• International Journal of Advanced Culture Technology
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• 제11권2호
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• pp.56-63
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• 2023

I proposed the architectural guideline for educational environment based on design thinking approach to integrate and enhance learners' activities and achievements. The physical environment of design education learning space should be applied by teaching methods and learning activities, especially for STEAM-based convergent education, the architectural space conditions should support the design process based on design thinking. The learning environment conditions influence design education with physical design factors and learners' communication, and the flexible environment based on design thinking, which is crucial for design education. The 3 steps of design thinking experiences also allow students to learn the context of ideas, skills and outcomes. Therefore, I argued that the learning surrounding based on design thinking needs flexible and mobile, connected, integrated, organized, and team-focused environments to support learners' understanding, participation, and collaboration, and to achieve the design process based on research findings. For spaces for convergent learning environments based on design thinking, common design principles should be reviewed, such as coexistence with technology, safety and security, transparency and spatial extension, multi-purpose space and outdoor learning.

• 한국엔터테인먼트산업학회논문지
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• 제13권7호
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• pp.469-477
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• 2019

본 연구에서는 피부미용 전공 학생들을 위한 TBL 수업을 개발하고, 그 효과성을 검증하기 위하여 G대학교 전공 교과목에 적용한 결과를 분석하였다. TBL은 학습내용에 대한 선행학습과 그로 인해 절약된 강의시간을, 학습한 내용을 적용하는 연습활동에 투입하는 것을 중요시한다. 이에 개발연구방법론으로 ADDIE 모형과 TBL 모형을 적용하여 기존의 강의식으로 진행했던 '피부미용' 교과목을 TBL 수업으로 재설계하였으며, 주차별 선행학습자료, 사전학습 확인용 퀴즈, 팀활동 계획안을 개발하였다. 그리고 43명의 학생을 대상으로 한 실험연구를 통하여 TBL 수업의 효과를 다음과 같이 분석하였다. 첫째, 전공개념 획득 및 적용을 평가한 학업성취도에서 TBL 수업에 참여한 학생들이 비교집단인 강의식 수업에 참여한 학생들보다 높은 성취도를 나타났다. 둘째, TBL 수업 참여 학생들을 대상으로 실시한 문제해결능력과 협력적 자기효능감에 사전-사후 검사 결과에서 유의미한 차이가 없었다. 셋째, TBL 수업에 대한 전반적인 만족도는 4.0으로 높은 수준을 보였다. 이러한 연구 결과에 대한 논의를 기술하고, 향후 TBL 수업 개선 및 연구를 위한 3가지 제언을 제시하였다.

• 공학교육연구
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• 제15권6호
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• pp.34-42
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• 2012

Problem solving by homework assignment is a process of practicing what were discussed in classrooms and thus is considered as an essential part of learning procedure in engineering education. We introduced the concept of cooperative learning, Student Teams-Achievement Division(STAD), to improve the students' learning efficiency by in-class problem solving. The instructor explained fundamental concepts, and lecture materials were handed out to compensate for the time of in-class team activity. Brief tests were given after every chapter, and team-based additional credits were given for the improvement comparing the average of previous tests of each student. This attempt of modified STAD was evaluated to have brought about a significant improvement in students' academic achievement, in addition to activating classroom atmosphere.

• 한국멀티미디어학회논문지
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• 제23권11호
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• pp.1396-1405
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• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.

• 한국문헌정보학회지
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• 제39권3호
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• pp.105-119
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• 2005

최근 들어 인터넷 기반 교육, 사이버 교육 등 다양한 교육프로그램 및 방식이 교육현장에 도입됨에 따라 기존의 전통적인 교육과 달리 전자매체에 의존하는 교육내용전달에 대한 관심과 요구가 증가하고 있다. 본 연구의 준비단계로 실시한 사전연구에서 공동학습을 위해 형성되었던 온라인 학습집단의 약 절반정도가 가상공간에서의 공동학습을 효율적인 것으로 답한 반면, 나머지 절반정도는 학습집단의 형성과 학습의 진행에 어려움을 경험했다. 이 연구는 대학 학부생들로 구성된 온라인 학습집단들로 하여금 전자우편과 토론게시판의 두 가지 전자매체를 통해서 주어진 토론과제를 수행하도록 하고, 각 전자매체의 특성과 온라인 공동학습집단의 정보공유 패턴 및 학습의 효율성과의 연관성을 조사하였다. 연구의 결과 전자우편의 경우는 학습자 간의 상호작용에 있어서 좀 더 개별적이고 친숙한 느낌으로 정보를 전달하는 장점을 지닌 반면. 비효율적이고 일방적인 정보전달로 인한 의견교류의 어려움을 나타냈다. 토론게시판을 이용한 경우는 여러 학습자와의 자료 공유 및 전체공지가 용이하며 조원 간의 적극적인 참여가 가능하다는 긍정적인 답변과 더불어 정확한 의사전달의 어려움. 게시물의 중복 등이 문제점으로 제기되었다. 따라서 매체별 특성에 따라 학습자 간의 이용의도 및 경험에 차이가 나타남을 알 수 있었다.

• Educational Technology International
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• 제9권2호
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• pp.97-121
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• 2008

The purposes of the present study are to describe the design of mobile learning scenarios based on learning sciences theories, and to discuss implications for the future research in this area. To move beyond mere speculations about the abundant possibilities of mobile learning and to make real impact in K-12 school settings, it is critical to conduct school-based research grounded on the learning sciences theories. Towards this end, this paper describes school-based mobile learning projects conducted by a research team at the Learning Sciences Lab in Singapore, and then discusses the possibilities and challenges of mobile learning to further inform future research. Specifically, this paper explores the affordances of mobile technology, such as portability, connectivity and context-sensitivity, to design seamless learning scenarios that bridge formal and informal learning experiences. The authors present a framework for re-conceptualizing different types of learning based on physical settings and intentionality, and then describe two seamless learning scenarios, namely 3Rs and Chinatown Trail, which were implemented in one primary school in Singapore. In conclusion, the authors discuss the affordances of seamless mobile learning for enhancing one's lived experiences to build a living ecological relationship between the person and the environment, and how mobile technology can play a critical role for enabling such lived experiences.

• Journal of Radiation Protection and Research
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• 제46권4호
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• 공학교육연구
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• 제21권4호
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• pp.28-34
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• 2018

Design class through team activities is increasing in engineering education. Team-based education has been known to improve students' creativity, problem solving ability, cooperative ability, self-directed learning ability, and communication ability. How to organize a team is an important issue that affects the performance of team activities as well as student satisfaction. However, previous studies have focused on the causal relationship between team formation and the team's performance. This paper deals with how to organize a balanced team in a real class. When the basic characteristic values of students are givens, the aim is to make the sum of the characteristic values as fair as possible for each team. We propose a mathematical team formation model and show how to apply it through case studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• 한국간호교육학회지
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• 제21권1호
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• pp.119-128
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• 2015

Purpose: This study was conducted to evaluate the effectiveness of an evidence-based nursing (EBN) course using action learning-based team learning in undergraduate nursing students. Methods: A quasi-experimental pretest-posttest control group design was employed. The participants who consented were 45 second-year nursing students (22 in the experimental, 23 in the control group) from a university in G-city, Korea. The intervention included lectures, practicals, team activities and reflection on overviewing EBN, formulating clinical questions, searching the evidence, and criticizing the research articles. At the beginning and the end of the 7-week EBN course, the participants completed self-reported questionnaires. Frequencies, $x^2$-test, t-test, and ANCOVA with the SPSS program 18.0, were used to analyze the data. Results: The experimental group showed significantly higher scores on EBN competency (F=25.80, p<001), information literacy (F=13.75, p=.001), and proactivity in problem solving (F=5.32, p=.026) than the control group. Conclusion: This study provides evidence that an EBN course improves undergraduate nursing students' EBN competencies, information literacy, and proactivity in problem solving. Team learning in EBN education can be an effective teaching strategy.