Earticle

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

본 연구는 담임교사 행정업무 제로화 정책이 학교현장에서 어떻게 집행되는지를 분석하는 데 목적이 있다. 부장교사들이 정책집행과정에서 처한 업무상황과 대응기제를 이해하기 위해 본 연구는 Lipsky 일선관료제 모형을 적용하여A중학교를 사례로 하여 질적 사례연구를 수행하였다. 연구 결과는 다음과 같다. A중학교에서 정책집행자이면서 일선관료인 부장교사들은 행정업무지원 부족, 교직원과 관계 갈등, 기대했던 업무환경과 현실의 충돌, 모호한 업무분장과목표, 정책 성과평가의 어려움 등의 업무상황을 경험하였다. 이러한 업무상황에 놓인 부장교사들은 단순화, 정형화, 회피·인내하기의 대응전략을 사용하였다. 이러한 연구 결과를 토대로 본 연구는 교원업무경감 정책의 성공적 집행을 위해서는 협력적 학교풍토가 중요한 환경 요인임을 제시하였다. 궁극적으로 학교행정업무의 총량을 축소하거나 전담행정인력을 지원하는 방식이 학교현장에 적합한 정책 수단이며, 동시에 교원들의 업무능력개발을 위한 지원이 필요함을 제언하였다.

The purpose of the study was to explore the policy implementation process in Homeroom-Teacher Administrative Work Zeroing Policy with the framework of the Street-Level Bureaucracy Model by Michael Lipsky. In doing so, the study employed a qualitative case study method to better understand the policy implementation situations where department head teachers were under as a street-level bureaucrat and their responding behaviors. The study found that department head teachers were under the following circumstances; insufficient resources for policy implementation, difficulty in the relationship with teachers and administrative staff, conflicts between expectations and work environment, ambiguity of school affairs division, ambiguous goals and difficulty in measuring performance. With regard to these policy situations, department head teachers employed their response strategies; simplification, routinization, avoidance and perseverance. Based on the findings, the study suggested that collaborative school climate should be considered as a key factor for the success of teacher’s workload reduction policies, including reduction of administrative workload in school, additional staffing, and support for development of teacher’s work skills.

본 연구의 목적은 특수교사의 인공지능(AI) 활용 경험과 인식을 교수·학습 수행 과정과 수업 외 업무 지원이라는 두 가지 측면에서 심층적으로 탐색하는 것이다. 이를 위해 AI 교육 관련 강의 경력이 2년 이상인 교육혁신 선도 특수교사 9명을 대상으로 초점집단면담을 실시하고, 자료를 분석하였다. 연구 결과는 다음과 같다. 첫째, 교수·학습 수행 과정에서 교사들은 AI와의 상호작용을 통해 수업 설계 및 자료 제작의 효율성을 높였고, 데이터에 기반한 IEP 수립과 개별 맞춤형 교육을 실현하였다. 그러나 학생의 AI 의존 심화와 집중력 저하 등의 부작용이 나타났다. 둘째, 수업 외 업무에서는 행정 업무 자동화를 통해 시간 효율성을 높이고, 학부모 소통 역량 강화 및 교사 정서 지원에 도움이 되었다. 그러나 교사의 AI 의존도 심화, AI의 기술적인 한계점, 데이터 보안 및 개인정보 유출이 문제점으로 지적되었다. 이에 특수교육 현장의 효과적인 AI 활용을 위해 교사의 교육적 전문성 강화, AI 기술 및 윤리 리터러시 교육이 필수적임을 강조하였다.

This study aims to explore special education teachers’ experiences and perceptions of utilizing Artificial Intelligence (AI) from two primary dimensions: the instructional process and administrative workload support. Focus Group Interviews were conducted with nine special education teachers recognized for their expertise in utilizing AI. The findings are as follows. First, regarding the instructional process, teachers enhanced the efficiency of lesson design and instructional material development through AI interaction while successfully developing data-driven IEPs and implementing personalized education. However, adverse effects emerged, including the students’ increasing dependence on AI and diminished attention spans. Second, in terms of administrative workload, AI improved time efficiency through automation, bolstered communication competency with parents, and provided emotional support for teachers. Conversely, concerns were raised regarding teachers’ over-reliance on AI, the technology’s inherent technical limitations, and risks related to both data security and privacy breaches. These results highlight that strengthening teachers’ pedagogical expertise and providing professional development in both AI technology and ethical literacy are imperative for the effective integration of AI within special education settings.

Mental workload has a improtant place in modern work environment such as human-computer interaction. Designing man-machine system requires knowledge and evaluation of the human cognitive process which controls information flow during our works. Many studies estimate reaction time as a index of menatal workload. This paper investigates what reflacts the workload of human information handling when the informations grow its degree. Experiment result introuce the memory time that explain the information-load more sensitive than react time. And react time shows learning effect but memory time does'nt show that effect So it can be concluded that cognitive learning or work schema needs more time to achieve dexterity than motor skill.