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NDSG Limits on Learning Analytics in Public HE

Data governance in tertiary education requires balancing advanced algorithmic student tracking with statutory privacy protections. The revised Swiss Federal Act on Data Protection establishes strict boundaries regarding proportionality, profiling transparency, and purpose limitation in university operations. Integrating institutional analytics architectures requires robust organizational oversight, mandatory impact assessments, and technical privacy safeguards.

Objekt und Gegenstand

Data processing and algorithmic modeling systems in public tertiary education institutions. — Statutory and architectural limits imposed by the revised Federal Act on Data Protection on student tracking systems.

Wissenschaftliche Neuheit

Systematic mapping of revised Swiss data protection provisions onto multi-tier institutional learning analytics architectures.

Dokumentenvorschau

Dies ist eine kurze Vorschau. Die Vollversion enthält erweiterten Text für alle Abschnitte, ein Fazit und ein formatiertes Literaturverzeichnis.

Bachelor's Thesis

Degree:
NDSG Limits on Learning Analytics in Public HE

Author:

Group

First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

Abstract
Introduction
1 Theoretical and Legal Foundations of Educational Data Processing
1.1 Conceptual Framework of Learning Analytics in Higher Education
1.2 Core Principles of the Revised Swiss Federal Act on Data Protection
1.3 Profiling and Automated Decision-Making under Statutory Constraints
2 Analytical Assessment of nDSG Compliance in University Data Pipelines
2.1 Telemetry, Behavioral Metrics, and Sensitive Student Data Processing
2.2 Legal Bases: Statutory Mandate versus Explicit Consent in Public Cantonal Universities
2.3 Institutional Governance Gaps and Compliance Deficits in Higher Learning
3 Technical and Organisational Measures for Compliant Analytics Architectures
3.1 Data Protection Impact Assessments for Predictive Student Modeling
3.2 Collaborative Architecture, Anonymisation, and Access Governance
3.3 Institutional Policy Frameworks and Oversight Mechanisms
Discussion
Eigenständigkeitserklärung
Conclusion
Bibliography

Introduction

The systematic collection and computational analysis of student data have become central to modern educational optimization in tertiary institutions. Higher education institutions increasingly rely on big data frameworks to track academic engagement, evaluate pedagogical outcomes, and design individualized intervention pathways [3], [5]. However, the integration of behavioral tracking tools directly intersects with fundamental rights concerning informational self-determination and algorithmic transparency [4].

In Switzerland, the revised Federal Act on Data Protection establishes rigorous thresholds for processing personal data, demanding strict adherence to purpose limitation, proportionality, and privacy by design [1]. Public higher education institutions operate under statutory public mandates while handling vast volumes of administrative, demographic, and behavioral telemetry [6]. When learning analytics architectures generate predictive models or automated risk profiles, they risk conflicting with statutory requirements governing high-risk profiling and transparent consent mechanisms [1], [4].

Comparative institutional evidence demonstrates that educational organizations frequently experience compliance deficits due to fragmented governance structures, insufficient policy formalization, and the absence of designated oversight officers [2]. In Swiss public universities, where cantonal public law interacts with federal data protection benchmarks, establishing clear legal bases for learning analytics remains a complex regulatory challenge. These tensions necessitate a rigorous examination of the boundaries imposed by statutory data privacy norms on institutional analytics practices [1], [2].

This study examines the statutory boundaries imposed by data protection legislation on the deployment of learning analytics in Swiss public universities. By conducting a comparative legal and technical analysis of data governance pipelines [6], the research delineates permissible boundaries of student telemetry and formulates technical and organizational mechanisms for compliant institutional deployment [1], [2].

2.3 Institutional Governance Gaps and Compliance Deficits in Higher Learning

Applying statutory privacy principles to institutional learning analytics highlights persistent governance deficits within university data architectures. Under the revised legal framework governing Swiss data protection, higher learning institutions must align automated student monitoring with updated accountability standards and clear regulatory benchmarks (Revision of Federal Data Protection Act (FDPA), 2020). However, educational data processing systems frequently operate without the necessary institutional safeguards and organizational structures. An analytical examination of university data governance demonstrates that tertiary institutions often exhibit severe compliance gaps due to an absence of designated data protection officers, missing institutional policies, and inadequate staff training regarding lawful personal data management (Are Universities Compliant? A Study of Tanzania’s Personal Data Protection Act in Higher Learning Institutions, 2025). When universities develop expansive analytics datasets that integrate administrative records, student demographics, and learning metrics for institutional research, establishing robust governance procedures becomes indispensable for maintaining data security and procedural legitimacy (Rearchitecting Data for Researchers: A Collaborative Model for Enabling Institutional Learning Analytics in Higher Education, 2019). Without structured oversight mechanisms, automated student tracking platforms risk exceeding their educational mandate and breaching statutory constraints. Public higher education institutions must therefore bridge the divide between theoretical compliance mandates and day-to-day administrative practices by formalizing access protocols, documenting analytics procedures, and ensuring continuous administrative oversight. Resolving these operational deficits enables universities to deploy analytical systems in strict conformity with statutory privacy rights.

References

  1. Switzerland: Revision of Federal Data Protection Act (FDPA)
    Nicole Beranek Zanon
    DOI-Link
  2. Are Universities Compliant? A Study of Tanzania’s Personal Data Protection Act in Higher Learning Institutions
    Doreen F. Mwamlangala
    DOI-Link
  3. Overview of Big Data and Analytics in Higher Education
    Ben Kei Daniel
    DOI-Link
  4. Big Data, Higher Education and Learning Analytics: Beyond Justice, Towards an Ethics of Care
    Paul Prinsloo, Sharon Slade
  5. Big Data in Higher Education: The Big Picture
    Ben K. Daniel
  6. Rearchitecting Data for Researchers: A Collaborative Model for Enabling Institutional Learning Analytics in Higher Education
    Steven Lonn, Benjamin Koester

Bibliographie

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Diplomarbeit

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NDSG Limits on Learning Analytics in Public HE | Diplomarbeit | Aicademy