Measuring Task Learning Curve with Usage Graph Eccentricity Distribution Peaks

Abstract

Interaction logs (or usage data) are abundant in the era of Big Data, but making sense of these data having Human-Computer Interaction (HCI) in mind is becoming a bigger challenge. Interaction Log Analysis involves tackling problems as automatic task identification, modeling task deviation, and computing task learning curve. In this work, we propose a way of measuring task learning curve empirically, based on how task deviations (represented as eccentricity distribution peaks) decrease over time. From the analysis of 427 event-by-event logged sessions (captured under users’ consent) of a technical reference website, this work shows the different types of learning curves obtained through the computation of how deviations decrease over time. The proposed technique supported the identification of 6 different task learning curves in the set of 17 tasks, allowing differentiating tasks easy to perform (e.g., view content and login) from tasks users face more difficulties (e.g., register user and delete content). With such results, HCI specialists can focus on reviewing specific tasks users faced difficulties during real interaction, from large datasets.