Research Output
Generating student feedback from time-series data using Reinforcement Learning
  We describe a statistical Natural LanguageGeneration (NLG) method for summarisa-tion of time-series data in the context offeedback generation for students. In thispaper, we initially present a method forcollecting 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. Weshow a novel way of constructing a rewardfunction for our Reinforcement Learningagent that is informed by the lecturers’method of providing feedback. We eval-uate our system with undergraduate stu-dents by comparing it to three baselinesystems: a rule-based system, lecturer-constructed summaries and a Brute Forcesystem.Our evaluation shows that thefeedback generated by our learning agentis viewed by students to be as good as thefeedback from the lecturers. Our findingssuggest that the learning agent needs totake into account both the student and lec-turers’ preferences.

  • Date:

    31 December 2013

  • Publication Status:

    Published

  • Publisher

    Association for Computational Linguistics

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Gkatzia, D., Hastie, H., Janarthanam, S., & Lemon, O. (2013). Generating student feedback from time-series data using Reinforcement Learning. In Proceedings of the 14th European Workshop on Natural Language Generation. , (115-124)

Authors

Monthly Views:

Available Documents