Application of Mathematical and Machine Learning Techniques to Analyse Eye Tracking Data Enabling Better Understanding of Children's Visual Cognitive Behaviours

Autor/inn/en	Moreno-Estevaa, Enrique Garcia; White, Sonia L. J.; Wood, Joanne M.; Black, Alex A.
Titel	Application of Mathematical and Machine Learning Techniques to Analyse Eye Tracking Data Enabling Better Understanding of Children's Visual Cognitive Behaviours
Quelle	In: Frontline Learning Research, 6 (2018) 3, S.72-84 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2295-3159
Schlagwörter	Artificial Intelligence; Man Machine Systems; Mathematics Education; Eye Movements; Graphs; Data Interpretation; Children; Visual Perception; Bayesian Statistics; Time on Task + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Mensch-Maschine-System; Mathematische Bildung; Augenbewegung; Grafische Darstellung; Data evaluation; Datenauswertung; Child; Kind; Kinder; Visuelle Wahrnehmung; Zeitaufwand
Abstract	In this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements were explored using machine learning approaches. Two different techniques of machine-learning were used (Bayesian and K-Means) to obtain separate model sequences or average scanpaths for those children who responded either correctly or incorrectly to the graph task. Application of these machine-learning approaches indicated distinct differences in the resulting scanpaths for children who completed the graph task correctly or incorrectly: children who responded correctly accessed information that was mostly categorised as critical, whereas children responding incorrectly did not. There was also evidence that the children who were correct accessed the graph information in a different, more logical order, compared to the children who were incorrect. The visual behaviours aligned with different aspects of graph comprehension, such as initial understanding and orienting to the graph, and later interpretation and use of relevant information on the graph. The findings are discussed in terms of the implications for early mathematics teaching and learning, particularly in the development of graph comprehension, as well as the application of machine learning techniques to investigations of other visual-cognitive behaviours. (As Provided).
Anmerkungen	European Association for Research on Learning and Instruction. Peterseliegang 1, Box 1, 3000 Leuven, Belgium. e-mail: info@frontlinelearningresearch.org; Web site: http://journals.sfu.ca/flr/index.php/journal/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01