IDENTIFYING STUDENT STRATEGIES THROUGH EYE TRACKING AND UNSUPERVISED LEARNING: THE CASE OF QUANTITY RECOGNITION

Maike Schindler, Erik Schaffernicht, Achim J. Lilienthal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Identifying student strategies is an important endeavor in mathematics education research. Eye tracking (ET) has proven to be valuable for this purpose: E.g., analysis of ET videos allows for identification of student strategies, particularly in quantity recognition activities. Yet, “manual”, qualitative analysis of student strategies from ET videos is laborious—which calls for more efficient methods of analysis. Our methodological paper investigates opportunities and challenges of using unsupervised machine learning (USL) in combination with ET data: Based on empirical ET data of N = 164 students and heat maps of their gaze distributions on the task, we used a clustering algorithm to identify student strategies from ET data and investigate whether the clusters are consistent regarding student strategies.

Original languageEnglish
Title of host publicationProceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education, 2021
EditorsMaitree Inprasitha, Narumon Changsri, Nisakorn Boonsena
PublisherPsychology of Mathematics Education (PME)
Pages9-16
Number of pages8
ISBN (Print)9786169383031
StatePublished - 2021
Externally publishedYes
Event44th Conference of the International Group for the Psychology of Mathematics Education, PME 2021 - Virtual, Online
Duration: 19 Jul 202122 Jul 2021

Publication series

NameProceedings of the International Group for the Psychology of Mathematics Education
Volume4
ISSN (Print)0771-100X
ISSN (Electronic)2790-3648

Conference

Conference44th Conference of the International Group for the Psychology of Mathematics Education, PME 2021
CityVirtual, Online
Period19/07/2122/07/21

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