Authors:
Linda Fernsel
;
Yannick Kalff
and
Katharina Simbeck
Affiliation:
Computer Science and Society, HTW Berlin University of Applied Sciences, Treskowallee 8, 10318 Berlin, Germany
Keyword(s):
Auditability, Artificial Intelligence, Learning Analytics, Moodle, Plugin Development.
Abstract:
The paper presents the work-in-progress development of a Moodle plugin to improve the auditability of Moodle’s Learning Analytics component. Future legislation, such as the EU AI Act, will require audits and “conformity assessments” of “high-risk” AI systems. Educational applications can be considered high-risk systems due to their important role in individual life and career paths. Therefore, their correctness, fairness, and efficiency must be assessed. However, auditing of the Learning Analytics functions in Moodle is limited. No suitable test-data is available, models and configurations are not persistent and only aggregated quality metrics are returned that are insufficient to assess fairness. The plugin addresses these issues and provides a data interface to extract data for audits. The plugin allows to a) upload and select data for the audit, b) clearly differentiate between model configuration and trained models, c) keep trained models, their configuration and underlying data
for future inspections and comparisons, and finally, d) the plugin saves raw predictions for further analysis. The plugin enables the audit of Moodle’s Learning Analytics and its underlying AI models and contributes to increased fairness and trustworthiness of Learning Analytics as well as its legally compliant application.
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