DeepAI AI Chat
Log In Sign Up

Automatic Summarization of Student Course Feedback

by   Wencan Luo, et al.
University of Central Florida
University of Pittsburgh

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.


page 1

page 2

page 3

page 4


An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses

Teaching large classes remains a great challenge, primarily because it i...

Planning Courses for Student Success at the American College of Greece

We model the problem of optimizing the schedule of courses a student at ...

Mining Student Responses to Infer Student Satisfaction Predictors

The identification and analysis of student satisfaction is a challenging...

Machine Learning-powered Course Allocation

We introduce a machine learning-powered course allocation mechanism. Con...

ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback

High-quality computer science education is limited by the difficulty of ...

Examples and Results from a BSc-level Course on Domain Specific Languages of Mathematics

At the workshop on Trends in Functional Programming in Education (TFPIE)...

A random walk through experimental mathematics

We describe our adventures in creating a new first-year course in Experi...