• 한국콘텐츠학회논문지
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• 제21권12호
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• pp.477-489
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• 2021

본 연구는 프로티언 경력지향성이 지속학습활동을 통해 주관적 경력성공에 영향을 미치는 매개과정에 대해서 조직문화 불균형성이 조절변인으로서의 역할을 하는지를 확인하기 위해 실시되었다. 이를 위해 대기업 경력 5년 이상의 사무직 근로자 276명을 대상으로 설문조사를 실시하였으며, SPSS 25와 Process Macro v3.5를 활용하여 자료를 분석하였다. 분석 결과 프로티언 경력지향성이 주관적 경력성공에 영향을 미치는 관계를 지속학습활동이 매개하는 것은 확인되었지만, 조직문화 불균형성의 조절효과 및 조절된 매개효과는 통계적으로 유의하지 않았다. 그러나 프로티언 경력지향성의 하위변인인 '자기주도성'이 지속학습활동을 통해 주관적 경력성공 및 그 하위변인 '고용가능성', '경력만족'에 영향을 미치는 매개과정에 대한 조직문화 불균형성의 조절 효과는 통계적으로 유의하게 나타났다. 마지막으로 본 연구의 시사점에 제한점, 후속연구 제언을 논의하였다.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• 디지털융복합연구
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• 제14권4호
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• pp.69-78
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• 2016

이 연구는 대학 학습공동체에 참여한 대학생들의 팀상호작용과 팀메타인지가 학습공동체 활동의 지속참여 의향에 어떠한 융복합적 영향을 주는가를 분석하기 위한 것이다. 팀상호작용 19개 문항과 팀메타인지 17개 문항으로 된 측정도구로, A 대학교 학습공동체 프로그램에 참여한 15개 팀 113명의 대학생을 대상으로 실시하였다. 이항 로지스틱 회귀분석을 통하여 밝힌 연구 결과는 첫째, 학습공동체 지속참여 의향에 팀상호작용 수준과 팀메타인지 수준이 영향을 미치는 것으로 나타났다. 팀상호작용 수준이 높을수록, 팀메타인지 수준이 낮을수록 지속참여할 가능성이 증가하였다. 둘째, 학습공동체 지속참여 의향에 영향을 주는 팀상호작용 요인 중, 학습 횟수가 많을수록, 팀원들이 학습을 위해 서로 격려할수록 지속참여 가능성이 증가하였고, 팀원들이 열심히 활동하지 않을수록, 학습에 몰입하지 않을수록, 학습 시간이 적을수록 지속참여 가능성이 감소하는 것으로 나타났다. 셋째, 학습공동체 지속참여 의향에 영향을 주는 팀메타인지 요인 중, 학습 횟수가 많을수록 참여 의향 가능성이 증가하고, 다양한 학습도구를 사용할수록, 평균 학습시간이 많을수록 참여 의향 가능성이 감소하였다. 이상의 결과를 바탕으로 대학생의 학습공동체 지속참여를 위한 융복합적 지원 방안은 다음과 같다. 팀상호작용을 촉진하기 위해 학습 횟수를 늘리고, 팀원간 상호 격려하도록 지원하여 긍정적인 학습공동체 경험을 유도하고, 팀메타인지 활동의 필요성과 유용성을 학생들이 충분히 인식하도록 효과적인 활용 방법을 지원할 필요가 있다.

• 대한안전경영과학회지
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• 제12권1호
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• pp.83-96
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• 2010

Learning Organization is a learning based community to make the most important value in the era of Knowledge Economy, Creation. That's why people share, facilitate personal, individual's knowledge & experience systems each other and make good thoughts & ideas in the organization. This study measures the building practices having conducted the F team in Samsung electronics using DLOQ that indicates the activate degree of Learning Organization and the quantitative degrees of Learning Organization through comparing the cross-evaluation between the already measured companies in addition to analyzing the F team's success factors. Learning Organization requires sustainable and continuous activity, not completes by changing many factors with human resources. The study will have the achievement if we measure the successful activity through global companies built a Learning Organization and facilitate the improvement activity sustainably.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2010년도 춘계학술대회
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• pp.218-225
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• 2010

Learning Organization is a learning based community to make the most important value in the era of Knowledge Economy, Creation. That's why people share, facilitate personal, individual's knowledge & experience systems each other and make good thoughts & ideas in the organization. This study measures the building practices having conducted the F team in Samsung electronics using DLOQ that indicates the activate degree of Learning Organization and the quantitative degrees of Learning Organization through comparing the cross-evaluation between the already measured companies in addition to analyzing the F team's success factors. Learning Organization requires sustainable and continuous activity, nor completes by changing many factors with human resources. The study will have the achievement if we measure the successful activity through global companies built a Learning Organization and facilitate the improvement activity sustainably.

• 한국과학교육학회지
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• 제24권1호
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• pp.76-89
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• 2004

Recent studies of science and technology "in-the-making" revealed that the process of designing material artifacts is not a straightforward application of prior images or theories by one (or more) person(s) isolated from his or her (their) environment. Rather, designing is a process contingent on the social and material setting for both engineering designers and students. Over the past decade, designing technological artifacts has emerged as an important learning environment in science classrooms. Through the analyses of a large database concerning an innovative simple machines curriculum for sixth-and seventh-grade students, we accumulated valid evidence for the nature of the designing process and science learning through it. In this paper, we show that design actions intertwine with the transformation of the objectified raw materials and artifact, the designer collective, and the mediating tools enabling that transformation, which constitute the elements of an activity from the perspective of cultural-historical activity theory. We conceptualize the continuous change of relation between material artifacts, designers, and tools throughout the design activity as co-evolution. Two episodes were selected to exemplify synchronic and diachronic change of relations inherent in co-evolving activity system. Finally, we discuss the implications of co-evolution during design activity for science learning.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• 대한화학회지
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• 제60권2호
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• pp.137-143
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• 2016

Effective teaching and learning is a continuous process of monitoring and re-organization of teaching method, so to benefit both students and educators. Reflective journal writing is an effective method for students to reflect on their learning experience about a new concept or subject taught and at the same time enables educators to improve on their academic skills. In the present paper, we have examined and evaluated the effectiveness of the Uncritical Inference Test (UIT) that was conducted in our basic organic chemistry course through a systematic network built based on students’ reflective writing. From the data analysis, the UIT has benefited students in three dimensions, namely cognitive, affective and group learning domains. Moreover, the UIT activity instilled an active learning environment in organic chemistry classroom and deeper learning among chemistry students as shown in the collected data. In future, this activity could be adapted as a teaching method to enhance students’ critical thinking skills and question-asking capability in other teaching courses.

• 한국콘텐츠학회논문지
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• 제17권6호
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• pp.124-131
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• 2017

디지털 미디어 시대에 미디어 활용이 증가하면서 학자들과 시청자주권 관련 시민단체들은 미디어 교육의 중요성을 강조해 왔다. 그렇지만, 미디어 교육의 효과와 참여 증진에 영향을 미치는 요인에 대한 연구가 부족한 것이 현실이다. 이에 본 연구는 미디어교육 참여자들의 참여동기가 만족도와 미디어 교육 지속참여 의도에 미치는 영향을 살펴보고자 하였다. 이를 위해 광주와 부산의 시청자미디어센터 교육 참여자를 대상으로 설문조사를 실시하였다. 연구 결과, 활동지향 동기가 참여자들의 만족도와 교육 지속참여의도에 영향을 미치는 것으로 나타났다. 본 연구의 결과는 향후 미디어 교육프로그램 계발에 활용하여 시청자의 미디어교육 참여를 더욱 확대하는데 활용할 수 있을 것이다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.141-143
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• 2021

The Activity patterns of animal species are difficult to access and the behavior of freely moving individuals can not be assessed by direct observation. As it has become large challenge to understand the activity pattern of animals such as dogs, and cats etc. One approach for monitoring these behaviors is the continuous collection of data by human observers. Therefore, in this study we assess the activity patterns of dog using the wearable sensors data such as accelerometer and gyroscope. A wearable, sensor -based system is suitable for such ends, and it will be able to monitor the dogs in real-time. The basic purpose of this study was to develop a system that can detect the activities based on the accelerometer and gyroscope signals. Therefore, we purpose a method which is based on the data collected from 10 dogs, including different nine breeds of different sizes and ages, and both genders. We applied six different state-of-the-art classifiers such as Random forests (RF), Support vector machine (SVM), Gradient boosting machine (GBM), XGBoost, k-nearest neighbors (KNN), and Decision tree classifier, respectively. The Random Forest showed a good classification result. We achieved an accuracy 86.73% while the detecting the activity.