2004 Fiscal Year Final Research Report Summary
On the Rubrics of Japanese High School Entrance Examinations for JSL Students
| Project/Area Number |
14580332
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Japanese language education
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| Research Institution | OKAYAMA UNIVERSITY |
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Principal Investigator |
MITSUMOTO Tomie OKAYAMA UNIVERSITY, FACULTY OF EDUCATION, ASSOCIATE PROFESSOR, 教育学部, 助教授 (80243450)
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| Project Period (FY) |
2002 – 2004
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| Keywords | foreign student / high school entrance examination / English, science, and math testing / rubric / morphological analysis / common questions of entrance examinations / Sandglass-Type Neural Network / frequently-used words / expressions in entrance exams |
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| Research Abstract |
As the number of foreign students dramatically increases due to temporal or permanent immigration of foreign residents in Japan, there rises a critical problem that the new comers have serious difficulty in taking high school entrance examinations (hereafter HSEE) : while the percentage of Japanese students who can pass the HSEE reaches 97%, that of foreign students remains around 53%. In such a situation, it should be necessary to take proper measures not only in the field of testing administration, but also in teaching practice.
This project, for the students above, has tried to grasp core words/expressions and characteristics of rubrics in HSEE in the following three subjects : English, science, and math. Samples of directions in these subjects were extracted from all Japanese prefectural high school exams for 2002.
As for the portion of English testing, we yielded key words/expressions through both qualitative and quantitative analysis. The latter was, for the purpose of enhancing the former results, conducted by the use of computer software, "Chasen". Turning to the cases of science and math testing, the frequently-used words/expressions in entrance exams were analyzed with the basis of "Sandglass-Type Neural Network." The future research direction is to further discuss how these key tokens/expressions can be best utilized for foreign students.
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