Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
Previous research has examined how eye-tracking metrics can serve as a proxy for directly measuring the amount of cognitive effort and processing required for comprehending computer code. We conducted a pilot study comprising expert (n = 10) and novice (n = 10) computer programmers to examine group differences in code comprehension abilities and perceptions. Programmers were asked to read two pieces of computer code, rate the code on various attributes, and then describe what the code does. Results indicate that experts and novices significantly differ in terms of their fixation counts made during the task, such that experts had more fixations than novices. This was counter to our hypothesis that experts would have fewer fixations than novices. We found no evidence that experts and novices differed in their average fixation durations, trustworthiness and performance perceptions, or willingness to reuse the code.
Using Eye-Tracking Data to Compare Differences in Code Comprehension and Code Perceptions between Expert and Novice Programmers
Online
Previous research has examined how eye-tracking metrics can serve as a proxy for directly measuring the amount of cognitive effort and processing required for comprehending computer code. We conducted a pilot study comprising expert (n = 10) and novice (n = 10) computer programmers to examine group differences in code comprehension abilities and perceptions. Programmers were asked to read two pieces of computer code, rate the code on various attributes, and then describe what the code does. Results indicate that experts and novices significantly differ in terms of their fixation counts made during the task, such that experts had more fixations than novices. This was counter to our hypothesis that experts would have fewer fixations than novices. We found no evidence that experts and novices differed in their average fixation durations, trustworthiness and performance perceptions, or willingness to reuse the code.
https://aisel.aisnet.org/hicss-54/cl/teaching_and_learning_technologies/13