TY - GEN
T1 - A study on attention allocation of psychological distress students based on eye movement data analysis
AU - Li, Bing
AU - Hu, Bin
AU - Li, Xiaowei
AU - Rao, Juan
AU - Wang, Manman
AU - Cai, Hanshu
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Psychological distress affects college students’ academic performance, and attention allocation plays an important role in learning process. In this paper, an experiment which combined smooth pursuit eye movement and alphabets recognition tasks was introduced, with the aim of discovering differences in attention allocation between psychological distress students and normal students. Three kinds of data were collected: the recording of alphabets recognition answers, the eye movement data, and the results of psychological test scale K10. We did statistical analysis on right answers, and the results of Analysis of Variance(ANOVA) showed the differences between psychological distress students and normal students were not statistically significant, however, accuracy changing with different velocities implied some differences. Then we adopted classification algorithms, and found the two groups could be distinguished using eye movement features related to attention allocation, with the highest accuracy of 76%. This also indicated attention allocation was different between the two groups.
AB - Psychological distress affects college students’ academic performance, and attention allocation plays an important role in learning process. In this paper, an experiment which combined smooth pursuit eye movement and alphabets recognition tasks was introduced, with the aim of discovering differences in attention allocation between psychological distress students and normal students. Three kinds of data were collected: the recording of alphabets recognition answers, the eye movement data, and the results of psychological test scale K10. We did statistical analysis on right answers, and the results of Analysis of Variance(ANOVA) showed the differences between psychological distress students and normal students were not statistically significant, however, accuracy changing with different velocities implied some differences. Then we adopted classification algorithms, and found the two groups could be distinguished using eye movement features related to attention allocation, with the highest accuracy of 76%. This also indicated attention allocation was different between the two groups.
KW - Attention allocation
KW - Classification
KW - Eye movement
KW - Psychological distress
KW - Smooth pursuit
UR - http://www.scopus.com/inward/record.url?scp=84924421775&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-15554-8_9
DO - 10.1007/978-3-319-15554-8_9
M3 - Conference contribution
AN - SCOPUS:84924421775
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 115
BT - Human Centered Computing - 1st International Conference, HCC 2014, Revised Selected Papers
A2 - Zu, Qiaohong
A2 - Seng, Sopheap
A2 - Zu, Qiaohong
A2 - Gu, Ning
A2 - Hu, Bo
PB - Springer Verlag
T2 - 1st International Conference on Human Centered Computing, HCC 2014
Y2 - 27 November 2014 through 29 November 2014
ER -