Robert G. M. Hausmann

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The goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to(More)
Self-explaining has been repeatedly shown to result in positive learning outcomes for students in a wide variety of disciplines. However, there are two potential accounts for why self-explaining works. First, those who self-explain experience more content than those who do not. Second, there are differences in the activity of generating the explanations(More)
Many intelligent tutoring systems (ITSs) offer feedback and guidance through structured dialogs with their students, which often take the form of a sequence of hints. However, it is often difficult to replicate the complexity and responsiveness of human conversation with current natural language understanding and production technologies. Although ITSs(More)
The Pittsburgh Science of Learning Center (PSLC) is developing a data storage and analysis facility, called DataShop. It currently handles log data from 6 full-year tutoring systems and dozens of smaller, experimental tutoring systems. DataShop requires a representation of log data that supports a variety of tutoring systems, atheoretical analyses and(More)
Learning outcomes from intelligent tutoring systems (ITSs) tend to be quite strong, usually in the neighborhood of one standard deviation. However , most ITS designers use the learning outcomes from expert human tutoring as the gold standard (i.e., two standard deviations). What can be done, with the current state of the art, to increase learning from an(More)
It is becoming a standard technique to use learning curves as part of evaluation of intelligent tutoring systems [1,2,3], but such learning curves require a method for attributing errors. That is, the method must determine for each error a student makes what " knowledge component " in the student model is to blame. To this point, alternative methods for(More)
Self-explaining is a domain-independent learning strategy that generally leads to a robust understanding of the domain material. However, there are two potential explanations for its effectiveness. First, self-explanation generates additional content that does not exist in the instructional materials. Second, when compared to comprehension, generation of(More)
Self-explaining is a beneficial learning strategy for studying worked-out examples because it either supplies missing information through the generation of inferences or because it provides a mechanism for repairing flawed mental models. Although self-explanation is generated with the purpose of helping the individual, is it also helpful to produce(More)