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Traditional studies of intelligent tutoring systems have focused on their use in the classroom. Few have explored the advantage of using ITS as a web-based homework (WBH) system, providing correctness-only feedback to students. A second underappreciated aspect of WBH is that teachers can use the data to more efficiently review homework. Universities across(More)
Many intelligent tutoring system (ITS) researchers are looking at ways to detect and to respond to student emotional states (for instance animated pedagogical agents that mirror student emotion). Such interventions are complicated to build, and do not take advantage of the potential for teachers to be part of the process. We present two studies that(More)
Much of the literature surrounding the effectiveness of intelligent tutoring systems has focused on the type of feedback students receive. Current research suggests that the timing of feedback also plays a role in improved learning. Some researchers have shown that delaying feedback might lead to a " desirable difficulty " , where students' performance(More)
Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students – explanations, feedback, and other pedagogical interactions. Considering the context of(More)
Knowledge tracing (KT) is well known for its ability to predict student knowledge. However, some intelligent tutoring systems use a threshold of consecutive correct responses (N-CCR) to determine student mastery, and therefore individualize the amount of practice provided to students. The present work uses a data set provided by ASSISTments, an intelligent(More)
This study suggests that the data generated by intelligent tutoring systems can be used to accurately predict end-of-year standardized state test scores. A traditional model including only past performance on the test yielded an R 2 of 0.38 and an enhanced traditional model that added current class average improved predictions (R 2 =0.50). These models(More)
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