Obaidellah, Unaizah Hanum and Blascheck, Tanja and Guarnera, Drew T. and Maletic, Jonathan I. (2020) A fine-grained assessment on novice programmers' gaze patterns on pseudocode problems. In: ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications, 2 - 5 June 2020, Online.
Full text not available from this repository.Abstract
To better understand code comprehension and problem solving strategies, we conducted an eye tracking study that includes 51 undergraduate computer science students solving six pseudocode program comprehension tasks. Each task required students to order a sequence of pseudocode statements necessary to correctly solve a programming problem. We compare the viewing patterns of computer science students to evaluate changes in behavior while participants solve problems of varying difficulty. The intent is to find out if gaze patterns are similar prior to solving the task and if this pattern changes as the problems get more difficult. The findings show that as the difficulty increases regressions between areas of interest also tend to increase. Furthermore, an analysis of clusters of participants' common viewing patterns was performed to identify groups of participants' sharing similar gaze patterns prior to selecting their first choice of answer. Future work suggests an investigation on the relationship of these patterns with other background information (such as gender, age, English language proficiency, course completion) as well as performance (score, duration of task completion, competency level).
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | RP030-14AET, RP061B-18SBS |
| Additional Information: | ACM Symposium on Eye Tracking Research and Applications (ETRA), Online, June 02-05, 2020 |
| Uncontrolled Keywords: | Program comprehension; Eye tracking; Human-computer interface; Teaching/learning strategies; Improving classroom teaching; Programming education |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 20 May 2023 02:40 |
| Last Modified: | 20 May 2023 02:40 |
| URI: | http://eprints.um.edu.my/id/eprint/37134 |
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