| Project/Area Number |
60580244
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
科学教育(含教育工学)
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| Research Institution | Faculty of Education, Oita University |
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Principal Investigator |
MURAI Moriyasu Professor, Faculty of Education, Oita university, 教育学部, 助教授 (70040730)
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| Co-Investigator(Kenkyū-buntansha) |
YANAI Tomohiko Assistant Professor, Faculty of Education, Oita university, 教育学部, 助教授 (60136025)
SAKAKIBARA Noriko Lecturer, Faculty of Education, Oita university, 教育学部, 講師 (90141473)
YAMAKI Asahiko Lecturer, Faculty of Education, Oita university, 教育学部, 講師 (20158083)
TOMITA Reishi Assistant Professor, Faculty of Education, Oita university, 教育学部, 助教授 (60145349)
MATSUMOTO Tadashi Lecturer, Faculty of Education, Oita university, 教育学部, 講師 (50145348)
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| Project Period (FY) |
1985 – 1986
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| Project Status |
Completed (Fiscal Year 1986)
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| Budget Amount *help |
¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 1986: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1985: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | CAI / Course Ware / Factot Analysis / Interpretive Structural Modeling / 教材研究 |
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| Research Abstract |
The method of course decision, presently adopted at CAI for practical use, lacks flexibility regardless of either linear types (Skinner) or branching types (Crouder). Even in branching types, the learner simply follows several branchings decided and presented by the programmer. This means that in CAI with fixed branchings nothing more expected than the programmer'steaching ability of the material. What is worse, as long as the flexibility of course decision is limited, it is basically impossible to prepare a system coping with various individual differences because the size of softwares cannot be limitless. For these reasons, studies of intelligent CAI have been making great progress. These studies, however, are seen successful under the condition that the answers to sub-procedures are quite clear. But in reality, they are not clarified in many cases. Our study took a different approach. We assumed that a proper learning course for an individual is decided phenomenologically, by analysing the group dataand individual differences.
We came to the conclusion that multivariate analyses were effective in many attempts to obtain significant information, by placing an individual's data in the group data. We found that in such affective domains as apprehending pictures and factor analyis, these were directly applicable. In the case of intellectual learning of new materials, the leaners' data itself has little deviation. Thus, learning courses should be decided in terms of interaction between the logical structures of the material assumed by the teacher, and the individual learner sassumption about the material placed in the group data. Finally, after much discussion, we obtained information for making algorisms of course decision for individual learners.
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