Robert G. M. Hausmann

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Human one-to-one tutoring has been shown to be a very effective form of instruction. Three contrasting hypotheses, a tutor-centered one, a student-centered one, and an interactive one could all potentially explain the effectiveness of tutoring. To test these hypotheses, analyses focused not only on the effectiveness of the tutors’ moves, but also on the(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)
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)
It has been well established that collaborative learning is more effective in producing learning gains than individuals working alone. The present study investigates three potential mechanisms responsible for learning from collaborative problem solving: other-directed explaining, co-construction, and self-directed explaining. College undergraduates were(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 theoretical stance explicated in this chapter assumes that scientific discoveries often require that the problem solver (either the scientist or the inventor) re-conceptualizes the problem in a way that crosses ontological categories. Examples of the highest level of ontological categories are entities, processes, and mental states. Discoveries might be(More)
Cognitive science principles should have implications for the design of effective learning environments. The self-explanation principle was chosen for the current work because it has developed significantly over the last 20 years. Early formulations hypothesized that self-explanation facilitated inference generation to supply missing information about a(More)
Previous research has demonstrated a correlation between learning and elaborative dialogs. To better understand that relationship, the present experiment contrasted performance in three conditions: individuals, a control condition that did not receive communication training, and an elaboration condition. After a pretest, participants were asked to solve a(More)
Cognitive science principles should have implications for the design of effective learning environments. The selfexplanation principle was chosen for the current project because it has developed significantly over the past few years. Early formulations suggested that self-explanation facilitated inference generation to supply missing information about a(More)