P. Baffes, R. Mooney, Refinement-based student modeling and automated bug library construction, Journal of Artificial Intelligence in Education 7 (1) (1996) 75--116. Home/Search Document Details and Download
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Topological inference of teleology: Deriving function from.. - Everett (1999) (Correct)
....required as input only the current state of the problem, that is, the completed structure and the assumptions made to that point. Inferences about the function of the student s work would guide the advice presented to the student. In contrast to other work in intelligent tutoring systems, e.g. [2,4,5], we do not require a model of the student, which simplifies the coach considerably. The domain of thermodynamic cycles is of interest for several reasons. First, it concerns designed artifacts of considerable complexity, which provides a forcing function to move us beyond toy problems. In point ....
P. Baffes, R. Mooney, Refinement-based student modeling and automated bug library construction, Journal of Artificial Intelligence in Education 7 (1) (1996) 75--116.
Dynamically Initializing the Student Model in a Web-Based.. - Tsiriga, Virvou (2002) (Correct)
....is automatically updated each time students interact with the systerm According to Sison and Shimura [5] machine learning or machine learning like techniques have so far been used in two areas of student modeling research. First, many systems, such as DEBUGGY [6] ACM [7] THEMIS [8] and ASSERT [9], have used methods inherited from the area of machine learning in order to induct a single, consistent student model from multiple observed student behaviors. Furthermore, machine learning or machine learning like techniques have also been utilized for the purpose of automatically extending or ....
P. Baffes, and R. Mooney, "Refinement-based student modeling and automated bug libra construction," Journal of trtificial Intelligence in Education, vol. 7(1), pp. 75-116, 1996.
Initializing the Student Model using Stereotypes and Machine.. - Tsiriga, Virvou (2002) (Correct)
....procedure is automatically updated each time a student interacts with the system. According to Sison and Shimura [7] machine leaming or machine leaming like techniques have so far been used in two areas of student modeling research. First, many systems, such as DEBUGGY [8] THEMIS [9] and ASSERT [10], have used methods inherited from the area of machine leaming in order to induct a single, consistent student model from multiple observed student behaviors. Furthermore, machine learning or machine leaming like techniques have also been utilized for the purpose of automatically extending or ....
P. Baffes, and R. Mooney, "Refinement-based student modeling and automated bug libra construction," Journal of Artificial Intelligence in Education, vol. 7, pp. 75-116, 1996.
The Application of Machine Learning to Student Modeling.. - Sison, Shimura (1996) (2 citations) (Correct)
....errors. Behavior recognition is more important when dealing with complex behavior (e.g. programs) than when the behavior is, say, a single number. Hence, DEBUGGY (Burton, 1982) and ACM (Langley and Ohlsson, 1984) which deal with numbers in subtraction, and THEMIS (Kono et al. 1994) and ASSERT (Baffes Mooney, 1996), which deal with labeled propositions, perform relatively trivial behavior recognition. The bulk of their work is in the induction of the student model from a set of noncomplex behavior. 3 Definition 3 Behavior characterization is the process of differentiating between the student behavior and ....
....Model Induction in ASSERT Unlike synthetic SM induction, transformational SM induction (PIXIE, SMS1, Hoppe s) has no direct counterpart in inductive ML, although theory revision comes close. For example, using the propositional theory reviser NEITHER (Baffes Mooney, 1993) ASSERT (Baffes Mooney, 1996) induces an SM from the ideal model and a student s answers to a set of multiple choice questions, and it is the SM, rather than the student behavior, that is examined for misconceptions. Table 5 shows the basic procedure of model induction in ASSERT. Like THEMIS, ASSERT deals with behavior in the ....
Baffes P., & Mooney, R. (1996). Refinement-based student modeling and automated bug library construction. To appear in Jl. Artificial Intelligence in Education.
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