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.

这项研究提出了一种用于确定屏幕测试中注视模式的软件系统。该系统将机器学习技术应用于从眼动仪获得的眼动数据，以在解决屏幕测试时根据学生的视线行为模式对其进行分类。通过将眼睛运动坐标转换为感兴趣区域的序列来确定这些模式。拟议的软件系统从序列中提取特征，并执行聚类，从而根据学生的注视方式对其进行分组。为了确定注视模式，该系统包含用于与眼睛跟踪设备进行通信，收集和预处理学生的注视数据以及使用不同的呈现方法对数据进行可视化的组件。这项研究提出了一种方法，以确定注视模式和拟议软件的实施细节。通过确定51名参加了包含20个问题的常识测试的大学生的注视方式来评估这项研究。这项研究旨在提供一种软件基础结构，该结构可以使用学生的凝视模式作为他们阅读行为以及他们的处理注意力或困难等因素的附加指标。