TITLE: 
Towards Transferrable Personalized Student Models in Educational Games

ABSTRACT:
To help facilitate play and learning, game-based educational activities often feature a computational agent as a co-player. Personalizing this agent's behavior to the student player is an active area of research, and prior work has demonstrated the benefits of personalized educational interaction across a variety of domains. A critical research challenge for personalized educational agents is real-time student modeling. Most student models are designed for and trained on only a single task, which limits the variety, flexibility, and efficiency of student player model learning. In this paper we present a research project applying transfer learning methods to student player models over different educational tasks, studying the effects of an algorithmic ``multi-task personalization'' approach on the accuracy and data efficiency of student model learning. We describe a unified robotic game system for studying multi-task personalization over two different educational games, each emphasizing early language and literacy skills such as rhyming and spelling. We present a flexible Gaussian Process-based approach for rapidly learning student models from interactive play in each game, and a method for transferring each game's learned student model to the other via a novel instance-weighting protocol based on task similarity. We present results from a simulation-based investigation of the impact of multi-task personalization, establishing the core viability and benefits of transferrable student models and outlining new questions for future in-person research.

AUTHORS:
Samuel Spaulding, Jocelyn Shen, Hae Won Park, Cynthia Breazeal

NOTE:
Presented in the workshop as part of the ENCORE track. This paper is from AAMAS 2021 conference. The paper can be accessed at the following link: http://www.samspaulding.com/resources/Spaulding_AAMAS21.pdf