Understanding privacy and data protection issues in learning analytics using a systematic review

Abstract

The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross‐regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence‐based, thereby increasing the trustworthiness of learning analytics and its usefulness. Practitioner notesWhat is already known about this topic Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics. Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions. What this study contributes Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle. Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views. Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics. Implications for practice and/or policy Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics. Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders. Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.

Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics

“…LA has come a long way since its inception in 2011. However, privacy concerns have also been on a steady rise and have been identified as a major obstacle to the further development of the field [15,32]. Privacy issues differ at distinct stages of a LA project.…”

“…Privacy issues differ at distinct stages of a LA project. In the phases of data analytics and data publication and sharing, insufficient anonymization, sensitive data storage, and calculation are urgent privacy issues [32]. Current de-identification and anonymization techniques may not provide the necessary privacy protection.…”

“…Privacy in a congruent way, has also become a significant barrier to scaling the field [15]. In their recent review, [32] outlined eight distinct privacy and data protection issues in phases of the LA process. Concerns about privacy issues continue to escalate with the increasing volume of data, the diversity of data patterns, the regular updates of data protection laws, and the popularity of generative artificial intelligence [23].…”

“…In contrast, current approaches to addressing privacy issues in LA have some important limitations. For example, legal-and framework-based solutions often lack practical evidence and clear and actionable guidelines [32,36].…”

“…Such a lack of proper anonymization creates opportunities for data misuse. Regulations of the local storage and processing of sensitive data, such as the General Data Protection and Regulation act (GDPR) [29] are also difficult to fully address with current privacy solutions in LA [32]. In this context, synthetic data holds significant promise for mitigating privacy concerns in the field of LA and for expanding analytics capabilities without exposing individual details [5].…”