Using Behavior Data to Predict User Success in Ontology Class Mapping - An Application of Machine Learning in Interaction Analysis

Ontology visualization has played an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability evaluations of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavior data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment as opposed to direct input that informs the underlying visualization system. Although findings from usability studies have contributed to the refinement of ontology visualizations as a whole, the visualization techniques themselves remain a one-size-fits-all approach where all users are presented with the same visualizations and interactive features. By contrast, this paper investigates how user behavior data may offer real time indications as to how appropriate or effective a given visualization may be for a specific user at a moment in time, which in turn may inform the adaptation of the given visualization to the user on the fly. To this end, we apply established predictive modeling techniques in Machine Learning to predict user success using gaze data and event logs. We present a detailed analysis and demonstrate such predictions can be significantly better than a baseline classifier during visualization usage. These predictions can then be used to drive the adaptations of visual systems in providing ad hoc visualizations on a per user basis, which in turn may increase individual user success and performance.