16(9).

[44]

Wright, F., White, D., Hirst, T., & Cann, A. 2014. Visitors and Residents: mapping student attitudes to academic use of social networks. Learning, Media and Technology, 39(1), 126--141.

[45]

Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. 2011. The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42--52.

Digital Library

[46]

Young, E. 2015. Educational privacy in the online classroom: FERPA, MOOCs, and the big data conundrum. Harvard Journal Law & Technology, 28, 549--593.

Cited By

Assassi T(2025)ESP practitioners’ perspectives on the efficiency of using artificial intelligence ‘ChatGPT’ in needs analysisComputer Assisted Language Learning10.1080/09588221.2024.2428943(1-32)Online publication date: Jan-2025
https://doi.org/10.1080/09588221.2024.2428943
Chang MTissenbaum MPhilip TD’Mello S(2025)Co-designing AI with Youth Partners: Enabling Ideal Classroom Relationships through a Novel AI Relational Privacy Ethical FrameworkComputers and Education: Artificial Intelligence10.1016/j.caeai.2025.100364(100364)Online publication date: Jan-2025
https://doi.org/10.1016/j.caeai.2025.100364
Sharif HAtif A(2024)The Evolving Classroom: How Learning Analytics Is Shaping the Future of Education and Feedback MechanismsEducation Sciences10.3390/educsci1402017614:2(176)Online publication date: 8-Feb-2024
https://doi.org/10.3390/educsci14020176
Show More Cited By

Index Terms

Learning analytics at the intersections of student trust, disclosure and benefit
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections
2. Social and professional topics
  1. Computing / technology policy
    1. Privacy policies

Recommendations

Student privacy self-management: implications for learning analytics
LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge

Optimizing the harvesting and analysis of student data promises to clear the fog surrounding the key drivers of student success and retention, and provide potential for improved student success. At the same time, concerns are increasingly voiced around ...
Internet privacy concerns and beliefs about government surveillance - An empirical investigation

This U.S.-based research attempts to understand the relationships between users' perceptions about Internet privacy concerns, the need for government surveillance, government intrusion concerns, and the willingness to disclose personal information ...
Beyond self-disclosure

Massive amounts of information about others are disclosed across the world on social network sites every day. Disclosure of information about others may precipitate unanticipated consequences such as violating the privacy of another individual. Previous ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge

March 2019

565 pages

ISBN:9781450362566

DOI:10.1145/3303772

General Chairs:
Sharon Hsiao
Arizona State University, USA
,
Jim Cunningham
Arizona State University, USA
,
Katie McCarthy
Georgia State University, USA
,
Grace Lynch
Society for Learning Analytics Research, Australia
,
Program Chairs:
Christopher Brooks
University of Michigan, USA
,
Rebecca Ferguson
The Open University, UK
,
Ulrich Hoppe
University of Duisburg-Essen, Germany

Copyright © 2019 ACM.

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 ACM 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]

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SoLAR: The Society for Learning Analytics Research
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

LAK19

LAK19: The 9th International Learning Analytics & Knowledge Conference

March 4 - 8, 2019

AZ, Tempe, USA

Acceptance Rates

Overall Acceptance Rate 236 of 782 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

57
Total Citations
View Citations
808
Total Downloads

Downloads (Last 12 months)86
Downloads (Last 6 weeks)10

Reflects downloads up to 03 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Assassi T(2025)ESP practitioners’ perspectives on the efficiency of using artificial intelligence ‘ChatGPT’ in needs analysisComputer Assisted Language Learning10.1080/09588221.2024.2428943(1-32)Online publication date: Jan-2025
https://doi.org/10.1080/09588221.2024.2428943
Chang MTissenbaum MPhilip TD’Mello S(2025)Co-designing AI with Youth Partners: Enabling Ideal Classroom Relationships through a Novel AI Relational Privacy Ethical FrameworkComputers and Education: Artificial Intelligence10.1016/j.caeai.2025.100364(100364)Online publication date: Jan-2025
https://doi.org/10.1016/j.caeai.2025.100364
Sharif HAtif A(2024)The Evolving Classroom: How Learning Analytics Is Shaping the Future of Education and Feedback MechanismsEducation Sciences10.3390/educsci1402017614:2(176)Online publication date: 8-Feb-2024
https://doi.org/10.3390/educsci14020176
Acosta-Enriquez BArbulú Ballesteros MHuamaní Jordan OLópez Roca CSaavedra Tirado K(2024)Analysis of college students' attitudes toward the use of ChatGPT in their academic activities: effect of intent to use, verification of information and responsible useBMC Psychology10.1186/s40359-024-01764-z12:1Online publication date: 8-May-2024
https://doi.org/10.1186/s40359-024-01764-z
Alfredo REcheverria VJin YSwiecki ZGašević DMartinez-Maldonado R(2024)SLADE: A Method for Designing Human-Centred Learning Analytics SystemsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636847(24-34)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636847
Lünich MKeller B(2024)Explainable Artificial Intelligence for Academic Performance Prediction. An Experimental Study on the Impact of Accuracy and Simplicity of Decision Trees on Causability and Fairness PerceptionsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658953(1031-1042)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658953
Radway SQuintanilla KLudden CVotipka D(2024)An Investigation of US Universities' Implementation of FERPA Student Directory Policies and Student Privacy PreferencesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642066(1-35)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642066
Yan LEcheverria VJin YFernandez‐Nieto GZhao LLi XAlfredo RSwiecki ZGašević DMartinez‐Maldonado R(2024)Evidence‐based multimodal learning analytics for feedback and reflection in collaborative learningBritish Journal of Educational Technology10.1111/bjet.1349855:5(1900-1925)Online publication date: 22-Jun-2024
https://doi.org/10.1111/bjet.13498
Viberg OKizilcec RJivet IMartínez Monés AOh AMutimukwe CHrastinski SScheffel M(2024)Cultural Differences in Students’ Privacy Concerns in Learning Analytics across Germany, South Korea, Spain, Sweden, and the United StatesComputers in Human Behavior Reports10.1016/j.chbr.2024.100416(100416)Online publication date: Apr-2024
https://doi.org/10.1016/j.chbr.2024.100416
Lünich MKeller BMarcinkowski F(2024)Diverging Perceptions of Artificial Intelligence in Higher Education: A Comparison of Student and Public Assessments on Risks and Damages of Academic Performance Prediction in GermanyComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100305(100305)Online publication date: Sep-2024
https://doi.org/10.1016/j.caeai.2024.100305
Show More Cited By

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten