Automated Analysis of Student Verbalizations in Online Learning Environments

We present results in automating the analysis of student verbalizations in online learning environments, using an existing online tool designed to teach students to reason analytically about code as an example. The new extension captures "think-aloud'' data as students work through code reasoning activities. The data is recorded and transcribed automatically and used as input to a natural language processing / machine learning system designed to identify specific student attitudes (e.g., uncertain), behaviors (e.g., guessing), and difficulties (e.g., concept misunderstandings). We present the design and implementation of the tool, an analysis of its transcription accuracy, and an evaluation of its utility in identifying characteristics of student learning.