Automatic Query Refining Based on Eye-Tracking Feedback

This paper presents a new method named AQueReBET, which automatically refines a query set by an information seeker searching on the web. A revelation of the intention of an information seeker who is running a search can bring a significant improvement to the search process, and to browsing as well. It is practically impossible to acquire such intention by the explicit indication (feedback) due to the fact that web browsing takes place in real time. Therefore the intention must be determined in some other way. We hypothesize that it can be approximated by means of the implicit feedback preferably in the form of data from an eye tracker and mouse. We propose a method which automatically refines a seeker’s search query and thus we can offer documents with higher relevance, decrease the number of query reformulations and increase the seeker’s satisfaction. The query refinement is based on an analysis of gaze data from an eye tracker and also on groupization. In the proposed method, we calculate word-level importance based on term frequency, term uniqueness (tf-idf) and total fixation duration within the subdocument (word's snippet in search results)

Eye-tracking en masse: Group user studies, lab infrastructure, and practices

The costs of eye-tracking technologies steadily decrease. This allows research institutions to obtain multiple eye-tracking devices. Already, several multiple eye-tracker laboratories have been established. Researchers begin to recognize the subfield of group eye-tracking. In comparison to the single-participant eye-tracking, group eye-tracking brings new technical and methodological challenges. Solutions to these challenges are far from being established within the research community. In this paper, we present the Group Studies system, which manages the infrastructure of the group eye-tracking laboratory at the User Experience and Interaction Research Center (UXI) at the Slovak University of Technology in Bratislava. We discuss the functional and architectural characteristics of the system. Furthermore, we illustrate our infrastructure with one of our past studies. With this paper, we also publish the source code and the documentation of our system to be re-used

EMIP: The eye movements in programming dataset

A large dataset that contains the eye movements of N=216 programmers of different experience levels captured during two code comprehension tasks is presented. Data are grouped in terms of programming expertise (from none to high) and other demographic descriptors. Data were collected through an international collaborative effort that involved eleven research teams across eight countries on four continents. The same eye tracking apparatus and software was used for the data collection. The Eye Movements in Programming (EMIP) dataset is freely available for download. The varied metadata in the EMIP dataset provides fertile ground for the analysis of gaze behavior and may be used to make novel insights about code comprehension