We present a dual eye-tracking study that demonstrates the effect of sharing selection among collaborators in a remote pair-programming scenario. Forty pairs of engineering students completed several program understanding tasks while their gaze was synchronously recorded. The coupling of the programmers' focus of attention was measured by a cross-recurrence analysis of gaze that captures how much programmers look at the same sequence of spots within a short time span. A high level of gaze cross-recurrence is typical for pairs who actively engage in grounding efforts to build and maintain shared understanding. As part of their grounding efforts, programmers may use text selection to perform collaborative references. Broadcast selections serve as indexing sites for the selector as they attract non-selector's gaze shortly after they become visible. Gaze cross-recurrence is highest when selectors accompany their selections with speech to produce a multimodal reference.

This paper exploits the personal aspects of an individual's eye-movements in dynamic Tetris-playing environments. Effective features representing the players' eye-moving characteristics are extracted, and they are shown to be different across difference players. Delta features are also calculated to present the dynamic changes of the static features. A series of personal identification experiments are performed by using a hidden Markov models (HMM). Our experimental results show that compared with local information, modeling and tracking the dynamic temporal information (i.e., delta features) is of more importance in distinguishing different players' eye-movement. Given a 10-zoid consecutive playing signals (about 30 seconds) we can achieve an identification rate of 82.1% by combining them both.

In this paper, we analyze complex gaze tracking data in a collaborative task and apply machine learning models to automatically predict skill-level differences between participants. Specifically, we present findings that address the two primary challenges for this prediction task: (1) extracting meaningful features from the gaze information, and (2) casting the prediction task as a machine learning (ML) problem. The results show that our approach based on profile hidden Markov models are up to 96% accurate and can make the determination as fast as one minute into the collaboration, with only 5% of gaze observations registered. We also provide a qualitative analysis of gaze patterns that reveal the relative expertise level of the paired users in a collaborative learning user study.

This paper presents an algorithm that detects misunderstandings in collaborative work at a distance. It analyses the movements of collaborators' eyes on the shared workspace, their utterances containing references about this workspace, and the availability of 'remote' deictic gestures. This method is based on two findings: 1. participants look at the points they are talking about in their message; 2. their gazes are more dense around these points compared to other random looks in the same timeframe. The algorithm associates the distance between the gazes of the emitter and gazes of the receiver of a message with the probability that the recipient did not understand the message.