In this study, we taught 20 physics students two different visual strategies to graphically interpret the physical meaning of vector field divergence. Using eye-tracking technology, we recorded students' eye-movement behavior of both strategies when they were engaged in graphical vector field representations. From the eye-tracking data we extracted the number of fixations and saccadic direction and proposed a linear SVM model to classify strategies of problem-solving in the vector field domain. The results show different gaze patterns for the two strategies, and the influence of vector flow orientation on gaze-patterns. A high accuracy of 81.2%(0.11%) has been achieved by testing the algorithm using cross-validation, i.e. that the algorithm is able to predict the strategy the student applies to judge the divergence of a vector field. The results provide guiding tools for learning-effective instruction design and teachers gain benefit from monitoring the students' non-verbal level of performance and fluency using each strategy. Apart from that, students would receive the objective feedback on their progress of learning.

Relating mathematical concepts to graphical representations is a challenging task for students. In this paper, we introduce two visual strategies to qualitatively interpret the divergence of graphical vector field representations. One strategy is based on the graphical interpretation of partial derivatives, while the other is based on the flux concept. We test the effectiveness of both strategies in an instruction-based eye-tracking study with N = 41 physics majors. We found that students' performance improved when both strategies were introduced (74% correct) instead of only one strategy (64% correct), and students performed best when they were free to choose between the two strategies (88% correct). This finding supports the idea of introducing multiple representations of a physical concept to foster student understanding. Relevant eye-tracking measures demonstrate that both strategies imply different visual processing of the vector field plots, therefore reflecting conceptual differences between the strategies. Advanced analysis methods further reveal significant differences in eye movements between the best and worst performing students. For instance, the best students performed predominantly horizontal and vertical saccades, indicating correct interpretation of partial derivatives. They also focused on smaller regions when they balanced positive and negative flux. This mixed-method research leads to new insights into student visual processing of vector field representations, highlights the advantages and limitations of eye-tracking methodologies in this context, and discusses implications for teaching and for future research. The introduction of saccadic direction analysis expands traditional methods, and shows the potential to discover new insights into student understanding and learning difficulties.

The competent handling of representations is required for understanding physics' concepts, developing problem-solving skills, and achieving scientific expertise. Using eye-tracking methodology, we present the contributions of this paper as follows: We first investigated the preferences of students with the different levels of knowledge; experts, intermediates, and novices, in representational competence in the domain of physics problem-solving. It reveals that experts more likely prefer to use vector than other representations. Besides, a similar tendency of table representation usage was observed in all groups. Also, diagram representation has been used less than others. Secondly, we evaluated three similarity measures; Levenshtein distance, transition entropy, and Jensen-Shannon divergence. Conducting Recursive Feature Elimination technique suggests Jensen-Shannon divergence is the best discriminating feature among the three. However, investigation on mutual dependency of the features implies transition entropy mutually links between two other features where it has mutual information with Levenshtein distance (Maximal Information Coefficient = 0.44) and has a correlation with Jensen-Shannon divergence (r(18313) = 0.70, p < .001).

Measuring the attention of users is necessary to design smart Human Computer Interaction (HCI) systems. Particularly, in reading, the reading types, so-called reading, skimming, and scanning are signs to express the degree of attentiveness. Eye movements are informative spatiotemporal data to measure quality of reading. Eye tracking technology is the tool to record eye movements. Even though there is increasing usage of eye trackers in research and especially in psycholinguistics, collecting appropriate task-specific eye movements data is expensitive and time consuming. Moreover, machine learning tools like Recurrent Neural Networks need large enough samples to be trained. Hence, designing a generative model in order to have reliable research-oriented synthetic eye movements is desirable. This paper has two main contributions. First, a generative model in order to synthesize reading, skimming, and scanning in reading is developed. Second, in order to evaluate the generative model, a bidirectional Long ShortTerm Memory (BLSTM) is proposed. It was trained with synthetic data and tested with real-world eye movements to classify reading, skimming, and scanning where more than 95% classification accuracy is achieved. ACM Classification

This paper asks the question: how salient is human gaze for Adjective Noun Concepts (a.k. a Adjective Noun Pairs - ANPs)? In an existing work the authors presented the behavior of human gaze attention with respect to ANPs using eye-tracking setup, because such knowledge can help in developing a better sentiment classification system. However, in this work, only very few ANPs, out of thousands, were covered because of time consuming eye-tracking based data gathering mechanism. What if we need to gather the similar knowledge for a large number of ANPs? For example this could be required for designing a better ANP based sentiment classification system. In order to handle that objective automatically and without using an eye-tracking based setup, this work investigated if there are saliency detection methods capable of recreating the human gaze behavior for ANPs. For this purpose, we have examined ten different state-of-the-art saliency detection methods with respect to the ground-truths, which are human gaze pattern themselves over ANPs. We found very interesting and useful results that the Graph-Based Visual Saliency (GBVS) method can better estimate the human-gaze heatmaps over ANPs that are very close to human gaze pattern.

The recent developments in the eye tracking technology lead to new insights in how humans read, yet little is known about how the layout affects the comprehension. In this study, the differences in the understandability and the reading behaviour of two different page orientations (portrait and landscape) of a scientific poster are investigated. An eye tracking experiment was designed to find out whether the participants focus more on different areas in different orientations and whether the orientation has any effect on the reading behaviour or the overall comprehension of the poster. The participants' gazes were recorded and mapped onto the document using homographies. The saccade and transitional analysis over 30 participants concludes that the portrait orientation is better for remembering specific details while the landscape orientation supplements a high level understanding.

In this study, we started to investigate the impact of different layouts in scientific papers using eye tracking technology. At this stage, we limit our study to the comparison between layout formats inside the Computer Science Community. Association for Computing Machinery (ACM) proceeding as the double-column format and Springer Lecture Notes in Computer Science (LNCS) as the single-column format have been selected for investigation and will be presented in this paper. We employed a wearable eye tracker instead of a remote desk-mounted eye tracker. Due to their mobility and flexibility, this technology has been selected to simulate real-world environment while reading printed documents. Data acquired by a wearable head-mounted eye trackers is based on the gaze position with respect to the video recorded by the embedded camera. Hence, the coordinate of the gaze must be mapped to the corresponding document so that it enables us to investigate eye tracking data analysis techniques. In order to perform this task, we adopted a robust document retrieval technique called Locally Likely Arrangement Hashing (LLAH) to our data. Briefly, the scenario of the process is as follows: First, participants read the print-out scientific papers with the eye tracker. Then, gaze data maps to the corresponding original document in our retrieval database. Finally, the gaze analysis system extracts the intended information for statistical evaluation. Our findings show subjects are more fluent and faster in the double-column proceeding format as compared to the single-column.

Minimally invasive catheter-mediated (MIC) interventions represent a key approach to treat patients with a wide range of cardiovascular diseases; the operators' performance rely on his or her ability to read the dynamic (cine, fluoroscopy) and static x-rays images rapidly, and accurately. Here, we demonstrate the feasibility of expertise transfer employing a low cost eye tracking system for experts gaze visualization in real-life (MIC) interventional scenario. As the video quality from head-mounted eye tracker is not sufficient for data analysis, due to head-movement, dark shades, blurring, etc., therefore we have developed an automatic method for mapping the recorded gaze from the eye-tracker video to high quality x-ray video, allowing for tracking of the complete visual perception of individual operators throughout the life performance of individual interventions based on high resolution image recordings. The high quality gaze video from an expert doctors provide an important educational resource to teach novices how to read the dynamic x-ray images.

In this paper, we have conducted an eye tracking experiment by employing an inexpensive, lightweight, and portable eye tracker paired with a tablet. Students were instructed to solve the physics problems by presenting them three coherent representations about a phenomenon: Vectorial representations, data tables and diagrams. The effectiveness of each representation was assessed for three levels of student expertise (experts, intermediates and novices) using entropy-based transition analysis of the gaze data. The results show that students of different skill level (a) prefer different representations for problem-solving, (b) switch between representations with different frequencies, and (c) can be distinguished by the density of representation use. The obtained results confirm earlier findings of physics education research quantitatively which were initially obtained by student interviews and observational studies.

In this paper, we have conducted an eye tracking experiment by employing an inexpensive, lightweight, and portable eye tracker paired with a tablet. Students were instructed to solve the physics problems by presenting them three coherent representations about a phenomenon: Vectorial representations, data tables and diagrams. The effectiveness of each representation was assessed for three levels of student expertise (experts, intermediates and novices) using eye-tracking gaze data. The results show that students of different skill level (a) prefer different representations for problem-solving, (b) switch between representations with different frequencies, and (c) can be distinguished by the density of representation use. The obtained results confirm earlier findings of physics education research quantitatively which were initially obtained by student interviews and observational studies.

Research on reading has been started with the famous publication of Edmund Burke Huey’s (1908): The Psychology and Pedagogy of Reading [1]. In the last decades, computing power increased dramatically. This evolvement paved a new path for the researchers to elevate reading research especially through the medium of eye tracking technologies. This made text very special in the domain of eye tracking [2]. In this paper, we introduce the eyeReading system which facilitates research in Psychology of Reading and provides a framework for gaze based Human-Text Interaction.

This paper asks the questions: what makes a beautiful landscape beautiful, what makes a damaged building look damaged? It tackles the challenge to understand which regions of Adjective Noun Pairs (ANP) images attract attention when observed by a human subject. We employ eye-tracking techniques to record the gaze over a set of multiple ANPs images and derive regions of interests for these ANPs. Our contribution is to study eye fixation pattern in the context of ANPs and their characteristics between being objective or subjective on the one hand and holistic vs. localizable on the other hand. Our finding indicate that subjects who differ in their assessment of ANP labels also have different eye fixation pattern. Further, we provide insights about ANP attention during implicit and explicit ANP assessment.

We instructed a group of 20 undergraduate physics students with an instruction to visually interpret divergence of vector field with integral and differential strategies. We designed two distinct sets of 10 tasks and recorded the students’ eye gaze while they completed the task. In this study, we first developed Attentive Region Clustering (ARC), a novel unsupervised approach to analyze and evaluate the fixations and saccadic movements of the participants. Secondly, a linear Support Vector Machine model was used to classify the two problem-solving strategies in the vector field domain. The results revealed the implication of vector flow orientation in the eye movement patterns. We achieved an accuracy 10- fold cross-validation, we achieved 81.2 % ( 11 % ) accuracy by evaluating a linear Support Vector Machine model to classify which strategy was applied by the student to comprehend the divergence of a vector field problem. The outcome of this work is useful to monitor the student visual performance on similar tasks. Besides, advances in Human-Computer Interaction empower students by getting objective feedback on their progress by visual clues in a vector field problems.