id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
flr-365	Garcia Moreno-Esteva, Enrique; White, Sonia L. J.; Wood, Joanne M.; Black, Alex A.	Application of mathematical and machine learning techniques to analyse eye-tracking data enabling better understanding of children’s visual-cognitive behaviours	2018		.htm	text/html	6404	238	41	3.2 Finding the central data item in each class and what it reveals The average scanpath derived by method two (central data item) determined that correct children had the following sequence: {A1,B5,B1,B1,B1,A1,A1,A1,C2,A1,B2,B2,B2,B2,A2,B2,A2,A2,A1,B3,C2,C1,A3,A3,A1,A3,B3,A4,A3,A4,A3,A3,B2,A2,A1,A1} while the sequence for incorrect children was as follows: {C1,B1,B1,A1,A1,B2,B2,B2,B2,B2,B2,B2,A3,C1,C2,B3,A3,A4,C1,B5,B2,A2,B2,B2,B2,B2,A2,C3,C1,A4,C2,A3,B1, A1,A2,A1} Eye tracking research has revealed that more experienced problem-solvers (experts) can identify task relevant visual information more rapidly than less experienced individuals (novices), and their visual attention (eye fixation scanpaths) tend to be more focused on relevant than irrelevant regions of the visual stimulus (Gegenfurtner, Lehtinen & Säljö, 2011; Tsai, Hou, Lai, Liu, & Yang, 2012); these objective findings were corroborated by self-reported accounts of participants completing the task (Tsai, et al., 2012).	cache/flr-365.htm	txt/flr-365.txt