| Co-Investigator(Kenkyū-buntansha) |
NAKAMURA Tomoyasu Tokyo Institute of Technology, Faculty of Engineering, Instructor, 工学部, 助手 (30251614)
MATSUDA Toshiki Tokyo Institute of Technology, Faculty of Engineering, Associate Professor, 工学部, 助教授 (60173845)
ICHIKAWA Masanori Tokyo University of Foreign Studies, Faculty of Foreign Studies, Associate Profe, 外国語学部, 助教授 (20168313)
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| Research Abstract |
1) Estimatin of Competence and Confidence level Based on correct/wrong data as wel as confident/non-confident data, we deveoped the new IRT method which describes learners' positin in two dimensions, namely, true competence and confidence level.
2) Estimation of Knowledge State Combining traditinal IRT method and latent class model, we developed the method which classifies each learner into one of the thre casses, namely, "Compete Knowledge", "Partial Knowledge", and "complete Ignorance".
3) Estimationof posterior Distribution for Competence Parameter Using Gibbs Sampler, we developed a new method which derives the marginal posterior distribution for the competence parameters. When this distribution is available, we can make use of the formal decision theory to determine the next instruction step.
4) Baysian Network Representation of Leamers Based on the network model of problem solving process, we developed the method, which identifies each learner's state in this network, using the Baysian approach.
These methods provide useful information to design the ptimal sequence of instruction for eachlearner. Our goal is to construct CAI system which includes the adove mentioned methods to identify each learner's trace.
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