  Annotation is an effective reading strategy people often undertake while
interacting with digital text. It involves highlighting pieces of text and
making notes about them. Annotating while reading in a desktop environment is
considered trivial but, in a mobile setting where people read while
hand-holding devices, the task of highlighting and typing notes on a mobile
display is challenging. In this paper, we introduce GAVIN, a gaze-assisted
voice note-taking application, which enables readers to seamlessly take voice
notes on digital documents by implicitly anchoring them to text passages. We
first conducted a contextual enquiry focusing on participants' note-taking
practices on digital documents. Using these findings, we propose a method which
leverages eye-tracking and machine learning techniques to annotate voice notes
with reference text passages. To evaluate our approach, we recruited 32
participants performing voice note-taking. Following, we trained a classifier
on the data collected to predict text passage where participants made voice
notes. Lastly, we employed the classifier to built GAVIN and conducted a user
study to demonstrate the feasibility of the system. This research demonstrates
the feasibility of using gaze as a resource for implicit anchoring of voice
notes, enabling the design of systems that allow users to record voice notes
with minimal effort and high accuracy.
