This paper presents a user study that investigates the factors affecting student attention to user-adaptive hints during interaction with an educational computer game. The study focuses on Prime Climb, an educational game designed to provide individualized support for learning number factorization skills in the form of textual hints based on a model of student learning. We use eye-tracking data to capture user attention patterns on the game adaptive-hints and present results on how user performance, hint timing, and attitude toward getting help all affect the degree to which students attend to hints. We also show that improved attention to hints is a worthwhile goal, at least in Prime Climb, because when they are attended to hints can positively affect a student's performance with the game.

Abstract. We present a user study to investigate which factors affect student attention to user-adaptive hints during interaction with an educational computer game. The game is Prime Climb, an educational game designed to provide individualized support for learning number factorization skills in the form of hints based on a model of student learning. We use eye-tracking data to capture user attention patterns on the game adaptive-hints, and present results on how these patterns are impacted by factors related to existing user knowledge, hint timing, and attitude toward getting help in general. We plan to leverage these results in the future for making hint delivery adaptive to these factors. Keywords: Adaptive help, educational games, pedagogical agents, eye-tracking IntroductionThe ability of providing interventions that are adaptive to each student's specific needs is one of the distinguishing features of intelligent tutoring systems (ITS). One of the most widespread forms of adaptive interventions is to provide hints designed to gradually help students through specific educational activities when they have difficulties proceeding on their own [14]. Despite the wide adoption of adaptive hints, there is an increasing body of research showing their possible limitations, going from students gaming the system, i.e., using the hints to get quick answers from the ITS [see 7 for an overview], to help avoidance, i.e. students not using hints altogether [e.g., 8, 9]. In this paper, we are interested in investigating the latter issue. More specifically, we seek to gain a better understanding of which factors may affect a student's tendency to attend to adaptive hints that the student has not explicitly elicited. This research has three main contributions to the ITS field. First, while previous work on help avoidance focused on capturing and responding to a student's tendency to avoid requesting hints [e.g., 8, 9], here we investigate how students react when the hints are provided unsolicited. A second contribution is that we look at attention to adaptive hints during interaction with an educational computer game (edu-game), whereas most previous work on student usage (or misusage) of hints has been in the context of more structured problem solving activities. Providing adaptive hints to support learning during game play is especially challenging because it requires a trade-off between fostering learning and maintaining engagement. We see the results

Abstract. Prime Climb is an educational game that provides individualized support for learning number factorization skills. This support is delivered by a pedagogical agent in the form of hints based on a model of student learning. Previous studies with Prime Climb indicated that students may not always be paying attentions to the hints, even when they are justified. In this paper we discuss preliminary work on using eye tracking data on user attention patterns to better understand if and how students process the agent's personalized hints, with the long term goal of making hint delivery more effective.

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