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1. A practical guide on conducting eye tracking studies in software engineering

   https://doi.org/10.1007/s10664-020-09829-4  

   Sharafi, Zohreh; Sharif, Bonita; Guéhéneuc, Yann-Gaël; Begel, Andrew; Bednarik, Roman; Crosby, Martha (October 2020, Empirical software engineering)

   null (Ed.)

   For several years, the software engineering research community used eye trackers to study program comprehension, bug localization, pair programming, and other software engineering tasks. Eye trackers provide researchers with insights on software engineers’ cognitive processes, data that can augment those acquired through other means, such as on-line surveys and questionnaires. While there are many ways to take advantage of eye trackers, advancing their use requires defining standards for experimental design, execution, and reporting. We begin by presenting the foundations of eye tracking to provide context and perspective. Based on previous surveys of eye tracking for programming and software engineering tasks and our collective, extensive experience with eye trackers, we discuss when and why researchers should use eye trackers as well as how they should use them. We compile a list of typical use cases—real and anticipated—of eye trackers, as well as metrics, visualizations, and statistical analyses to analyze and report eye-tracking data. We also discuss the pragmatics of eye tracking studies. Finally, we offer lessons learned about using eye trackers to study software engineering tasks. This paper is intended to be a one-stop resource for researchers interested in designing, executing, and reporting eye tracking studies of software engineering tasks. 

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2. 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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3. Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

   https://doi.org/10.3389/fmars.2021.785357  

   Reavis, Janie L.; Demir, H. Seckin; Witherington, Blair E.; Bresette, Michael J.; Blain Christen, Jennifer; Senko, Jesse F.; Ozev, Sule (November 2021, Frontiers in Marine Science)

   Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficiently documented or described in the literature. Understanding sea turtle behavior in relation to fishing gear is key to discovering how they become entangled or entrapped in gear. This information can also be used to reduce fisheries interactions. However, recording and analyzing these behaviors is difficult and time intensive. In this study, we present a machine learning-based sea turtle behavior recognition scheme. The proposed method utilizes visual object tracking and orientation estimation tasks to extract important features that are used for recognizing behaviors of interest with green turtles ( Chelonia mydas ) as the study subject. Then, these features are combined in a color-coded feature image that represents the turtle behaviors occurring in a limited time frame. These spatiotemporal feature images are used along a deep convolutional neural network model to recognize the desired behaviors, specifically evasive behaviors which we have labeled “reversal” and “U-turn.” Experimental results show that the proposed method achieves an average F1 score of 85% in recognizing the target behavior patterns. This method is intended to be a tool for discovering why sea turtles become entangled in gillnet fishing gear. 

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4. Factors Contributing to the Problem-Solving Heuristics of Civil Engineering Students

   Geston, Sean L.; Brown, Shane; Barner, Matthew S.; Abadi, Masoud G.; Hurwitz, David S. (January 2019, ASEE annual conference & exposition)

   Problem solvers vary their approaches to solving problems depending on the context of the problem, the requirements of the solution, and the ways in which the problems and material to solve the problem are represented, or representations. Representations take many forms (i.e. tables, graphs, figures, images, formulas, visualizations, and other similar contexts) and are used to communicate information to a problem solver. Engagement with certain representations varies between problem solvers and can influence design and solution quality. A problem solver’s evaluation of representations and the reasons for using a representation can be considered factors in problem-solving heuristics. These factors describe unique problem-solving behaviors that can help understand problem solvers. These behaviors may lead to important relationships between a problem solver’s decisions and their ability to solve a problem and overall quality of the solution. Therefore, we pose the following research question: How do factors of problem-solving heuristics describe the unique behaviors of engineering students as they solve multiple problems? To answer this question, we interviewed 16 undergraduate engineering students studying civil engineering. The interviews consisted of a problem-solving portion that was followed immediately by a semi-structured retrospective interview with probing questions created based on the real time monitoring of the problem-solving interview using eye tracking techniques. The problem-solving portion consisted of solving three problems related to the concept of headloss in fluid flow through pipes. Each of the three problems included the same four representations that were used by the students as approaches to solving the problem. The representations are common ways to present the concept of headloss in pipe flow and included two formulas, a set of tables, and a graph. This paper presents a set of common reasons for why decisions were made during the problem-solving process that help to understand more about the problem-solving behavior of engineering students. 

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5. Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning Techniques

   https://doi.org/10.3390/s24134067  

   Liu, Jiefei; Bailey, Derek W; Cao, Huiping; Son, Tran Cao; Tobin, Colin T (July 2024, Sensors)

   Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations. 

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