• The Journal of the Korea Contents Association
• /
• v.17 no.10
• /
• pp.472-482
• /
• 2017

In this study, discrete mathematical items were classified into analytical items and mathematical items were analyzed on the basis of analytic framework items of mathematics and the past items of mathematics subject contents of the period 2011-2017 school year. First, the discrete mathematics evaluation areas and evaluation contents proposed by the Korea Institute for Curriculum and Evaluation should be evenly distributed. Second, the items of measuring metacognitive knowledge as a strategic knowledge on the use of cognitive methods should be given. Third, the ratio of the number of items in discrete mathematics to the number of that was 3.8%~6.8%, and the ratio according to the item weighting was 2.2%~6.3%. Fourth, it is analyzed that all the items are suitable for the evaluation goal and the pre-service math teachers who have faithfully implemented the curriculum have maintained the appropriate level of difficulty to solve. Finally, the content items such as the method of counting the discrete mathematics curriculum, the Recurrence Relation, the generation function, and the graph are matched with the teacher certification examination and the mathematics education curriculum of each teachers college. By these reasons, we conclude that the contribution of pre-service teachers to the motivation of learning is obtained and implications.

• Journal of Science Education
• /
• v.42 no.3
• /
• pp.308-321
• /
• 2018

The purpose of the study is to develop a questionnaire that examines unskeptical attitudes in scientific inquiry context. The questionnaire items were developed through literature research, expert review, and statistical analyses for validity and the differences in scores were identified by gender and tracks. A total of 363 high school students participated in the study. To explore the validity evidence of items, the Rasch analysis and the reliability of internal consistency were performed, and the two-way ANOVA was performed to compare the scores of the unskeptical attitudes between gender and academic track. Self-reporting and Likert-scaling 23 items were developed to measure unskeptical attitudes in scientific inquiry context. The items were developed in the sub-domain of scientific inquiry: 'questioning and hypothesis generating,' 'experiment designing,' and 'explaining and interpreting.' Second, the validity and reliability of the unskeptical were identified in a rigorous method. The validity of items were identified by multi-dimensional partial score model analysis through the Rasch model, and all 23 items were found to be fit to model. Various reliability evidences were also found to be appropriate. It was found that there were no significant differences of unskeptical attitude score between the gender and academic track except one comparison. The developed questionnaire could be used to check an unskeptical attitude in the course of scientific inquiry and to compare the effects of scientific inquiry classes.

• The Mathematical Education
• /
• v.63 no.2
• /
• pp.187-207
• /
• 2024

In this study, we explored the feasibility of automatically scoring descriptive assessment items using GPT-4 based ChatGPT by comparing and analyzing the scoring results between teachers and GPT-4 based ChatGPT. For this purpose, three descriptive items from the permutation and combination unit for first-year high school students were selected from the KICE (Korea Institute for Curriculum and Evaluation) website. Items 1 and 2 had only one problem-solving strategy, while Item 3 had more than two strategies. Two teachers, each with over eight years of educational experience, graded answers from 204 students and compared these with the results from GPT-4 based ChatGPT. Various techniques such as Few-Shot-CoT, SC, structured, and Iteratively prompts were utilized to construct prompts for scoring, which were then inputted into GPT-4 based ChatGPT for scoring. The scoring results for Items 1 and 2 showed a strong correlation between the teachers' and GPT-4's scoring. For Item 3, which involved multiple problem-solving strategies, the student answers were first classified according to their strategies using prompts inputted into GPT-4 based ChatGPT. Following this classification, scoring prompts tailored to each type were applied and inputted into GPT-4 based ChatGPT for scoring, and these results also showed a strong correlation with the teachers' scoring. Through this, the potential for GPT-4 models utilizing prompt engineering to assist in teachers' scoring was confirmed, and the limitations of this study and directions for future research were presented.

• Journal of The Korean Association For Science Education
• /
• v.44 no.1
• /
• pp.119-130
• /
• 2024

This study explores the possibility of using a generative artificial intelligence, ChatGPT, for student assessment in science subjects. In order to achieve our goal, we developed assessment items, collected students' responses, and input them into ChatGPT to implement the assessment procedures. Subsequently, we shared the assessment results from ChatGPT with science teachers and compared them to the teachers' assessment process to investigate the use of ChatGPT in student assessment. Regarding the results, in terms of setting the scoring rubric, we found the rubric generated by ChatGPT to be generally appropriate. However, the consistency between the scoring results obtained from ChatGPT and those determined by the teachers was relatively low. This inconsistency was more pronounced in items with additional assessment components and a more intricate rubric. In regard to feedback on student responses, there were some instances where the feedback generated was scientifically incorrect or beyond the scope of the curriculum, but there were also some positives, such as the provision of exemplary answers to questions and additional examples that helped students learn further. From these results, the teachers perceived limitations in using ChatGPT to conduct assessment in terms of reliability, which is considered crucial in student assessment, but suggested that it could be used to support assessment. Finally, synthesizing these findings, implications for utilizing ChatGPT in student assessment were suggested.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.3
• /
• pp.547-553
• /
• 2009

Moodle is a web-based open-source LMS. This system provides the effective functions for making a various types of quizzes easily. Any interface for making a Cloze-type quiz conveniently, however, are not provided in the Moodle. In this paper, a web-based program has been developed which helps users make the Cloze-type quizzes easily. The program provides a convenient interface and achieved the considerable reduction in time-cost of making the Cloze-type quiz. The program is very helpful for users to try to make a variety of quizzes by using the Cloze-type.

• Journal of Science Education
• /
• v.41 no.2
• /
• pp.179-194
• /
• 2017

The purpose of this study is to construct two equivalent science relevance recognition test tool after confirming the reliability and validity of the CARS(Changes in Attitude of Relevance to Science) questionnaire to determine the applicability of the items to Korean students and to compare gender and school differences. For this study, 59 items of the CARS scientific relevance test were translated and assigned to 787 middle and high school students (analyed the answer of 300 middle school students and 431 high school students). In order to determine the fit of the CARS question to Korean students and to overcome the limitation of the number of questions, we used the item-linking method of the Rasch model. By analyzing the results of the research, we constructed two equivalent scientific relevance recognition questionnaires of CARS-A and CARS-B with 25 items. The Pearson correlation coefficient of the Rasch scores of the two equivalent test was 0.78. The two types of scientific relevance recognition test tools generated through this study can be used to confirm students' attitude of scientific relevance to daily life, or to confirm the change after a certain class or grade. Through this study, we will discuss the implications of students' perceptions of science associations in science education, and the development and application of tools.

• Proceedings of the Korean Society for Cognitive Science Conference
• /
• 2006.06a
• /
• pp.9-14
• /
• 2006

본고는 한국어 교육방식의 하나라고 할 수 있는 한국어 어휘를 대상으로 한문제 출제 방식에서 문제 은행식 출제 방식이 갖고 있는 여러 가지 문제점을 해소할 수 있는 하나의 방법으로서 한국어 어휘 학습 시스템을 위한 자동문제 생성 기술을 제시한다. 먼저 기존 한국어 어휘 문제의 문항 분석 결과를 바탕으로 8가지 어휘력 평가 유형 및 각 유형별 자동 문제 생성 패턴을 구축하고, 한국어 어휘에 대한 풍부한 정보를 담고 있는 국어사전을 기반으로 한 자동 한국어 어휘 문제 생성 기술을 제시한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2009.01a
• /
• pp.153-158
• /
• 2009

SCORM(Sharable Content Object Reference Model)은 세계 e-Learning 표준화 분야에서 가장 주목을 받고 있는 ADL(Advanced Distributed Learning)의 표준화 모델이다. SCORM2004 RTE(Run-Time Environment) 에서 상호작용 데이터 모델(Interaction Data Model)의 기능을 활용하면 LMS(Learning Management System)가 문항을 자동 생성하여 문제은행을 보다 쉽게 구현할 수 있다. 내용학습 후에 형성평가를 실시하기 위한 문항을 학습자가 원하는 만큼 공급할 수 있다. 본 연구는 일반계 고등학교 수학교과의 삼각함수 성질을 학습하는 데 있어 RTE의 상호작용 데이터 모델로 구현한 문제은행을 갖춘 반복학습 콘텐츠를 개발하여 학습효과를 높이고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.10a
• /
• pp.599-601
• /
• 2009

In currently being adopted as a e-leaning standard, SCORM it is difficult to provide the customized contents to a learner by changing the learner's level at runtime, and to control selective studying. So, we designed and implemented the customized contents organization engine(CCOE) in order to complement SCORM's faults in this paper. The CCOE consists of a level evaluation module, a contents re-organization module and a question item selection module. A level evaluation module evaluates the learner's level based on a question item reaction theory. And a question item selection module selects some random items by each level or by considering the learner's level which is then provided to a studying before evaluation, a section evaluation, and a quiz. And then this module transmits the selected items to the contents reorganization module for providing the quiz. A contents re-organization module selects the customized contents based on the learner's level by searching the tagged difficulty to the content, and creates the sequence with the selected items and the transmitted items from the question item selection module. If proposed in this paper CCOE is applied, the higher effectiveness of learning is expected by providing the customized learning contents based on the re-evaluated learner's level by each section.

• Journal of The Korean Association For Science Education
• /
• v.36 no.3
• /
• pp.389-397
• /
• 2016

This study aims to test the efficacy of English-based automated computer scoring models and machine translation to score Korean college students' written responses on natural selection concept items. To this end, I collected 128 pre-service biology teachers' written responses on four-item instrument (total 512 written responses). The machine translation software (i.e., Google Translate) translated both original responses and spell-corrected responses. The presence/absence of five scientific ideas and three $na{\ddot{i}}ve$ ideas in both translated responses were judged by the automated computer scoring models (i.e., EvoGrader). The computer-scored results (4096 predictions) were compared with expert-scored results. The results illustrated that no significant differences in both average scores and statistical results using average scores was found between the computer-scored result and experts-scored result. The Pearson correlation coefficients of composite scores for each student between computer scoring and experts scoring were 0.848 for scientific ideas and 0.776 for $na{\ddot{i}}ve$ ideas. The inter-rater reliability indices (Cohen kappa) between computer scoring and experts scoring for linguistically simple concepts (e.g., variation, competition, and limited resources) were over 0.8. These findings reveal that the English-based automated computer scoring models and machine translation can be a promising method in scoring Korean college students' written responses on natural selection concept items.