Maria Ofelia San Pedro

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The worldwide increase in demand for qualified workers in science, technology, engineering, and mathematics (STEM) fields has resulted in a greater focus on preparing students to enroll in postsecondary STEM programs. The processes that lead students to become interested in and equip them for STEM careers begin years earlier. Previous research indicates(More)
In association rule mining, interestingness refers to metrics that are applied to select association rules, beyond support and confidence. For example, Merceron & Yacef (2008) recommend that researchers use a combination of lift and cosine to select association rules, after first filtering out rules with low support and confidence. However, the empirical(More)
There is increasing evidence that fine-grained aspects of student performance and interaction within educational software are predictive of long-term learning. Machine learning models have been used to provide assessments of affect, behavior, and cognition based on analyses of system log data, estimating the probability of a student’s particular affective(More)
This dissertation research focuses on assessing student behavior, academic emotions, and knowledge from a middle school online learning environment, and analyzing their potential effects on decisions about going to college. Using students’ longitudinal data ranging from their middle school, to high school, to postsecondary years, I leverage quantitative(More)
This study investigates how we can effectively predict what type of game a user will choose within the game-based environment iSTART-2. Seventy-seven college students interacted freely with the system for approximately 2 hours. Two models (a baseline and a full model) are compared that include as features the type of games played, previous game achievements(More)
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