Scholarly and Professional Sources
-
European Commission (2018). General Data Protection Regulation (GDPR) – key legal framework for data protection in the EU.
-
OECD (2021). AI and the Future of Skills, Education and Learning – includes discussions on the ethical implications of using AI in education.
-
JISC (2020). Code of Practice for Learning Analytics – clear guidelines for the ethical use of data in education.
Institutional Policies and Examples of Good Practice
-
University of Edinburgh. Assessment and Feedback Principles – an example of institutional guidelines for fair and transparent assessment.
-
EDUCAUSE (2022). Privacy and Student Data: Principles for Responsible Use.
-
SURF (Netherlands). Trust and Ethics in Learning Analytics – with an emphasis on institutional policies and privacy protection.
-
Codes of Ethics of the University of Zagreb, the University of Rijeka, and other Croatian universities and higher education institutions: they cover fundamental principles, ethical rules for teaching and research/creative work, and the procedures of ethics bodies.
Digital Tools Useful for the Workshop
-
Hypothes.is – for collaborative annotation of sources and commenting on guidelines.
-
Padlet or Miro – for collaborative mapping of challenges and policy proposals.
-
Overleaf (or Google Docs) – for collaborative writing of policy proposals in groups.
-
Moodle “Workshop” activity – for submitting proposals and peer assessment.
Recommendation for Participants
When developing your policy proposal, rely on:
-
Principles – fairness, transparency, responsibility.
-
Privacy protection measures – encryption, data minimisation, students’ right of access to their data.
-
Tools and practices – which technologies to use responsibly (e.g., Moodle Analytics compared with commercial AI platforms).
-
Role of teachers – supporting students in understanding how their data is used.
Background Colour
Font Face
Font Size
Text Colour
Font Kerning
Image Visibility
Letter Spacing
Line Height
Link Highlight