No abstract

Reciprocal eye contact is a significant part of human interaction, but its role in classroom interaction has remained unexplored, mostly due to methodological issues. A novel approach in educational science, multiple-person mobile gaze-tracking, allows us to gather data on these momentary processes of nonverbal interaction. The current mixedmethod case study investigates the role of teacher-student eye contact in interpersonal classroom interaction using this methodological approach from three mathematics lessons. We combined gaze-tracking data with classroom videos, which we analyzed with continuous coding of teachers' interpersonal behavior. Our results show that teacher communion and agency affect the frequency and durations of teachers and students' gazes at each other. Students tend to gaze their teachers more during high teacher communion and low agency, but qualitative and quantitative differences between the teachers and their classes emerged as well. To conclude, the formation of eye contacts is situational and affected by momentary interpersonal changes as well as the qualities of teacher-student interactions.

We present a computationally light real-time algorithm which automatically detects blinks, saccades, and fixations from electro-oculography (EOG) data and calculates their temporal parameters. The method is probabilistic which allows to consider the uncertainties in the detected events. The method is real-time in the sense that it processes the data sample-by-sample, without a need to process the whole data as a batch. Prior to the actual measurements, a short, unsupervised training period is required. The parameters of the Gaussian likelihoods are learnt using an expectation maximization algorithm. The results show the promise of the method in detecting blinks, saccades, and fixations, with detection rates close to 100 %. The presented method is published as an open source tool.

We constantly move our gaze to gather acute visual information from our environment. Conversely, as originally shown by Yarbus in his seminal work, the elicited gaze patterns hold information over our changing attentional focus while performing a task. Recently, the proliferation of machine learning algorithms has allowed the research community to test the idea of inferring, or even predicting action and intent from gaze behaviour. The on-going miniaturization of gaze tracking technologies toward pervasive wearable solutions allows studying inference also in everyday activities outside research laboratories. This paper scopes the emerging field and reviews studies focusing on the inference of intent and action in naturalistic behaviour. While the task-specific nature of gaze behavior, and the variability in naturalistic setups present challenges, gaze-based inference holds a clear promise for machine-based understanding of human intent and future interactive solutions.