Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis

Villamor, Maureen; Rodrigo, Maria Mercedes T.

doi:10.1561/116.00000031

Cited by 3 publications

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“…Fixations are spatially stable gaze points during which time visual processing takes place (Poole & Ball, 2006) while saccades are the rapid eye movements in between fixations, during which time no visual processing occurs (Yarbus, 1967). Tracking a pair’s eye movements simultaneously—i.e., dual eye tracking —has been explored to study joint attention in collaborative learning situations (Villamor & Rodrigo, 2022).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Visual Attention of Teams During Workload Transitions Using AOI-Based Cross-Recurrence Metrics

Atweh,

Hayek,

Riggs

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Cross-recurrence quantification analysis (CRQA) metrics may offer a means to provide information about the quality of collaboration in real-time. The goal of the present work is to use Area of Interest (AOI) based CRQA metrics to analyze the eye-tracking data of 10 pairs who participated in a shared unmanned aerial vehicle (UAV) command and control task. We are interested in how teams respond to workload transitions and how it affects AOI-based CRQA metrics. The results showed that as workload increased, team members spent a longer time on the same task which may indicate that they are coordinating together on a task, or they are not adapting and getting “trapped” in certain tasks. The findings suggest that CRQA AOIbased metrics are sensitive to workload changes and validate these metrics in unraveling the visual puzzle of how workload impacts scanpath patterns which contribute to quantifying the adaptation process of pairs over time. This also has the potential to inform the design of real-time technology in the future.

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Section: Introductionmentioning

confidence: 99%

Quantifying Visual Attention of Teams During Workload Transitions Using AOI-Based Cross-Recurrence Metrics

Atweh,

Hayek,

Riggs

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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How Do Programming Students Read and Act upon Compiler Error Messages?

Rodrigo

Tablatin

2023

Lecture Notes in Computer Science

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Visual Attention Patterns in Finding Source Code Defects

Tablatin

2023

Wseas Transactions on Information Science and Applications

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Existing research used visual effort metrics to determine the visual attention patterns of participants with varying skill levels while finding source code defects. While most of the findings of these studies agree on the results for fixation count metrics, there are differences in the results for fixation duration metrics. Therefore, there is a need for further investigations on the use of visual effort metrics to determine the difference in the visual effort of experts and novices between multiple programs. Thus, we aimed to identify the factors affecting the varying results on fixation duration metrics and validate the results on fixation count metrics. We used visual effort metrics to identify the visual attention patterns of high and low-performing students engaged in defect-finding tasks on multiple programs. We performed statistical tests on the total fixation count, fixation counts on the error lines, total fixation duration, and fixation duration on the error lines to determine the difference in the visual attention patterns between the groups. Among the fixation metrics used, only the total fixation duration metric revealed a significant difference between the high and low-performing students across all programs. High-performing students spent less time on simple programs with simple error types but spent more time on complex programs with logical and semantic error types. In contrast, low-performing students focused more attention on easy programs with one or more syntax errors compared to high-performing students. The results of this study could shed some light on the contrasting findings of previous studies regarding fixation duration. These findings suggest that visual attention patterns of high and low-performing students may vary on multiple programs. The amount of visual effort exerted by the group depends upon the program’s complexity, location of errors in the source code, type of errors injected, and the number of lines of code. This implies that the time spent finding the errors may be associated with the characteristics of the programs and the location and type of injected errors. Therefore, researchers must provide detailed information on these characteristics when describing differences in visual effort metrics between subjects engaged in bug-finding tasks.

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Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis

Cited by 3 publications

References 0 publications

Quantifying Visual Attention of Teams During Workload Transitions Using AOI-Based Cross-Recurrence Metrics

Quantifying Visual Attention of Teams During Workload Transitions Using AOI-Based Cross-Recurrence Metrics

How Do Programming Students Read and Act upon Compiler Error Messages?

Visual Attention Patterns in Finding Source Code Defects

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