%0 Conference Paper %1 2b7baba2ca054597a4e4ba88f2230bf0 %A Marx, Erik %A Witt, Clemens %A Leonhardt, Thiemo %B WiPSCE '24: Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research %D 2024 %I Association for Computing Machinery %K area_responsibleai FIS_scads artificial conceptions intelligence, interview learning, machine mental misconceptions, models, qualitative research, students study, %P 1--10 %R 10.1145/3677619.3678114 %T Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study %X Since students are familiar with machine learning (ML)-based applications in their everyday lives, they already construct mental models of how these systems work. This can result in misconceptions that influence the learning of correct ML concepts. Therefore, this study investigates the misconceptions students hold about the functionality of ML-based applications. To this end, we conducted semi-structured interviews with five students, focusing on their understanding of facial recognition and ChatGPT. The interviews were analyzed using an inductively developed code system and qualitative content analysis. This process identified six key misconceptions held by students: “Programmed Behavior,” “Exactness,” “Data Storage,” “Continuous Learning,” “User-trained Model,” and “Autonomous Data Acquisition”. These misconceptions include the notion that AI learns continuously during application, or that training data is saved and reused later. This paper presents the identified misconceptions and discusses their implication for the design and evaluation of effective learning activities in the context of ML.

@inproceedings{2b7baba2ca054597a4e4ba88f2230bf0, abstract = {Since students are familiar with machine learning (ML)-based applications in their everyday lives, they already construct mental models of how these systems work. This can result in misconceptions that influence the learning of correct ML concepts. Therefore, this study investigates the misconceptions students hold about the functionality of ML-based applications. To this end, we conducted semi-structured interviews with five students, focusing on their understanding of facial recognition and ChatGPT. The interviews were analyzed using an inductively developed code system and qualitative content analysis. This process identified six key misconceptions held by students: “Programmed Behavior,” “Exactness,” “Data Storage,” “Continuous Learning,” “User-trained Model,” and “Autonomous Data Acquisition”. These misconceptions include the notion that AI learns continuously during application, or that training data is saved and reused later. This paper presents the identified misconceptions and discusses their implication for the design and evaluation of effective learning activities in the context of ML.}, added-at = {2024-11-28T16:27:18.000+0100}, author = {Marx, Erik and Witt, Clemens and Leonhardt, Thiemo}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/25eb568a11033d469a312a8535d8efdab/scadsfct}, booktitle = {WiPSCE '24: Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research}, day = 16, doi = {10.1145/3677619.3678114}, interhash = {736697c5bba11dfc316dfe067c296eda}, intrahash = {5eb568a11033d469a312a8535d8efdab}, keywords = {area_responsibleai FIS_scads artificial conceptions intelligence, interview learning, machine mental misconceptions, models, qualitative research, students study,}, language = {English}, month = sep, note = {Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).}, pages = {1--10}, publisher = {Association for Computing Machinery}, timestamp = {2024-11-28T17:40:55.000+0100}, title = {Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study}, year = 2024 }