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  1. Assessing Mathematics Misunderstandings via Bayesian Inverse Planning.Anna N. Rafferty, Rachel A. Jansen & Thomas L. Griffiths - 2020 - Cognitive Science 44 (10):e12900.
    Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how to solve the equation and the ways in which they might answer incorrectly offers the opportunity to obtain a more nuanced perspective of their algebra skills. To automatically make sense of step‐by‐step solutions, we (...)
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  • Observing Tutorial Dialogues Collaboratively: Insights About Human Tutoring Effectiveness From Vicarious Learning.Michelene T. H. Chi, Marguerite Roy & Robert G. M. Hausmann - 2008 - Cognitive Science 32 (2):301-341.
    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 four other instructional methods—one‐on‐one human tutoring, observing tutoring individually, collaborating without observing, and studying alone—the results showed that students learned to solve physics problems just as effectively from observing tutoring (...)
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  • Remedial Tutoring with Intelligent Tutoring Systems - The Case of INTUITION.J. Siemer & M. C. Angelides - 1996 - Journal of Intelligent Systems 6 (3-4):279-308.
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  • Skill Acquisition and the LISP Tutor.John R. Anderson, Frederick G. Conrad & Albert T. Corbett - 1989 - Cognitive Science 13 (4):467-505.
    An analysis of student learning with the LISP tutor indicates that while LISP is complex, learning it is simple. The key to factoring out the complexity of LISP is to monitor the learning of the 500 productions in the LISP tutor which describe the programming skill. The learning of these productions follows the power‐law learning curve typical of skill acquisition. There is transfer from other programming experience to the extent that this programming experience involves the same productions. Subjects appear to (...)
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