• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.871-878
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• 2021

This study analyzed the polarization of software education by analyzing the relationship between the average monthly total private education expenditure, software education expenditure, and academic achievement. For this purpose, data were collected and analyzed by surveying 2,780 parents of elementary school children nationwide. The results of this study are as follows: First, there was a statistically significant difference in children's academic achievement depending on whether or not they participated in software education. Second, the higher the children's academic achievement, the higher the percentage of participation in software private education expenditure. Third, there was a significant positive correlation between total private education expenditure and software-related private education expenditure. Fourth, although not statistically significant, there was a positive correlation between software private education expenses and academic achievement. In this study, software education provided by public education is not sufficient. For this reason, participation in private education and the amount of expenditure are increasing, and there is a gap in improving the main competencies of students according to household income.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.349-354
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• 2021

In this study, a model that can converge with artificial intelligence in each subject as software and artificial intelligence education become mandatory in the curriculum revised in 2022 is proposed. The proposed AI convergence education model is based on the content of the subject (accomplishment standard + subject). The second axis is artificial intelligence tools, the third axis is artificial intelligence technology, and the fourth axis is data applied in daily life. In order to apply artificial intelligence to each subject, it is necessary to apply artificial intelligence tools, artificial intelligence technology, and data in daily life to the achievement standards and content of each subject. If the achievement standards and subject contents are structured in this way, it can be seen that the convergence with each subject is good. Therefore, when composing textbooks by achievement standards and topics, it is necessary to add artificial intelligence tools, artificial intelligence technology, and daily data.

• Journal of Science Education
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• v.42 no.2
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• pp.165-181
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• 2018

This study compares and analyzes the characteristics and effects of various educational contextual variables according to students' achievement level and gender groups based on the results of PISA 2015 science domain. PISA 2015 included additional variables about teaching-learning and affective characteristics in the field of science, because science was the main domain of PISA 2015. The results of the mediation analysis using a multiple group structural equation model showed that the environment and strategy for the teaching and learning had a positive effect on the affective characteristics, and also positively affected science achievement through the mediator of the affective characteristics. Particularly, the environment and strategy for the teaching and learning was the most effective in improving the affective characteristics for the low achievement group. It was found that the difference of the mediated effect between achievement level groups was statistically significant, but that between male and female students was not. Therefore, the appropriate the environment and strategy for the teaching and learning will need to be emphasized consistently to improve students' cognitive and affective achievement. The implications and suggestions of these results were discussed.

• Journal of The Korean Association For Science Education
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• v.37 no.5
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• pp.891-909
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• 2017

This study analyzed some of the discrepancies in quantitative and qualitative data focusing on cognitive and affective achievement in science education. Academic and affective achievement score of 308 high school students were collected as quantitative data, and 33 students were interviewed for qualitative data. We examined the causes and types of discrepancies in terms of testing tools. As a result from quantitative data, there were a large number of students with a big difference between subjects in cognitive achievement, and constructs in affective achievement. More than 20% of the students did not match tendency between achievements in two areas. Through interviews, some examples such as intentional control of science learning for future study and careers, different responses by differences in perception between school science and science, appeared. A comparison of quantitative data by testing tool between qualitative ones and interviews showed conflicting result, where most students evaluated themselves differently from their own quantitative data. That is due to the students' interaction with the testing tools. Two types of discrepancy related to testing tool are found. One is 'the concept difference between the item developer and students,' the other is 'the difference between students' exposed response and their real mindset.' These are related to the ambiguity of the terms used in the tool and response bias due to various causes. Based on this study, an effort is required to elaborate the testing item that matches students' actual perception and to apply students' science learning experience to testing items.

• Journal for History of Mathematics
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• v.21 no.3
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• pp.157-182
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• 2008

TIMSS 2003 is the third and most recently round of IEA's Trends in International Mathematics and Science Study. In this study, I considered the changes of the mathematics cognitive domain in TIMSS and got some facts for developing assessment framework. And I analyzed 7 countries' achievement in the view of our country Korea, i.e. Singapore, Hongkong, Chinese Taipei, Japan, Netherlands, and Unites States. With the reliable and valid achievement scales for cognitive domains given by ISC, students' achievement scales were analyzed according to country, percentile, and sex in each cognitive domain.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.550-566
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• 2018

In the era of the 4th industrial revolution, enhancing achievement evaluation based on process oriented instruction is essential. It assists human growth both cognitively and mentally. The purpose of this study is to analyze current condition of high school achievement evaluation in Daejeon region for enhancing English class based on achievement Assessment. Analyzing high school achievement evaluation plans as well as analyzing items of paper and pencil test using TELL program was conducted. Reanalysis of global citizen theme-based English according to core achievement standard was performed. The questionnaire was analyzed using the SPSS Win 20.0 Program to figure out significant difference of instructional method, the rate of students' grade improvement, English class related to the state of career recognition. T-test, ANOVA was performed to determine if there was a difference between the individual instructional variables. The research result is designed to construct or develop English class based on achievement evaluation while providing each high school with the result of current state of high school achievement evaluation. Specific characteristics of individual achievement result was conducted in terms of analyzing distribution of answer sheet response in order to be used as information for managing each high school achievement evaluation.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.892-896
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• 2008

인터넷 사용의 일상화와 초고속통신망의 급속한 확산은 웹 기반 원격교육의 보편화를 가져왔다. 웹 기반 원격교육은 전통적 교육방식인 면대면 교육방식의 새로운 형태로써 도입되었으나 학습자와 교육자의 요구사항으로 면대면 교육방식의 대체제가 아닌 전략적 도구로써 진화하고 있다. 웹 기반 원격교육은 e러닝, e멘토링, 블렌디드러닝 등 다양한 시도가 추세이다. 이러한 시도의 공통적인 특징은 학습자, 교육자, 시스템 간의 상호작용에 대한 요구사항이 높아지고 있다는 것이다. 이 연구에서 웹 기반 원격교육의 학업성취에 영향을 미치는 변인과 웹 기반 원격교육 시스템 상호작용에 대한 조절효과를 실증 분석하였다.

• Journal of The Korean Association For Science Education
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• v.42 no.6
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• pp.611-619
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• 2022

The purpose of this study is to explore the possibility of applying big data analysis to provide appropriate feedback to students using evaluation data in science education at a time when interest in educational data mining has recently increased in education. In this study, we use the evaluation data of 2,576 students who took 24 questions of the national assessment of educational achievement. And we use K-means cluster analysis as a method of unsupervised machine learning for clustering. As a result of clustering, students were divided into six clusters. The middle-ranking students are divided into various clusters when compared to upper or lower ranks. According to the results of the cluster analysis, the most important factor influencing clusterization is academic achievement, and each cluster shows different characteristics in terms of content domains, subject competencies, and affective characteristics. Learning motivation is important among the affective domains in the lower-ranking achievement cluster, and scientific inquiry and problem-solving competency, as well as scientific communication competency have a major influence in terms of subject competencies. In the content domain, achievement of motion and energy and matter are important factors to distinguish the characteristics of the cluster. As a result, we can provide students with customized feedback for learning based on the characteristics of each cluster. We discuss implications of these results for science education, such as the possibility of using this study results, balanced learning by content domains, enhancement of subject competency, and improvement of scientific attitude.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.267-277
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• 2022

Achievement at university is recognized in a comprehensive sense as the level of qualitative change and development that students have embodied as a result of their experience in university education. Therefore, the academic achievement of university students will be given meaning in cooperation with the historical and social demands for diverse human resources such as creativity, leadership, and global ability, but it is practically an indicator of the outcome of university education. Measurement of academic achievement by such credits involves many problems, but in particular, standardization of academic achievement by credits based on evaluation methods, contents, and university rankings is a very difficult problem. In this study, we present a model that uses machine learning techniques to predict whether or not academic achievement is excellent for D-University graduates. The variables used were analyzed using up to 96 personal information and bachelor's information such as graduation year, department number, department name, etc., but when establishing a future education course, only the data after enrollment works effectively. Therefore, the items to be analyzed are limited to the recommended ability to improve the academic achievement of the department/student. In this research, we implemented an academic achievement prediction model through analysis of core abilities that reflect the philosophy, goals, human resources image, and utilized machine learning to affect the impact of the introduction of the prediction model on academic achievement. We plan to apply the results of future research to the establishment of curriculum and student guidance conducted in the department to establish a basis for improving academic achievement.

• Communications of Mathematical Education
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• v.14
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• pp.229-250
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• 2001

협동 학습은 학습자간의 긍정적 상호 작용을 촉진하여 학습의 극대화를 도모하고자 하는 수업 방법으로, 수학 과목에서 협동 학습은 수학에 대한 성취도, 태도, 문제 해결력 등 인지적, 정의적 영역에서 긍정적인 효과를 나타내고 있다. 이에 본고에서는 수학 과목에서 협동학습에 대한 국${\cdot}$내외의 연구 동향을 살펴보고, 중학교 학생들의 학습양식과 수학성취도와의 관계를 조사하여 학습양식에 따라 소집단을 구성한 수학에서의 협동 학습을 모색하고자 한다.