보고서 정보
주관연구기관 |
한국교육개발원 Korean Educational Development Institude |
연구책임자 |
이기준
|
참여연구자 |
임후남
,
한효정
,
남궁지영
,
금종예
,
장윤정
,
양태정
,
손영호
,
김다솜
|
보고서유형 | 최종보고서 |
발행국가 | 대한민국 |
언어 |
한국어
|
발행년월 | 2021-12 |
과제시작연도 |
2021 |
주관부처 |
국무조정실 The Office for Government Policy Coordination |
등록번호 |
TRKO202300003492 |
과제고유번호 |
1105017326 |
사업명 |
한국교육개발원(R&D) |
DB 구축일자 |
2023-06-28
|
키워드 |
교육 분야 데이터.연구 데이터.정책 사업 데이터.Education data.Research data.Policy project data.
|
초록
▼
□ 결론 및 제언
○ 이 연구를 통해 교육분야에서 발생되는 연구 데이터와 정책데이터의 특성을 구분하고 이들의 특성을 잘 표현할 수 있는 분류 기준을 제시하였다. 이를 통해 교육분야 데이터의 연구 데이터와 정책사업 데이터를 수집하고 분류하여 분석한 결과를 보이고 있으며 교육분야 데이터를 향후 데이터의 사용자가 자신이 원하는 데이터의 존재를 빠르고 효율적으로 찾을 수 있는 시스템을 제안하였다.
○ 향후 교육분야 데이터의 활용과 공유를 활성화하기 위해서는 교육 분야의 데이터 전체를 종합적으로 한데 묶어 볼 수 있는 총람 정보의
□ 결론 및 제언
○ 이 연구를 통해 교육분야에서 발생되는 연구 데이터와 정책데이터의 특성을 구분하고 이들의 특성을 잘 표현할 수 있는 분류 기준을 제시하였다. 이를 통해 교육분야 데이터의 연구 데이터와 정책사업 데이터를 수집하고 분류하여 분석한 결과를 보이고 있으며 교육분야 데이터를 향후 데이터의 사용자가 자신이 원하는 데이터의 존재를 빠르고 효율적으로 찾을 수 있는 시스템을 제안하였다.
○ 향후 교육분야 데이터의 활용과 공유를 활성화하기 위해서는 교육 분야의 데이터 전체를 종합적으로 한데 묶어 볼 수 있는 총람 정보의 통합 생성 및 제공 시스템 필요하고 제공 데이터의 범위를 확대할 필요, 데이터 연계성 확대를 위한 개인정보 관리 가이드라인 및 심의기구 구축 필요, 교육분야 데이터 활용 활성 활성화를 위한 연합/협력체계 구축 필요를 제언하였다.
(출처 : 연구요약 18p)
Abstract
▼
After the enactment of the ‘Public Data Act’ in 2013, the policy stance regarding the openness of public data has changed from a passive to an active one. Since the 3 Data Act (Personal Information Protection Act, Information and Communications Network Act, Credit Information Act) was revised in 202
After the enactment of the ‘Public Data Act’ in 2013, the policy stance regarding the openness of public data has changed from a passive to an active one. Since the 3 Data Act (Personal Information Protection Act, Information and Communications Network Act, Credit Information Act) was revised in 2020 to utilize public data, the concept of public data has been also changing to the concept of openness, sharing, and analysis, and the recognition that the owner of public data is the public has been spreading.
The data-based administrative law came into effect in 2020 as public data emphasizes the linkage and utilization in the openness aspect. Accordingly, public institutions are actively conducting data provision, connection, and joint use.
Accordingly, in order to open and share public data, and vitalize data-based education administration, it is necessary to analyze the current state of production and use of data in the education field, and the possibility and tasks of data linkage utilization. In the field of education, it is necessary to understand the current status of data collected by research area, data type, and survey target.
Also, improvement measures are needed for education data that needs supplementation and is being produced but with low linkage potential. In addition, it is necessary to present a plan for improvement by deriving specific tasks related to the possibility of linkage.
For efficient use of education data that are dispersed in the education field, this study analyzes the production, management, and utilization of data, deduces major issues and problems related thereto, and proposes plans to utilize the education data map for active use of education data.
The study focuses on how much data, which is required for the establishment and execution of major education policies, is produced and utilized, what data areas and types are lacking, to what extent existing data are open, shared, and utilized, what the reasons are if the sharing and utilization are limited, how standardized standards and classifications that are necessary for data openness, sharing, and utilization are, and what policy tasks are needed to activate the linkage and utilization of data.
The demand for evidence-based policy establishment has been emphasized since the early 2000s and many studies have been conducted. However, there are few studies on the establishment of a data utilization system in the education field since most of the research has focused on collecting the types of data collected by each institution and analyzing the fragmentary data.
Educational data is surveyed, managed, and serviced by education-related institutions such as the Korea Education and Research Information Service, the Korean Educational Development Institute, and the Korea Institute for Curriculum and Evaluation. The data managed by most institutions are largely divided into two categories: research data and policy project data.
Research data mainly refers to quantitative and qualitative data collected in the process of carrying out the institution’s research projects or commissioned research. Since this research data is collected in the course of conducting research, the cost of collecting, managing, and processing the data is often relatively small. In most cases, the research is conducted in a short period of time, and the continuity of data is cut in line with the research cycle. Looking at the management of data collected as research data, most institutions primarily manage it directly by the chief researcher, but recently, it is required to be submitted according to the institution’s regulation or guideline.
In most cases, the research data in the education field are divided according to the education level such as early childhood education, elementary and secondary education, higher education, and lifelong education, and the subjects of research are limited to the subjects of school education such as students, teachers, and parents. Most common research methods are qualitative surveys such as Delphi surveys, and most of the studies conducted such surveys include questionnaires or survey papers in their original text in the appendix of the research report.
Policy project data refers to data collected or generated in the course of entrusting the implementation of specific education-related tasks from government ministries such as the Ministry of Education or various institutions. In general, the policy project enables establishment of a set of time series data since it relatively has large budget for project execution and maintains project continuity. Since most of the policy project data is collected for the execution of policy tasks, it is generated regularly during the period of the project and is generally managed by a database system-based computer system.
This study classifies and analyzes data of the Ministry of Education’s policy projects and data of research conducted at various research institutes in the field of education such as the Korean Educational Development Institute, the Korea Institute of Child Care and Education, the Korea Institute for Curriculum and Evaluation, the Korea Research Institute for Vocational Education & Training, and the National Youth Policy Institute.
The data is classified according to the metadata data classification system designed in this study, based on the data survey and the contents describing the outline of the data investigation conducted in the 2020 research report, published on the internet sites of the Korean Educational Development Institute, the Korea Institute of Child Care and Education, the Korea Institute for Curriculum and Evaluation, the Korea Research Institute for Vocational Education & Training, and the National Youth Policy Institute. For the classification, this study investigates 95 studies from 5 institutions: 24 of the Korean Educational Development Institute, 13 of the Korea Institute of Child Care and Education, 21 of the Korea Institute for Curriculum and Evaluation, 22 of the Korea Research Institute for Vocational Education & Training, and 15 of the National Youth Policy Institute. The total number of surveys in those studies is 245, including 54 of the Korean Educational Development Institute, 43 of the Korea Institute of Child Care and Education, 63 of the Korea Institute for Curriculum and Evaluation, 53 of the Korea Research Institute for Vocational Education & Training, and 32 of the National Youth Policy Institute.
In order to categorize the survey, this study uses the final 15 items. For classification of the research data, it consists of the name of the project, the person in charge of the research, the name of the survey used to generate the data in the research, and the survey description that can confirm the purpose of the research. In order to identify other attribute information, it contains the fields of analysis, survey content, survey method, survey subject, survey size, questionnaire disclosure, number of surveys, number of respondents' basic information items, whether original data are included, multi-year survey, and whether or not approval statistics are included as survey items.
A data map of various dimensions is created using the classification results of the research data classified in this way. This research data map can be used to easily find an accessible route by allowing users who want to use research data to quickly find out which survey of which studies at which institutions have data suitable for the main topic of their desired study.
In addition, according to a structured classification table, this study analyzes the results of classification of data from various policy projects carried out by educational institutions. For the data used for this purpose, a part of the data managed by the Ministry of Education is transferred and analyzed. In addition, for some data, the results that are classified in this study are finally reviewed and analyzed by the person in charge at the relevant institution.
This study analyzes a total of 59 policy projects from 16 institutions. According to the nature of the data, the fifty-nine systems are classified as a total of 17 items according to the data classification corresponding to the 13 sub-classifications described in the next section. As a result, the fifty-nine systems are found to have a total of 222 sub-classification.
For the data generated in the policy project investigated in this way, the characteristics of the data are classified according to their purpose, form, and management status. To classify this, this study designs a classification table of 17 items of policy project data.
The policy project data analyzed in this way can also create a data map of various dimensions. This data map shows which system has data suitable for the main topic of the user’s desired task. It can be used to quickly find out who owns the data and find out which institutions have it, providing an easy way to find a path to access the data.
In this way, we have shown examples of data map configuration for research data and policy project data. However, in order to increase the usability of the data map, it should be a data map that users can create by combining various data characteristics according to their requirements, rather than a data map created by the provider in advance. Therefore, this study produces a data map generation system prototype that can create a data map by combining the characteristic information of the research data and policy project data generated through this study up to three dimensions.
The data map generation system prototype is made so that users can select up to three characteristic variables of research data and policy project data on a web browser. It is developed in a form in which the detailed information of the data is displayed at the bottom of the plot when the user clicks on the number of the detailed information that the user wants to know about among the displayed data number.
The study distinguishes the characteristics of research data and policy data generated in the education field, and presents classification criteria that can express these characteristics. This study shows the results of collecting, classifying, and analyzing research data and policy project data of education field, and proposes a system that enables users to quickly and efficiently find the education data they want.
In order to activate the utilization and sharing of education data in the future, the study emphasizes the need of creation and provision of an integrated system for creating and providing catalog information that can comprehensively bundle all data in the education field, and suggests to expand the scope of the provided data, and establish personal information management guidelines, a deliberation body to expand data linkage, and a coalition/cooperation system to vitalize the use of data in the education field.
(source : Abstract 189p)
목차 Contents
- 표지 ... 1
- 머리말 ... 5
- 연 구 요 약 ... 7
- 목차 ... 21
- 표목차 ... 23
- 그림목차 ... 25
- Ⅰ. 서 론 ... 27
- 1. 연구의 필요성 및 목적 ... 29
- 2. 연구 내용 ... 32
- 3. 연구 방법 ... 36
- Ⅱ. 선행 연구 및 사례 분석 ... 41
- 1. 선행연구 분석 ... 43
- 2. 데이터 맵 사례 분석 ... 53
- Ⅲ. 교육 분야 데이터 특성 및 관리 현황 ... 63
- 1. 교육분야 데이터 종류와 특성 ... 65
- 2. 교육분야 데이터 관리 현황 ... 71
- 3. 교육분야 데이터 연계 문제점 ... 85
- Ⅳ. 교육 분야 데이터 현황 및 데이터 맵 분석 ... 87
- 1. 연구 데이터 분류 및 분석 ... 89
- 2. 정책 사업 데이터 분류 및 분석 ... 125
- 3. 교육분야 데이터 맵 시스템 프로토타입 설계 ... 169
- Ⅴ. 결론 및 제언 ... 175
- 1. 결론 ... 177
- 2. 제언 ... 180
- 참고문헌 ... 187
- Abstract ... 189
- 부록 ... 195
- 부록1. 연구 데이터 분류 대상 연구 과제명 및 조사명 ... 197
- 끝페이지 ... 210
※ AI-Helper는 부적절한 답변을 할 수 있습니다.