We describe a statistical Natural Language Generation (NLG) method for summarisation 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 example feedback from lecturers in a datadriven 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 evaluate our system with undergraduate students by comparing it to three baseline systems: a rule-based system, lecturerconstructed summaries and a Brute Force system. Our evaluation shows that the feedback generated by our learning agent is viewed by students to be as good as the feedback from the lecturers. Our findings suggest that the learning agent needs to take into account both the student and lecturers' preferences.
The research of REF relates most closely to the LARC project -they generated reports based on time-series data from student lecture attendance and weekly questionnaires, and used reinforcement learning informed by the lecturers' method of providing feedback to choose the content to be contained in the report.
Generating Student Feedback from Time-Series Data Using Reinforcement Learning
ENLG
Computer Science