.png){kind=icon}
返回
Determining Gaze Behavior Patterns in On-Screen Testing
.png){kind=icon}
PRE
AI
DOI：10.1177/0735633120978617.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This study proposes a software system for determining gaze patterns in on-screen testing. The system applies machine learning techniques to eye-movement data obtained from an eye-tracking device to categorize students according to their gaze behavior pattern while solving an on-screen test. These patterns are determined by converting eye movement coordinates into a sequence of regions of interest. The proposed software system extracts features from the sequence and performs clustering that groups students by their gaze pattern. To determine gaze patterns, the system contains components for communicating with an eye-tracking device, collecting and preprocessing students' gaze data, and visualizing data using different presentation methods. This study presents a methodology to determine gaze patterns and the implementation details of the proposed software. The research was evaluated by determining the gaze patterns of 51 undergraduate students who took a general knowledge test containing 20 questions. This study aims to provide a software infrastructure that can use students' gaze patterns as an additional indicator of their reading behaviors and their processing attention or difficulty, among other factors.
Keyword:
eye tracking
on-screen testing
data mining
machine learning
gaze behavior patterns
期刊
IF:
4.9
论文数:
1.6K
被引数:
4.0K
机构
引用论文
POSB4 Effectiveness of Caplacizumab on Reducing Acute Mortality in Acquired Thrombotic Thrombocytopenic Purpura: Results from a French National Registry (CNR-MAT)POSB4 胶原蛋白-阿司匹林（Caplacizumab）对降低获得性血栓性血小板减少性紫癜急性期死亡率的有效性：来自法国国家登记系统（CNR-MAT）的结果
OAAI
.png){kind=icon}
OAAI分享收藏
分享收藏分享收藏
查看更多内容