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Abstract:"Prime Climb is an educational game that provides individual support for learning number factorization skills 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 attention to the hints, even when they are justified (i.e. based on a student model's assessment). In this thesis we will discuss the test-bed game, Prime Climb, and our re-implementation of the game which allowed us to modify the game dynamically and will allow for more rapid prototyping in the future. To assist students as they play the game, Prime Climb includes a pedagogical agent which provides individualized support by providing user-adaptive hints. We then move into our work with the eye-tracker to better understand if and how students process the agent's personalized hints. We will conclude with a user study in which we use eyetracking data to capture user attention patterns as impacted by factors related to existing user knowledge, hint types, and attitude towards getting help in general. We plan to leverage these results in the future to make hint delivery more effective."

"In this paper, we will describe work that we have done in this direction using as a test-bed an edu-game for number factorization, Prime Climb. This game includes a pedagogical agent that provides adaptive interventions during game playing based on a model of student learning [13, 18]. Here we focus on how we re-implemented the original Prime Climb game into a framework that enables rapid prototyping and testing of different design hypothesis. We also discuss preliminary work on using eye tracking data on user attention patterns to better understand if and how students process the agent‟s adaptive interventions." (from the introduction)