Modeling Language Proficiency Using Implicit Feedback.

We describe the results of several experiments with interactive interfaces for native and L2 English students, designed to collect implicit feedback from students as they complete a reading activity. In this study, implicit means that all data is obtained without asking the user for feedback. To test the value of implicit feedback for assessing student proficiency, we collect features of user behavior and interaction, which are then used to train classification models. Based upon the feedback collected during these experiments, a student's performance on a quiz and proficiency relative to other students can be accurately predicted, which is a step on the path to our goal of providing automatic feedback and unintrusive evaluation in interactive learning environments.