The Datafication of Student Life in Higher Education: Privacy Problems and Paths Forward

Closes:

About the Journal 

Information and Learning Sciences (ILS) is a peer-reviewed journal that publishes interdisciplinary research within information science and the learning sciences/education sciences. For additional journal information, see the journal’s website. As of June, 2022, ILS’s Scopus CiteScore was 4.8. It had an H-Index of 32 according to Scimago and ranked 43 (of 249 journals, Q1) in the Library and Information Sciences category and ranked 344 (of 1381 journals, Q2) in the Education category. Gold and green open access options are available to authors. 

Guest Editor

Dr. Kyle M. L. Jones 
Associate Professor 
School of Informatics and Computing 
Indiana University-Indianapolis (IUPUI) 

[email protected]
https://thecorkboard.org

Overview of the Special Issue’s Theme 

Higher education institutions continue to datafy student life in all its forms: academic, social, personal, health, etc. Some of these actions are intentional. Universities build data infrastructures to strategically capture student behaviors, communications, and profiles to better serve their educational interests. Other forms of student datafication arise as a consequence of the ubiquity of information and communication technologies on campuses and how they create revelatory and analyzable data trails, which can directly or indirectly identify students. Learning sciences researchers recognize the potential to inform teaching strategies and improve learning outcomes by studying student data trails. Higher education administrators and institutional researchers see benefits, too; for them, acting on data can improve higher education’s effectiveness and create new efficiencies. But as the guest editor wrote in a Future of Privacy Forum special report, “these opportunities bring significant, undeniable social, political, ethical, and legal problems that education stakeholders should neither discount nor ignore. Chief among these problems is student data privacy, from which one could argue that most of these other social, political, ethical, and legal issues stem” (Jones, 2022, p. 3). 

This special issue focuses on student and learner privacy as a central problem in the fields of educational data mining (EDM), learning analytics (LA), and information science (IS) research and practice, with an emphasis on laying out paths forward to constructively address privacy. Since around 2010, the early years of LA, researchers and practitioners alike have published a significant body of work 1) identifying privacy as a complicated issue (Rubel & Jones, 2016) and 2) cataloging its related ethical facets (e.g., autonomy, trust, intellectual freedom) (Pardo & Siemens, 2014; Slade & Prinsloo, 2013). This critical, ethics-forward work has been valuable in that it has enhanced sensitivities around privacy and demonstrated the role of privacy in educational practices.  

However, only more recently have publications attempted to address how to build and implement educational technologies and data practices with privacy and consent as central features (Cormack, 2016; Jones, 2019; Li et al., 2021). Other research has begun to treat faculty and students as key stakeholders in the design of LA tools (Jones et al., 2020; Klein et al., 2019; Mahmoud et al., 2020; Paris et al, 2022; Sun et al., 2019; West et al., 2020), and as co-designers alongside programmers and data scientists (Alvarez et al., 2020; Buckingham Shum et al., 2019; Michos et al., 2020; Sarmiento & Wise, 2022). Literature in this body of research has demonstrated how to address privacy issues in a practical way. This special issue aims to further enhance these pragmatic facets of the student privacy research agenda, in the higher education context. 

Types of Contributions

For this special issue, the guest editor welcomes the following types of contributions that address the theme for the special issue: 

Theoretical 

Unlike other scholarly areas (e.g., surveillance capitalism, personal privacy, etc.), student privacy is underdeveloped as a theoretical concept. Often, research discusses sociotechnical concerns that raise student privacy problems, but this type of research does not centrally focus on the meaning and value of student privacy. Theoretical contributions would help to fill this particular gap in the literature. 

Empirical 

Qualitative, quantitative, and mixed methods studies of all types that are focused on student privacy are welcome and encouraged. There is a growing body of literature in this area, but there are ample opportunities to develop more substantive research that address particular stakeholder behaviors, needs, wants, perspectives, and expectations vis-à-vis privacy. 

Policy 

Studies of extant policies and related practices would help researchers and practitioners alike recognize the weaknesses, strengths, and consequences of particular policy designs. The aim of these types of studies should be to help institutional actors develop useful, justifiable policies for their own institutions. Policy studies could be conducted, for instance, at the micro level (e.g., a campus unit/department), meso level (e.g., across a campus), or macro level (e.g., for a state university system, university consortium). 

Critical 

Studies using a critical data or algorithm studies framework are also welcome. These approaches focus readers on specific artifacts of EDM, LA, and institution data infrastructures to demonstrate the consequences of their implementations. Critical studies should provide actionable solutions that hold promise to resolve the issues raised using these types of frameworks. 

Commentaries 

Commentaries, or opinion pieces, are welcome if they clearly and directly address a particular issue or raise a viewpoint associated with the special issue’s theme. For example, commentaries may provide constructive criticism about a perspective on student privacy or focus on a contentious point. Other commentaries could bring new information to readers about funding opportunities, potentially useful collaborations, and describe potential research agendas or professional development pathways related to the theme. Commentaries should be supported with useful literature. All commentaries will undergo a review by the guest editor, who may also submit the piece for formal peer review. 

Note: Excluding reference lists, commentaries should not exceed 2,000 words and all other contributions should be between 6,000 and 8,000 words. If submitting a qualitative study, for example with extensive evidentiary transcription, you may request an expanded word count limit from the guest editor (max 9,500 words). 

Important Dates 

Item 

Date 

Submission Due 

March 31, 2023 

First Reviews Completed 

May 30, 2023 

First Revisions Due (as needed) 

July 30, 2023 

Second Reviews Completed (as needed) 

August 31, 2023 

Second Revisions Due (as needed) 

September 30, 2023 

Publication of Issue 

January, 2024 

 

Submission Guidelines 

Submissions should comply with the journal author guidelines and be made through ScholarOne Manuscripts, the online submission and peer review system. The system will prompt you with a pulldown menu; please select the option, “Datafication Privacy Paths.” 

Questions 

The guest editor welcomes any and all questions about potential contributions and is willing to meet one-on-one (via Zoom) to discuss; contact him at [email protected]. Further, the guest editor welcomes the opportunity to match contributors—especially doctoral students and junior scholars—with mentors should they desire.  

References 

Alvarez, C. P., Martinez-Maldonado, R., & Buckingham Shum, S. (2020). LA-DECK: A card-based learning analytics co-design tool. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 63–72. https://doi.org/10.1145/3375462.3375476 

Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9. https://learning-analytics.info/journals/index.php/JLA/issue/view/463 

Cormack, A. N. (2016). A data protection framework for learning analytics. Journal of Learning Analytics, 3(1), 91–106. https://doi.org/10.18608/jla.2016.31.6 

Jones, K. M. L. (2019). Learning analytics and higher education: A proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16(1), 24. https://doi.org/10.1186/s41239-019-0155-0 

Jones, K. M. L. (2022). The datafied student: Why students’ data privacy matters and the responsibility to protect it. Future of Privacy Forum. https://studentprivacycompass.org/resource/the-datafied-student-why-stu… 

Jones, K. M. L., Asher, A., Goben, A., Perry, M. R., Salo, D., Briney, K. A., & Robertshaw, M. B. (2020). “We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology, 71(9), 1044–1059. https://doi.org/10.1002/asi.24358 

Klein, C., Lester, J., Rangwala, H., & Johri, A. (2019). Technological barriers and incentives to learning analytics adoption in higher education: Insights from users. Journal of Computing in Higher Education, 31(3), 604–625. https://doi.org/10.1007/s12528-019-09210-5 

Li, W., Sun, K., Schaub, F., & Brooks, C. (2021). Disparities in students’ propensity to consent to learning analytics. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-021-00254-2 

Mahmoud, M., Dafoulas, G., Abd ElAziz, R., & Saleeb, N. (2020). Learning analytics stakeholders’ expectations in higher education institutions: A literature review. The International Journal of Information and Learning Technology, 38(1), 33–48. https://doi.org/10.1108/IJILT-05-2020-0081 

Michos, K., Lang, C., Hernández-Leo, D., & Price-Dennis, D. (2020). Involving teachers in learning analytics design: Lessons learned from two case studies. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 94–99. https://doi.org/10.1145/3375462.3375507 

Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. https://doi.org/10.1111/bjet.12152 

Paris, B., Reynolds, R., & McGowan, C. (2022). Sins of omission: Critical informatics perspectives on privacy in e-learning systems in higher education. Journal of the Association for Information Science and Technology, 73( 5), 708– 725.  

Rubel, A., & Jones, K. M. L. (2016). Student privacy in learning analytics: An information ethics perspective. The Information Society, 32(2), 143–159. https://doi.org/10.1080/01972243.2016.1130502 

Sarmiento, J. P., & Wise, A. F. (2022). Participatory and co-design of learning analytics: An initial review of the literature. LAK22: 12th International Learning Analytics and Knowledge Conference, 535–541. https://doi.org/10.1145/3506860.3506910 

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366 

Sun, K., Mhaidli, A. H., Watel, S., Brooks, C. A., & Schaub, F. (2019). It’s My Data! Tensions Among Stakeholders of a Learning Analytics Dashboard. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3290605.3300824 

West, D., Luzeckyj, A., Toohey, D., Vanderlelie, J., & Searle, B. (2020). Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics. Australasian Journal of Educational Technology, 36(2), 60–70. https://doi.org/10.14742/ajet.4653