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1. Analyzing Unconstrained Reading Patterns of Digital Documents Using Eye Tracking

   https://doi.org/10.1109/JCDL52503.2021.00036  

   Mahanama, Bhanuka; Jayawardena, Gavindya; Jayarathna, Sampath (September 2021, 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL))

   Researchers read scientific literature to keep current in the field and find state-of-the-art solutions to various scientific problems. Prior work suggests that reading patterns may vary with the researcher’s domain expertise and on the content of the digital document. In this work, we present a pilot study of eye-tracking measures during a reading task with the options for zooming and panning of the reading material. The main goal is to analyze unconstrained reading patterns of digital documents using eye movement fixations and dwell time on various sections of a digital document. Our results indicate that participants mostly focused on methodology and results sections, which is consistent with the prior work with constrained reading patterns. 

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2. Can the E-Z Reader Model Predict Eye Movements Over Code? Towards a Model of Eye Movements Over Source Code

   https://doi.org/10.1145/3379156.3391983  

   Madi, Naser Al; Peterson, Cole S.; Sharif, Bonita; Maletic, Jonathan (May 2020, ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications)

   null (Ed.)

   Studies of eye movements during source code reading have supported the idea that reading source code differs fundamentally from reading natural text. The paper analyzed an existing data set of natural language and source code eye movement data using the E-Z reader model of eye movement control. The results show that the E-Z reader model can be used with natural text and with source code where it provides good predictions of eye movement duration. This result is confirmed by comparing model predictions to eye-movement data from this experiment and calculating the correlation score for each metric. Finally, it was found that gaze duration is influenced by token frequency in code and in natural text. The frequency effect is less pronounced on first fixation duration and single fixation duration. An eye movement control model for source code reading may open the door for tools in education and the industry to enhance program comprehension. 

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3. From Novice to Expert: Analysis of Token Level Effects in a Longitudinal Eye Tracking Study

   https://doi.org/10.1109/ICPC52881.2021.00025  

   Al Madi, Naser; Peterson, Cole S.; Sharif, Bonita; Maletic, Jonathan I. (May 2021, 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC))

   null (Ed.)

   Program comprehension is a vital skill in software development. This work investigates program comprehension by examining the eye movement of novice programmers as they gain programming experience over the duration of a Java course. Their eye movement behavior is compared to the eye movement of expert programmers. Eye movement studies of natural text show that word frequency and length influence eye movement duration and act as indicators of reading skill. The study uses an existing longitudinal eye tracking dataset with 20 novice and experienced readers of source code. The work investigates the acquisition of the effects of token frequency and token length in source code reading as an indication of program reading skill. The results show evidence of the frequency and length effects in reading source code and the acquisition of these effects by novices. These results are then leveraged in a machine learning model demonstrating how eye movement can be used to estimate programming proficiency and classify novices from experts with 72% accuracy. 

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4. Measuring and Understanding Online Reading Behaviors of People with Dyslexia

   Grusky, Max; Taft, Jessie; Naaman, Mor; Azenkot, Shiri (July 2020, In submission)

   Extending the benefits of online reading to people with reading disabilities such as dyslexia requires broader research on reading behavior in addition to existing small-scale eye-tracking studies. We conduct the first large-scale mixed-methods study of the unique reading challenges of people with dyslexia. We combine in-person interviews (N=6), online surveys (N=566) and a novel browser-based tool able to measure detailed reading behavior remotely on a controlled set of five pages (N=477) or as a browser extension (N=89) collecting long-term reading behavior data on self-selected pages. We find a variety of text and page layout factors that pose challenges to readers with and without dyslexia, and identify in-browser reading behaviors associated with dyslexia. Findings point toward improvements to technologies for identifying struggling readers, and to ways to improve the layout and appearance of online articles to improve reading ease for people with and without dyslexia. 

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5. Individual Chunking Ability Predicts Efficient or Shallow L2 Processing: Eye-Tracking Evidence From Multiword Units in Relative Clauses

   https://doi.org/10.3389/fpsyg.2020.607621  

   Pulido, Manuel F. (January 2021, Frontiers in Psychology)

   null (Ed.)

   Behavioral studies on language processing rely on the eye-mind assumption, which states that the time spent looking at text is an index of the time spent processing it. In most cases, relatively shorter reading times are interpreted as evidence of greater processing efficiency. However, previous evidence from L2 research indicates that non-native participants who present fast reading times are not always more efficient readers, but rather shallow parsers. Because earlier studies did not identify a reliable predictor of variability in L2 processing, such uncertainty around the interpretation of reading times introduces a potential confound that undermines the credibility and the conclusions of online measures of processing. The present study proposes that a recently developed modulator of online processing efficiency, namely, chunking ability, may account for the observed variability in L2 online reading performance. L1 English – L2 Spanish learners’ eye movements were analyzed during natural reading. Chunking ability was predictive of overall reading speed. Target relative clauses contained L2 Verb-Noun multiword units, which were manipulated with regards to their L1-L2 congruency. The results indicated that processing of the L1-L2 incongruent units was modulated by an interaction of L2 chunking ability and level of knowledge of multiword units. Critically, the data revealed an inverse U-shaped pattern, with faster reading times in both learners with the highest and the lowest chunking ability scores, suggesting fast integration in the former, and lack of integration in the latter. Additionally, the presence of significant differences between conditions was correlated with individual chunking ability. The findings point at chunking ability as a significant modulator of general L2 processing efficiency, and of cross-language differences in particular, and add clarity to the interpretation of variability in the online reading performance of non-native speakers. 

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