Topic-Level Feedback Summarization for an Explanation-Based Classroom Response System

Fostering engagement among undergraduate computer science students in large-lecture settings can be challenging for instructors. Didactic teaching styles common in such lectures may not be as effective as dialogic teaching, but the overhead involved with dialogic teaching may preclude its use in large introductory computer science courses. Classroom response systems such as multiple-choice ''clicker'' systems provide a way for students to engage with an instructor, but evidence suggests that multiple-choice questions may not foster deep thought in the way that open-ended questions do. Open-ended questions foster deeper engagement and AI-enabled learning analytics offer a powerful method of automatically assessing student responses, but grading text responses produced by students and summarizing class-wide performance during lectures presents unique difficulties, especially for algorithmic questions prevalent in computer science lectures.