Identifying key visual-cognitive processes in students’ interpretation of graph representations using eye-tracking data and math/machine learning based data analysis

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http://hdl.handle.net/10138/232479

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Garcia Moreno-Esteva , E , White , S , Wood , J & Black , A 2017 , Identifying key visual-cognitive processes in students’ interpretation of graph representations using eye-tracking data and math/machine learning based data analysis . in T Dooley & G Gueudet (eds) , Proceedings of the Tenth Congress of the European Society for Research in Mathematics Education (CERME10, February 1-5, 2017) . Dublin City University , Dublin, Ireland , pp. 3928-3935 , CERME10 , Dublin , Ireland , 01/02/2017 . < http://www.mathematik.uni-dortmund.de/~prediger/ERME/CERME10_Proceedings_2017.pdf >

Titel: Identifying key visual-cognitive processes in students’ interpretation of graph representations using eye-tracking data and math/machine learning based data analysis
Författare: Garcia Moreno-Esteva, Enrique; White, Sonia; Wood, Joanne; Black, Alexander
Medarbetare: Dooley, Thérèse
Gueudet, Ghislaine
Upphovmannens organisation: Department of Education
Mobile gaze tracking of social interaction
Utgivare: Dublin City University
Datum: 2017
Språk: eng
Sidantal: 8
Tillhör serie: Proceedings of the Tenth Congress of the European Society for Research in Mathematics Education (CERME10, February 1-5, 2017)
ISBN: 978-1-873769-73-7
Permanenta länken (URI): http://hdl.handle.net/10138/232479
Abstrakt: We present a mathematical and computational analysis, partially based on machine learning techniques, of the visual scan-paths obtained during a graph interpretation task which allows us to identify when the problem solver succeeds in solving the problem with a fair degree of accuracy, and helps to understand the visual-cognitive processes at work during the problem solving task.
Subject: 516 Educational sciences
Referentgranskad: Ja
Licens: unspecified
Användningsbegränsning: openAccess
Parallelpublicerad version: publishedVersion
Finansierad av: SUOMEN AKATEMIA
Finansierings ID:


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