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We investigate the prevalence and learning impact of different types of off-task behavior in classrooms where students are using intelligent tutoring software. We find that within the classrooms studied, no other type of off-task behavior is associated nearly so strongly with reduced learning as "gaming the system": behavior aimed at obtaining correct(More)
It has been found in recent years that many students who use intelligent tutoring systems game the system, attempting to succeed in the educational environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly. In this paper, we introduce a system which gives a gaming student(More)
Intelligent tutoring systems that utilize Bayesian Knowledge Tracing have achieved the ability to accurately predict student performance not only within the intelligent tutoring system, but on paper post-tests outside of the system. Recent work has suggested that contextual estimation of student guessing and slipping leads to better prediction within the(More)
Human observations and classifications have shown to provide substantial leverage for developing models of students' motivation, attitudes, and strategic choices, as a student interacts with an intelligent tutoring system. However, human observation and classification is highly time-consuming, which has limited its use. We present a technique for conducting(More)
In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and improve learning. In addition, accurate detection of student affect could play an essential role in research(More)
Cognitive Tutors are proven effective learning environments, but are still not as effective as one-on-one human tutoring. We describe an environment (ALPS) designed to engage students in question-asking during problem solving. ALPS integrates Cognitive Tutors with Synthetic Interview (SI) technology, allowing students to type free-form questions and receive(More)
We present an analysis of the affect that precedes, follows, and co-occurs with students' choices to go off-task or engage in on-task conversation within two versions of a virtual laboratory for chemistry. This analysis is conducted using field observation data collected within undergraduate classes using the virtual laboratory software as part of their(More)
Among the most important tasks of the teacher in a classroom using the Reasoning Mind blended learning system is proactive remediation: dynamically planned interventions conducted by the teacher with one or more students. While there are several examples of detectors of student behavior within an online learning environment, most have focused on behaviors(More)
This study examines the impact of integrating worked examples into a Cognitive Tutor for genetics problem solving, and whether a genetics process modeling task can help prepare students for explaining worked examples and solving problems. Students participated in one of four conditions in which they engaged in either: (1) process modeling followed by(More)