The Relationship Between Eye Fixation Patterns and Performance in Java Programming Exercises
Eye-tracking technology has provided researchers with an innovative way to investigate various aspects of human behavior. In this study, the focus was on examining the relationship between eye fixation patterns and performance in Java programming exercises. The aim was to determine whether there were any significant differences in the eye movements of students who answered the exercises correctly compared to those who answered incorrectly.
A total of thirty-one students from a university in Metro Manila participated in the study. They were asked to solve five Java programming exercises, and their eye movements were recorded using an eye-tracking device. However, for the analysis, only the fixation data from three of the five exercises were considered.
The first step in the analysis process was to preprocess the fixation data. This involved filtering out any irrelevant data points and converting them into a format that could be easily visualized. Once the data was ready, heatmap bin graphs were generated to visualize the eye fixation patterns of the participants.
Dividing the participants into two groups based on their answers (correct and wrong), the researchers were then able to compare the fixation patterns between the groups. This was done using the Mann-Whitney U Test, a non-parametric statistical test suitable for comparing two groups when the data is not normally distributed.
The results of the analysis showed that there were significant differences in the eye fixation patterns between the correct and wrong answer groups. Participants who provided correct answers tended to have longer fixations on certain code segments, suggesting that they were more deeply engaged in analyzing and understanding the problem. On the other hand, participants who answered incorrectly tended to have more scattered fixations, indicating a lack of focus and attention to crucial details.
These findings have important implications for programming education. By understanding the relationship between eye fixation patterns and performance, instructors can develop targeted interventions to improve students’ coding skills. For example, exercises can be designed to encourage longer fixations on critical code segments, fostering a more systematic and thorough approach to problem-solving.
Furthermore, eye-tracking technology can be integrated into programming courses as a diagnostic tool. By tracking students’ eye movements during coding exercises, instructors can identify areas where students may be struggling and provide personalized feedback and support.
In conclusion, this study highlights the potential of eye-tracking technology in understanding and improving programming performance. By gaining insights into the relationship between eye fixation patterns and performance, educators can enhance the effectiveness of programming instruction and better support students in acquiring essential coding skills.