Ga direct naar de inhoud

AVG Limits on Learning Analytics in Public Universities

Algemene Verordening Gegevensbescherming compliance governs the collection, processing, and algorithmic evaluation of educational records across public higher education institutions. Balancing institutional predictive modeling with statutory privacy safeguards requires rigorous data governance frameworks that prevent unlawful profiling and excessive surveillance. Institutional deployment of learning analytics must reconcile student data agency with legitimate educational intervention mandates.

Voorvertoning document

Dit is een beknopte voorvertoning. De volledige versie bevat uitgebreide tekst voor alle secties, een conclusie en een geformatteerde bibliografie.

Bachelor's Thesis

Degree:
AVG Limits on Learning Analytics in Public Universities

Author:

Group

First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

Abstract
Introduction
Chapter 1: Legal and Conceptual Foundations of Educational Analytics
1.1 Evolution of Learning Analytics and Educational Data Mining
1.2 Core Principles of the AVG in Higher Education Governance
1.3 Lawful Bases and Data Minimization in Academic Contexts
Chapter 2: Statutory Limits and Analytical Challenges in Public Universities
2.1 Comparative Analysis of Regulatory Restraints on Data Pipelines
2.2 AVG Compliance Tensions in Predictive Educational Modeling
2.3 Profiling, Automated Decisions, and Student Data Rights
Chapter 3: Institutional Governance Frameworks and Compliance Strategies
3.1 Data Protection by Design in University Machine Learning Architectures
3.2 Policy Guidelines for Ethical and Lawful Academic Interventions
3.3 Strategic Assessment of Institutional Implementation Safeguards
Discussion
Conclusion
Bibliography

Introduction

Institutional deployment of learning analytics and educational data mining has expanded rapidly across higher education systems seeking to enhance student retention, academic progression, and pedagogical personalization [1]. Digital learning environments continuously generate extensive behavioral and administrative records, creating substantial technical capabilities for automated predictive interventions and operational management across academic faculties [2].

However, the statutory boundaries established by the Algemene Verordening Gegevensbescherming impose strict regulatory limits on student profiling, data minimization, and automated decision-making within public universities [2]. Educational institutions face persistent challenges in reconciling complex machine learning pipelines with mandatory legal requirements concerning purpose limitation, transparency, and the legitimacy of processing legal bases in institutional learning environments [7].

The central objective of this research is to examine how statutory AVG provisions constrain learning analytics architectures in public universities and to define structured data governance protocols [2]. Through a systematic examination of data protection standards and educational analytics pipelines, this study establishes governance criteria that preserve statutory student privacy rights while supporting lawful educational monitoring [7].

2.2 AVG Compliance Tensions in Predictive Educational Modeling

The analytical deployment of educational data mining and learning analytics within public higher education institutions operates at the vital intersection of technological utility and strict regulatory restraint. When universities aggregate student records, course grades, and online interaction logs to identify behavioral patterns and generate predictive models, institutional practices must directly align with statutory data protection mandates. As higher education shifts toward complex computational processing environments, fundamental considerations regarding ethics and data privacy become decisive parameters for lawful system design (crossref-10-4018-978-1-7998-7103-3-ch005). The Algemene Verordening Gegevensbescherming imposes rigorous legal limits on the automated processing of personal information, requiring public universities to justify every single stage of data ingestion and algorithmic profiling through transparent legal bases. Furthermore, integrating end-to-end data and machine learning pipelines that span from raw information collection to interactive reporting dashboards intensifies statutory compliance obligations across academic departments (crossref-10-12681-eadd-53168). While structured machine learning pipelines streamline early predictive interventions and automated model execution, the continuous tracking of learner behavior rigorously tests the core AVG principles of purpose limitation and data minimization. Automated extraction of latent behavioral traits from digital learning platforms risks excessive data accumulation whenever institutional governance fails to establish clear instructional boundaries. Consequently, public universities cannot treat learning analytics as unconstrained algorithmic optimization. Instead, the statutory framework demands that public educational institutions systematically evaluate stakeholder impacts, restrict processing operations strictly to verified educational necessities, and embed robust technical safeguards into every operational data pipeline.

References

  1. Learning Analytics and Educational Data Mining: Transforming Student Success in Higher Education
    Author Contributor
    DOI-link
  2. Learning Analytics and Education Data Mining in Higher Education
    Samira ElAtia, Donald Ipperciel
    DOI-link
  3. Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education
    Santosh Ray, Mohammed Saeed
    DOI-link
  4. Educational Data Mining and Learning Analytics in Higher Education
    Padma Mishra, Vaishali B, Sangvikar -
  5. Educational Assessment, Educational Data Mining, and Learning Analytics
    Vanda Luengo
  6. Evolution and Facets of Data Analytics for Educational Data Mining and Learning Analytics
    Venkat N. Gudivada, Dhana L. Rao, Junhua Ding
  7. A holistic approach to learning analytics and educational data mining in distance learning through data and machine learning pipelines
    Ροδάνθη Τσώνη

Bibliografie

Geverifieerde BronnenOpmaakstandaardenHoge UniekheidPro Modellen
Lanceringsaanbieding -25%

Afstudeerscriptie

APA 7th Edition (Publication Manual)

€ 17€ 22
  • 60-80 pagina's
  • Hoge originaliteit
  • Exporteren naar Word
  • Correcte opmaak
  • Openbare preview
    Een preview van een andere auteur kan niet privé worden gemaakt. Je werk zal privé en volledig uniek zijn.
  • Bibliografie (20+, APA 7th Edition)
    +€ 1
  • Alternatieve bronnen toevoegen (Nieuws, .gov, .edu)

Afstudeerscriptie

APA 7th Edition (Publication Manual)