Implicit Measures of Lostness and Success in Web Navigation

In two studies, we investigated the ability of a variety of structural and temporal measures computed from a web navigation path to predict lostness and task success. The user’s task was to find requested target information on specified websites. The web navigation measures were based on counts of visits to web pages and other statistical properties of the web usage graph (such as compactness, stratum, and similarity to the optimal path). Subjective lostness was best predicted by similarity to the optimal path and time on task. The best overall predictor of success on individual tasks was similarity to the optimal path, but other predictors were sometimes superior depending on the particular web navigation task. These measures can be used to diagnose user navigational problems and to help identify problems in website design

Tag trails: Navigating with context and history

We describe a technique for preserving and presenting context and history while navigating web resources described by keywords. We use tagging and tag clouds as an application area for our technique. The technique is illustrated by employing it in a prototype that interfaces data from a social tagging website used to bookmark academic articles. The prototype displays a “tag trail” which can reveal contextual connections between web resources and the associated tags. We argue that the user’s understanding of web resources is aided by making such connections explicit

Visualizing Search Sequences

ABSTRACT This video presents a novel visualization technique of interactive search sessions. The objective of this work is to enable characterization and comparison of interactive search sessions with respect to strategies and tactics employed by different people and on different search tasks. The visual aspect of this approach aims to off-load cognition by shifting part of the required processing to perception enabling thus information science researchers to obtain quick overview of search sessions. The video explains the technique and its applications on examples created from data collected in a controlled Web search study

Relating Eye-Tracking Measures With Changes In Knowledge on Search Tasks

We conducted an eye-tracking study where 30 participants performed searches on the web. We measured their topical knowledge before and after each task. Their eye-fixations were labelled as "reading" or "scanning". The series of reading fixations in a line, called "reading-sequences" were characterized by their length in pixels, fixation duration, and the number of fixations making up the sequence. We hypothesize that differences in knowledge-change of participants are reflected in their eye-tracking measures related to reading. Our results show that the participants with higher change in knowledge differ significantly in terms of their total reading-sequence-length, reading-sequence-duration, and number of reading fixations, when compared to participants with lower knowledge-change.Comment: ACM Symposium on Eye Tracking Research and Applications (ETRA), June 14-17, 2018, Warsaw, Polan

Towards Inferring Web Page Relevance — An Eye-Tracking Study

We present initial results from a project, in which we examined feasibility of inferring web page relevance from eye-tracking data. We conduced a controlled, lab-based Web search experiment, in which participants conducted assigned information search tasks on Wikipedia. We performed analyses of variance as well as employed classification algorithms in order to predict user perceived Web page relevance. Our findings demonstrate that it is feasible to infer document relevance from eye-tracking data on Web pages. The results indicate that eye fixation duration, pupil size and the probability of continuing reading are good predictors of Web page relevance. This work extends results from previous studies of text document search conducted in more constrained environments.ye

Inferring User Knowledge Level from Eye Movement Patterns

The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n=40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n=409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session