• Title/Summary/Keyword: Item pool for criteria

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Development of Conformance Testing Criteria for STEP AP218 (Ship Structure) (선체구조 모델 데이터의 교환 표준에 따른 적합성 시험 기준의 개발)

  • Hwang, Ho-Jin
    • Journal of Ocean Engineering and Technology
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    • v.24 no.2
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    • pp.74-81
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    • 2010
  • Ship STEP is the international standard for the exchange of ship modeling data between heterogeneous systems. It is expected that STEP AP218 can be used for seamless data exchange between various CAD/CAM/CAE systems used in the shipbuilding design process. Although the conformance assessment for standards would maximize the performance and confidence about data exchanges, most research has been directed toward interoperability testing. ISO SC4/TC184 only provides the method for conformance testing, and it can be used with test cases on application protocols. Even though standards have been defined for conformance assessment and testing, there is no organization or association. CAD vendors have focused on interoperability testing for evaluation of the performance of their systems. In this paper, the conformance testing criteria for AP218 have been developed with abstract test cases of ship structures. The requested STEP translator was also reviewed with a developed item pool of testing criteria. The criteria methodology would be a guideline for the development of translators and interfaces. The item pool method of testing criteria for conformance assessment would increase performance and efficiency of data translators for Ship STEP and other standards.

A blueprint for designing and developing the listening and the reading test of National English Ability Test (NEAT): Item-types decision-making model (국가영어능력평가시험(NEAT)의 검사지 구성의 원칙과 절차: 문항 유형 확정 모델)

  • Kim, Yong-Myeong
    • English Language & Literature Teaching
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    • v.16 no.4
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    • pp.153-184
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    • 2010
  • On the bases of the 5 principles and the 4 criteria for designing and developing of the listening and the reading test of National English Ability Test (NEAT), this study presents Item-Types Decision-Making Model as a blueprint for designing and constructing the two tests. It sets up the criteria for validating item types, designs a modular type of test specifications, constructs an item-types bank, and specifies a complementary type of test specifications of the two tests. To gather all these threads up, it constructs Item-Types Decision-Making Model which consists of such components as the item-type pool, the validity criteria and the procedures of testing item types, the item-types bank, the modular and the complementary type test specification. Thus, it shows how the Model works in developing and constructing the two level-differentiated listening and reading tests (the 2nd and the 3rd rank) of NEAT. Finally, it discusses some implications and applications of the Model to the two level-differentiated tests (the A and the B type) of 2014 CSAT (College Scholastic Ability Test) systems, National Assessment of Educational Achievement (NAEA), and classroom testing. In conclusion, Item-Types Decision-Making Model functions as a testing template in an item development system and as a matrix in an item-types bank system.

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An Exploratory Study on ChatGPT's Performance to Answer to Police-related Traffic Laws: Using the Driver's License Test and the Road Traffic Accident Appraiser (ChatGPT의 경찰 관련 교통법규 응답 능력에 대한 탐색적 연구 - 운전면허 학과시험과 도로교통사고감정사 1차 시험을 대상으로 -)

  • Sang-yub Lee
    • Journal of Digital Policy
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    • v.2 no.4
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    • pp.1-10
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    • 2023
  • This study conducted preliminary study to identify effective ways to use ChatGPT in traffic policing by analyzing ChatGPT's responses to the driver's license test and the road traffic accident appraiser test. I collected ChatGPT responses for the driver's license test item pool and the road traffic accident appraiser test using the OpenAI API with Python code for 30 iterative experiments, and analyzed the percentage of correct answers by test, year, section, and consistency. First, the average correct answer rate for the driver's license test and the for road traffic accident appraisers test was 44.60% and 35.45%, respectively, which was lower than the pass criteria, and the correct answer rate after 2022 was lower than the average correct answer rate. Second, the percentage of correct answers by section ranged from 29.69% to 56.80%, showing a significant difference. Third, it consistently produced the same response more than 95% of the time when the answer was correct. To effectively utilize ChatGPT, it is necessary to have user expertise, evaluation data and analysis methods, design a quality traffic law corpus and periodic learning.