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GDPR Limits on Learning Analytics in Public HE

Automated processing of student telemetry and educational records in public universities operates under the stringent regulatory constraints of European privacy law. Regulatory mechanisms such as purpose limitation, mandatory impact assessments, and restrictions on algorithmic profiling restrict pervasive tracking infrastructures. Reconciling digital pedagogy with fundamental rights requires institutional governance models rooted in Privacy by Design and robust data subject protections.

Objekt og emne

Learning analytics deployment in public higher education — GDPR statutory limits and compliance mechanisms governing student data processing

Dokument Forhåndsvisning

Dette er en kort forhåndsvisning. Den fulde version indeholder udvidet tekst til alle sektioner, en konklusion og en formateret bibliografi.

Bachelor's Project

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GDPR Limits on Learning Analytics in Public HE

Author:

Group

First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

Resumé
Abstract
Indledning
Problemformulering
1. Regulatory Architecture of Data Protection in European Higher Education
1.1 Core Principles of the GDPR and Extraterritorial Reach
1.2 Lawful Bases for Processing Student Data: Public Task versus Consent
1.3 Purpose Limitation and Data Minimisation in Institutional Analytics
2. Analytical Assessment of Learning Analytics Implementation Barriers
2.1 Data Subject Rights and Algorithmic Transparency in Educational Profiling
2.2 Data Protection Impact Assessments and Privacy by Design Requirements
2.3 National Discretion and Supervisory Authority Divergence across Member States
3. Governance Frameworks for Compliant Analytics in Public Universities
3.1 Institutional Data Protection Officer Roles and Compliance Oversight
3.2 Third-Party EdTech Vendors, Standard Contractual Clauses, and Data Transfers
3.3 Strategic Models for Legally Sustainable Learning Analytics
Discussion
Litteraturliste
Konklusion
Bibliography

Introduction

The deployment of algorithmic monitoring and predictive modelling across higher education institutions operates at the intersection of pedagogical innovation and strict European data privacy standards. Under the General Data Protection Regulation, public universities must reconcile systemic student tracking with foundational principles of lawfulness, fairness, and transparency [1]. This regulatory landscape establishes rigid boundaries for institutional data processing, mandating comprehensive accountability structures for automated learning analytics platforms [2].

Operational tensions emerge primarily from the friction between extensive digital surveillance required for predictive interventions and statutory constraints regarding purpose limitation and data minimisation. While institutions seek to enhance academic retention through continuous behavioural profiling, data protection jurisprudence strictly curtails automated categorisation and disproportionate data accumulation [1]. Furthermore, structural power asymmetries between enrolled students and public institutions undermine the validity of freely given consent as a lawful basis [3].

This investigation examines the normative limitations imposed by European privacy law on algorithmic learning analytics within public universities. By conducting a systematic legal-doctrinal and comparative governance analysis, this inquiry identifies statutory barriers and evaluates mechanisms such as Data Protection Impact Assessments and Privacy by Design [2]. The resulting insights establish rigorous operational criteria for balancing pedagogical intelligence with fundamental rights to personal data protection.

2.1 Data Subject Rights and Algorithmic Transparency in Educational Profiling

The deployment of automated learning analytics within public higher education institutions creates critical tensions between institutional monitoring and European privacy mandates. As the General Data Protection Regulation establishes comprehensive protections across the European Union, its regulatory framework strictly governs any processing of student data, regardless of where analytical platforms or processing entities reside (The General Data Protection Regulation (GDPR): A Landmark in Privacy Law, 2025). When applied to educational telemetry, predictive models, and behavioral evaluation, universities operate as data controllers bound by structural duties that directly constrain pervasive surveillance mechanisms. In public higher education, analytical profiling frequently impinges upon student autonomy and networked privacy, demanding heightened scrutiny of the lawful bases utilized for automated tracking (General Data Protection Regulation (GDPR), 2021). Although institutions seek actionable metrics to enhance academic retention, data processing mechanisms must align with core principles such as transparency, purpose limitation, and data subject rights. The asymmetric relationship between students and university administrations mirrors structural workplace imbalances, wherein genuine, uncoerced consent remains difficult to obtain, thereby compelling institutions to rely on public task justifications while upholding strict data minimization (General Data Protection Regulation (GDPR), 2021). Furthermore, the broader enforcement landscape demonstrates that institutional controllers must systematically implement foundational safeguards, including Data Protection Impact Assessments and Privacy by Design, to mitigate regulatory exposure and protect fundamental rights (The EU General Data Protection Regulation (GDPR): Five Years After and the Future of Data Privacy Protection in Review, 2023). Consequently, lawful educational analytics necessitates embedding algorithmic transparency directly into computational design.

References

  1. The General Data Protection Regulation (GDPR): A Landmark in Privacy Law
    Stella Macrin
    DOI-link
  2. The EU General Data Protection Regulation (GDPR): Five Years After and the Future of Data Privacy Protection in Review
    Alexander Wodi
    DOI-link
  3. General Data Protection Regulation (GDPR)
    Ana Isabel Guerra, Maria João Machado, Maria Malta Fernandes et al.
    DOI-link
  4. General Data Protection Regulation (GDPR) ambiguity, national diversity and data protection officer certification: Implementing Art. 39(1) GDPR in France, Italy, Luxembourg and Spain
    Jacob Kornbeck
  5. A Proposal for Multiple Instance Learning Framework Application to Protect Data Access Rights under General Data Protection Regulation (GDPR)
    Amie Taal, Odunayo Fadahunsi
  6. General Data Protection Regulation (GDPR)
    Sergio Barezzani

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