Paper: Generating Student Feedback from Time-Series Data Using Reinforcement Learning

ACL ID W13-2115
Title Generating Student Feedback from Time-Series Data Using Reinforcement Learning
Venue European Workshop on Natural Language Generation
Session
Year 2013
Authors

We describe a statistical Natural Language Generation (NLG) method for summarisa- tion of time-series data in the context of feedback generation for students. In this paper, we initially present a method for collecting time-series data from students (e.g. marks, lectures attended) and use ex- ample feedback from lecturers in a data- driven approach to content selection. We show a novel way of constructing a reward function for our Reinforcement Learning agent that is informed by the lecturers? method of providing feedback. We eval- uate our system with undergraduate stu- dents by comparing it to three baseline systems: a rule-based system, lecturer- constructed summaries and a Brute Force system. Our evaluation shows that the feedback generated by our learning agent is viewed by students t...