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

Maike Schindler, Erik Schaffernicht, Achim J. Lilienthal

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

3 Zitate (Scopus)

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.

OriginalspracheEnglisch
TitelProceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education, 2021
Redakteure/-innenMaitree Inprasitha, Narumon Changsri, Nisakorn Boonsena
Herausgeber (Verlag)Psychology of Mathematics Education (PME)
Seiten9-16
Seitenumfang8
ISBN (Print)9786169383031
PublikationsstatusVeröffentlicht - 2021
Extern publiziertJa
Veranstaltung44th Conference of the International Group for the Psychology of Mathematics Education, PME 2021 - Virtual, Online
Dauer: 19 Juli 202122 Juli 2021

Publikationsreihe

NameProceedings of the International Group for the Psychology of Mathematics Education
Band4
ISSN (Print)0771-100X
ISSN (elektronisch)2790-3648

Konferenz

Konferenz44th Conference of the International Group for the Psychology of Mathematics Education, PME 2021
OrtVirtual, Online
Zeitraum19/07/2122/07/21

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