Dikkat Ağlarının ve Göz Hareketlerinin Hata Ayıklama Performansı Üzerine Etkisinin İncelenmesi

Abstract

The purpose of this research is to determine the effects of high school students’ attention network on debugging performance. Additionally, eye movements were examined while debugging. In this study, a "Debug Performance Test (DEPT)" consisting of ten items including three compiler time bugs, three run time bugs, and four logic bugs was designed to determine participants’ debugging performances. Students’ attention network scores were measured with “Attention Network Test” and debugging performances were measured with DEPT. 108 students participated in the study, in which the levels of attention networks predicted their debugging performance. In addition, Gazepoint GP3 eye tracking device was used to determine the eye movements of students while debugging. Eye movements data were collected from 51 students. Data were analyzed with simple linear regression, multiple linear regression, Information Gain, Gain Ratio and Gini Index. As a result of the analysis, it has been found that general attention network and different attention network (alerting attention network, orienting attention network and executive attention network) levels do not predict debugging performance. Besides, it was observed that students' eye-movement behaviors vary across bug types during their debugging performances. When eye movements of students are examined, it is observed that there is a relationship between structural characteristics of the bug types and eye movements.