Abstract.A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learning environments and traditional learning systems. This view suggests that, on the one hand, students interacting with a game-based learning environment may be engaged but unlikely to learn, while on the other hand, traditional learning technologies may promote deep learning but provide limited engagement. This paper presents findings from a study with human participants that challenges the view that engagement and learning need be opposed. A study was conducted with 153 middle school students interacting with a narrative-centered learning environment. Rather than finding an oppositional relationship between learning and engagement, the study found a strong positive relationship between learning outcomes and increased engagement. Furthermore, the relationship between learning outcomes and engagement held even when controlling for students' background knowledge and game-playing experience.

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Self-efficacy is an individual's belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student's level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naïve Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empiri-123 82 S. W. McQuiggan et al. cal study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.

Targeted as a highly desired skill for contemporary work and life, problem solving is central to game-based learning research. In this study, middle grade students achieved significant learning gains from gameplay interactions that required solving a science mystery based on microbiology content. Student trace data results indicated that effective exploration and navigation of the hypothesis space within a science problem-solving task was predictive of student science content learning and in-game performance. Students who selected a higher proportion of appropriate hypotheses demonstrated greater learning gains and completed more in-game goals. Students providing correct explanations for hypothesis selection completed more in-game goals; however, providing the correct explanation for hypothesis selection did not account for greater learning gains. From the analysis, we concluded that hypothesis testing strategies play a central role in game-based learning environments that involve problem-solving tasks, thereby demonstrating strong connections to science content learning and in-game performance.

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