Understanding how visual attention is attended by programmers is critical to progress in the field of programming education. Conventional eye-tracking studies typically employ basic and coarsely defined Areas of Interest (AOIs), which fail to capture the underlying semantic content of source code. We present an automated system that produces Cognitive Attention Maps using Optical Character Recognition (OCR) technology-based AOIs and semantic tokens. Our system extracts bounding boxes from rendered code, enabling the alignment of gaze data with semantically relevant code components, such as identifiers, operators, keywords, and literals. The proposed framework facilitates multi-dimensional visual attention profiling (frequency and duration). Our framework, tested on programming tasks, reveals visual attention and performance-based differences that are not possible with conventional AOIs. Our study offers an in-depth and sophisticated perspective on programmer cognition. Future work will aim to enhance scanpath prediction by improving the semantic tokens’ integration to achieve more precise visual behavior prediction.

This paper investigates how the distribution of attention during problem-solving tasks can reveal cognitive processes in programmers of varying abilities, especially in program comprehension where the impact of abilities on the dynamic change of eye movement behavior over time is less understood. Through a quantitative analysis of eye gaze behavior, categorized into focused behavior (concentrating on a few positions) and overview behavior (scanning many positions), based on AoI sequences, the study analyzes data from 66 novice programmers tackling program comprehension tasks across selection and iteration constructs. The research aims to determine differences in attention allocation between high- and low-ability participants during programming problem-solving. Utilizing the TS-AOI method, findings show that high-ability participants maintain more focused attention and demonstrate shifts in attention from the early to the late phases of tasks, unlike their low-ability counterparts. These insights could guide strategies to support students in enhancing their programming comprehension skills.

The studies on program comprehension using eye-tracking technology have not largely used Scanpath Trend Analysis (STA) to generate common scanpaths for a group of specific expertise in programming comprehension studies. It is important to understand the applicability of STA to help educators distinguish the reading orders of individuals as they solve programming tasks to develop better educational materials and improve instruction. In this research work, we conducted an experiment using common fundamental programming questions on 66 undergraduate computer science students to study the gaze behavior among the novices (high and low performing) on programming comprehension. We aim to better understand the navigation behavior between groups of high and low performers’ common scanpaths generated by STA and whether Hierarchical Cluster Analaysis (HCA) can cluster these common scanpaths for high- and low-performing individuals across different stimuli. Findings suggest that the STA algorithm is a technique to consider to find common representative scanpaths of a group of individuals, however, HCA with relative Levenshtein distance metric alone may not be suitable to cluster high and low performers groups for varying numbers of AOIs across different stimuli.

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.

This eye tracking study examines participants' visual attention when solving algorithmic problems in the form of programming problems. The stimuli consisted of a problem statement, example output, and a set of multiple-choice questions regarding variables, data types, and operations needed to solve the programming problems. We recorded eye movements of students and performed an Area of Interest (Aol) sequence analysis to identify reading strategies in terms of participants' performance and visual effort. Using classical eye tracking metrics and a visual Aol sequence analysis we identified two main groups of participants---effective and ineffective problem solvers. This indicates that diversity of participants' mental schemas leads to a difference in their performance. Therefore, identifying how participants' reading behavior varies at a finer level of granularity warrants further investigation.

Gender differences in programming comprehension has been a topic of discussion in recent years. We conducted an eye-tracking study on 51(21 female, 30 male) computer science undergraduate university students to examine their cognitive processes in pseudocode comprehension. We aim to identify their reading strategies and eye gaze behavior on the comprehension of pseudocodes in terms of performance and visual effort when solving algorithmic problems of varying difficulty levels. Each student completed a series of tasks requiring them to rearrange randomized pseudocode statements in a correct order for the problem presented. Our results indicated that the speed of analyzing the problems were faster among male students, although female students fixated longer in understanding the problem requirements. In addition, female students more commonly fixated on indicative verbs (i.e., prompt, print), while male students fixated more on operational statements (i.e., loops, variables calculations, file handling).