Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

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.

Details

Original languageEnglish
Title of host publicationWiPSCE '24: Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research
EditorsTilman Michaeli, Sue Sentance, Nadine Bergner
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Pages1-10
Number of pages10
ISBN (electronic)9798400710056
Publication statusPublished - 16 Sept 2024
Peer-reviewedYes

External IDs

Scopus 85206088149
ORCID /0000-0003-4725-9776/work/171552973
ORCID /0000-0002-5918-804X/work/171553798

Keywords

Keywords

  • artificial intelligence, interview study, machine learning, mental models, misconceptions, qualitative research, students conceptions