Ahsan, Zubair and Obaidellah, Unaizah Hanum (2021) Visual behavior on problem comprehension among novice programmers with prior knowledge. In: 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021, 8 - 10 September 2021, Szczecin.
Full text not available from this repository.Abstract
Program comprehension studies investigate the underlying cognitive processes of individuals as they perform programming tasks. Over the years, the studies have begin to incorporate devices such as eye-tracking to empirically analyze reading patterns and visual attention. The outcome of such studies enables improved teaching instructions and learning procedures. This research aims to understand the comprehension strategies and the duration that individuals of varying performance take to attempt the programming questions. An experiment was conducted to collect data from 66 novice programmers' eye movements and their performance on algorithmic problems. The stimuli contained questions that the participants were familiar with having studied the same programs before the experiment was conducted to evaluate whether their prior knowledge influences their comprehension strategies. The results indicate that high performing students visit the problem statement more than the low performing students and yet spent less time on the problem statement as compared to the low performing students. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Computer education; Eye-tracking; Novice programmers; Program comprehension; Scanpath analysis |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 20 Oct 2023 06:06 |
| Last Modified: | 20 Oct 2023 06:06 |
| URI: | http://eprints.um.edu.my/id/eprint/35394 |
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