Erica L. Snow

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This study builds upon previous work aimed at developing a student model of reading comprehension ability within the intelligent tutoring system, iSTART. Currently, the system evaluates students' self-explanation performance using a local, sentence-level algorithm and does not adapt content based on reading ability. The current study leverages natural(More)
The purpose of this study was to investigate students' patterns of interactions within a game-based intelligent tutoring system (ITS), and how those interactions varied as a function of individual differences. The analysis presented in this paper comprises a subset (n=40) of a larger study that included 124 high school students. Participants in the current(More)
This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new approaches that measure student attributes such as demographic information, standardized test scores, and survey results. The results demonstrate that combining both text features and student(More)
This study expands upon an existing model of students' reading comprehension ability within an intelligent tutoring system. The current system evaluates students' natural language input using a local student model. We examine the potential to expand this model by assessing the linguistic features of self-explanations aggregated across entire passages. We(More)
We investigate how writing proficiency relates to the flexible use of cohesion. Forty-five students wrote 16 essays across 8 sessions. Natural language processing techniques were used to calculate the cohesion of each essay. Random walk and Euclidian distance measures were then used to visualize and classify students' flexibility in cohesion across the(More)
The authors use dynamical analyses to investigate the relation between students' patterns of interactions with various types of game-based features and their daily performance. High school students (n=40) interacted with a game-based intelligent tutoring system across eight sessions. Hurst exponents were calculated based on students' choice of interactions(More)
Research on individual differences indicates that students vary in how they interact with and perform while using intelligent tutoring systems (ITSs). However, less research has investigated how individual differences affect students' interactions with game-based features. This study examines how learning outcomes and interactions with specific game-based(More)