Md Shakil Hossain
ABSTRACT
Code comprehension studies techniques for extracting information that gives insights into how software is understood. For educators, this is an important but often difficult task. This is further complicated by larger classes, limited time, and not enough grading resources for early identification of students in need of help or to provide early feedback. During a code comprehension task, analyzing where a student look can provide valuable insights into what information the student perceives as important. The instructor can then assess if the student is looking in the right areas of the code. We investigate differences in how a student's eyes traverse code during a coding comprehension exercise and propose a systematic method to distinguish between students with a good understanding of the exercise and those who need additional help. The methodology uses coding exercises seeded with errors, a graded results of completed the exercises, measured fixation counts, and average fixation durations of the students' eyes within what we refer to as the targeted region of interest (TROI) of the code. We conducted experiments using basic Java code from the Programming Principles II course, and our eye-tracking data showed that students' ability to understand the context of the code (the grade on the task) and make proper judgments (feedback on their decisions) was positively correlated with a higher ratio in the number of fixations in the TROI.
- Bradley Boswell, Andrew Sanders, Andrew Allen, Gursimran Singh Walia, and Md Shakil Hossain. 2022. Using AI-based NiCATS System to Evaluate Student Comprehension in Introductory Computer Programming Courses. In 2022 IEEE Frontiers in Education Conference (FIE). 1--9. https://doi.org/10.1109/FIE56618.2022.9962681Google Scholar
Cross Ref
- M. Ali Akber Dewan, Mahbub Murshed, and Fuhua Lin. 2019. Engagement Detection in Online Learning: A Review. Smart Learning Environments 6, 1 (03 Jan 2019), 1. https://doi.org/10.1186/s40561-018-0080-zGoogle ScholarCross Ref
- Thomas Fritz, Andrew Begel, Sebastian C. Müller, Serap Yigit-Elliott, and Manuela Züger. 2014. Using Psycho-Physiological Measures to Assess Task Difficulty in Software Development. In Proceedings of the 36th International Conference on Software Engineering (Hyderabad, India) (ICSE 2014). Association for Computing Machinery, New York, NY, USA, 402--413. https://doi.org/10.1145/2568225.2568266Google ScholarDigital Library
- Haytham Hijazi, José Cruz, João Castelhano, Ricardo Couceiro, Miguel Castelo-Branco, Paulo de Carvalho, and Henrique Madeira. 2021. iReview: an Intelligent Code Review Evaluation Tool using Biofeedback. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE). 476--485. https://doi.org/10.1109/ISSRE52982.2021.00056Google ScholarCross Ref
- Md Shakil Hossain, Dhruv Pandya, Andrew Allen, and Felix G. Hamza-Lup. 2023. Learner Attention Quantification Using Eye Tracking and EEG Signals. In Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2, Kohei Arai (Ed.). Springer International Publishing, Cham, 836--847.Google Scholar
- Paige Rodeghero and Collin McMillan. 2015. An Empirical Study on the Patterns of Eye Movement during Summarization Tasks. In 2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). 1--10. https://doi.org/10.1109/ESEM.2015.7321188Google ScholarCross Ref
- David Rosengrant, Doug Hearrington, Kerriann Alvarado, and Danielle Keeble. 2012. Following Student Gaze Patterns in Physical Science Lectures. AIP Conference Proceedings 1413, 1 (02 2012), 323--326. https://doi.org/10.1063/1.3680060 arXiv:https://pubs.aip.org/aip/acp/article-pdf/1413/1/323/12210821/323_1_online.pdfGoogle ScholarCross Ref
- Dario D. Salvucci and Joseph H. Goldberg. 2000. Identifying Fixations and Saccades in Eye-Tracking Protocols. In Proceedings of the 2000 Symposium on Eye Tracking Research & Applications (Palm Beach Gardens, Florida, USA) (ETRA '00). Association for Computing Machinery, New York, NY, USA, 71--78. https://doi.org/10.1145/355017.355028Google ScholarDigital Library
- Andrew Sanders, Bradley Boswell, Andrew Allen, Gursimran Singh Walia, and Md Shakil Hossain. 2022. Development and Field-Testing of a Non-intrusive Classroom Attention Tracking System (NiCATS) for Tracking Student Attention in CS Classrooms. In 2022 IEEE Frontiers in Education Conference (FIE). 1--9. https://doi.org/10.1109/FIE56618.2022.9962447Google ScholarCross Ref
- Zohreh Sharafi, Timothy Shaffer, Bonita Sharif, and Yann-Gaël Guéhéneuc. 2015. Eye-Tracking Metrics in Software Engineering. In 2015 Asia-Pacific Software Engineering Conference (APSEC). 96--103. https://doi.org/10.1109/APSEC.2015.53Google ScholarCross Ref
- Tasnia Tabassum, Andrew A. Allen, and Pradipta De. 2020. Non-Intrusive Identification of Student Attentiveness and Finding Their Correlation with Detectable Facial Emotions. In Proceedings of the 2020 ACM Southeast Conference (Tampa, FL, USA) (ACM SE '20). Association for Computing Machinery, New York, NY, USA, 127--134. https://doi.org/10.1145/3374135.3385263Google ScholarDigital Library
- Narayanan Veliyath, Pradipta De, Andrew A. Allen, Charles B. Hodges, and Aniruddha Mitra. 2019. Modeling Students' Attention in the Classroom Using Eyetrackers. In Proceedings of the 2019 ACM Southeast Conference (Kennesaw, GA, USA) (ACM SE '19). Association for Computing Machinery, New York, NY, USA, 2--9. https://doi.org/10.1145/3299815.3314424Google ScholarDigital Library
- Jacob Whitehill, Zewelanji Serpell, Yi-Ching Lin, Aysha Foster, and Javier R. Movellan. 2014. The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions. IEEE Transactions on Affective Computing 5, 1 (2014), 86--98. https://doi.org/10.1109/TAFFC.2014.2316163Google ScholarCross Ref
- Ziwei Zhu, Sebastian Ober, and Roozbeh Jafari. 2017. Modeling and Detecting Student Attention and Interest Level Using Wearable Computers. In 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 13--18. https://doi.org/10.1109/BSN.2017.7935996Google ScholarCross Ref
Index Terms
- Leveraging Eye Tracking and Targeted Regions of Interest for Analyzing Code Comprehension
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