Serviceeinschränkungen vom 12.-22.02.2026 - weitere Infos auf der UB-Homepage

Treffer: Tracking Students’ Cognitive Processes During Program Debugging—An Eye-Movement Approach.

Title:
Tracking Students’ Cognitive Processes During Program Debugging—An Eye-Movement Approach.
Source:
IEEE Transactions on Education. Aug2016, Vol. 59 Issue 3, p175-186. 12p.
Database:
Business Source Premier

Weitere Informationen

This study explores students' cognitive processes while debugging programs by using an eye tracker. Students' eye movements during debugging were recorded by an eye tracker to investigate whether and how high- and low-performance students act differently during debugging. Thirty-eight computer science undergraduates were asked to debug two C programs. The path of students' gaze while following program codes was subjected to sequential analysis to reveal significant sequences of areas examined. These significant gaze path sequences were then compared to those of students with different debugging performances. The results show that, when debugging, high-performance students traced programs in a more logical manner, whereas low-performance students tended to stick to a line-by-line sequence and were unable to quickly derive the program's higher-level logic. Low-performance students also often jumped directly to certain suspected statements to find bugs, without following the program's logic. They also often needed to trace back to prior statements to recall information, and spent more time on manual computation. Based on the research results, adaptive instructional strategies and materials can be developed for students of different performance levels, to improve associated cognitive activities during debugging, which can foster learning during debugging and programming. [ABSTRACT FROM PUBLISHER]

Copyright of IEEE Transactions on Education is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)