Automatic Summarization of Student Course Feedback
This addresses the costly manual analysis of student feedback for instructors, though it appears incremental as it builds on existing summarization methods.
The paper tackles the problem of summarizing student course feedback by proposing an integer linear programming (ILP) framework, which outperforms baselines in ROUGE scores and human evaluations.
Student course feedback is generated daily in both classrooms and online course discussion forums. Traditionally, instructors manually analyze these responses in a costly manner. In this work, we propose a new approach to summarizing student course feedback based on the integer linear programming (ILP) framework. Our approach allows different student responses to share co-occurrence statistics and alleviates sparsity issues. Experimental results on a student feedback corpus show that our approach outperforms a range of baselines in terms of both ROUGE scores and human evaluation.