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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. 

In recent years, researchers have developed technology to analyze human facial expressions and other affective data at very high time resolution. This technology is enabling researchers to develop and study interactive robots that are increasingly sensitive to their human interaction partners’ affective states. However, typical interaction planning models and algorithms operate on timescales that are frequently orders of magnitude larger than the timescales at which real-time affect data is sensed. To bridge this gap between the scales of sensor data collection and interaction modeling, affective data must be aggregated and interpreted over longer timescales. In this paper we clarify and formalize the computational task of affect interpretation in the context of an interactive educational game played by a human and a robot, during which facial expression data is sensed, interpreted, and used to predict the interaction partner’s gameplay behavior. We compare different techniques for affect interpretation, used to generate sets of affective labels for an interactive modeling and inference task, and evaluate how the labels generated by each interpretation technique impact model training and inference. We show that incorporating a simple method of personalization into the affect interpretation process — dynamically calculating and applying a personalized threshold for determining affect feature labels over time — leads to a significant improvement in the quality of inference, comparable to performance gains from other data pre-processing steps such as smoothing data via median filter. We discuss the implications of these findings for future development of affect-aware interactive robots and propose guidelines for the use of affect interpretation methods in interactive scenarios. 

Personalized education technologies capable of delivering adaptive interventions could play an important role in addressing the needs of diverse young learners at a critical time of school readiness. We present an innovative personalized social robot learning companion system that utilizes children’s verbal and nonverbal affective cues to modulate their engagement and maximize their long-term learning gains. We propose an affective reinforcement learning approach to train a personalized policy for each student during an educational activity where a child and a robot tell stories to each other. Using the personalized policy, the robot selects stories that are optimized for each child’s engagement and linguistic skill progression. We recruited 67 bilingual and English language learners between the ages of 4–6 years old to participate in a between-subjects study to evaluate our system. Over a three-month deployment in schools, a unique storytelling policy was trained to deliver a personalized story curriculum for each child in the Personalized group. We compared their engagement and learning outcomes to a Non-personalized group with a fixed curriculum robot, and a baseline group that had no robot intervention. In the Personalization condition, our results show that the affective policy successfully personalized to each child to boost their engagement and outcomes with respect to learning and retaining more target words as well as using more target syntax structures as compared to children in the other groups.