<div class="csl-bib-body">
<div class="csl-entry">Ferraioli, V., & Lehner, L. (2026). Should we open the Black Box? Investigating Transparency in K-12 Machine Learning Education. In N. Bergner, T. Michaeli, & A. Brodnik (Eds.), <i>WiPSCE ’26 : Proceedings of the 20th WiPSCE Conference on Primary and Secondary Computing Education Research</i>. The Association for Computing Machinery (ACM). https://doi.org/10.1145/3801749.3801788</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/230495
-
dc.description.abstract
The “black box” nature of machine learning (ML) poses a significant pedagogical challenge. To address this, we present a study investigating the impact of “glass-boxing” on students’ understanding of ML concepts. The design uses an unplugged intervention where pipeline transparency is manipulated across three conditions covering data preparation, modelling, and evaluation. This is implemented using variations of the unplugged learning materials. We report on lessons learned from a pilot implementation (N = 66) of the study design and our planned changes.
en
dc.language.iso
en
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
AI literacy
en
dc.subject
machine learning
en
dc.subject
K-12
en
dc.subject
unplugged activities
en
dc.title
Should we open the Black Box? Investigating Transparency in K-12 Machine Learning Education
