• 한국방사선학회논문지
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• 제16권5호
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• pp.611-618
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• 2022

본 연구는 COVID-19 상황 속에서 온라인 수업을 경험한 방사선(학)과 대학생들을 대상으로 수업 중 이루어지는 딴짓의 정도를 확인하고 자기주도적 학습역량, 몰입과 학업성취도가 딴짓에 미치는 영향을 규명하기 위함이었다. 연구방법은 서술적 조사연구이며, 연구 대상은 방사선(학)과 학생들로 구조화된 설문지를 이용하여 2022년 6월 1일부터 6월 30일까지 조사하여 200부를 최종 분석 대상으로 하였다. 수집된 자료는 SPSS/WIN 21.0을 이용하여 척도의 신뢰도를 검증하고, 딴짓을 유형을 파악하고 한 후 일반적 특성, 딴짓, 자기주도적 학습역량, 몰입과 학업성취도에 대하여 기술통계, 분산분석, Pearson's 상관관계 그리고 다중회귀분석을 실시하였다. 연구결과 대상자들의 딴짓은 '딴 생각하기', '문자하기' 순이 였으며 딴짓은 1.75점, 자기주도적 학습능력은 3.60점, 몰입은 3.23점, 학업성취도는 4.29점으로 나타났다. 딴짓과 자기주도적 학습역량, 몰입과 학업성취도 간에는 유의한 음의 상관관계가 있었으며, 딴짓에 미치는 영향을 미치는 요인은 학업성취도, 나이, 몰입, 자기주도적 학습능력 순이었다. 이상의 결과로 방사선(학)과 대학생들의 딴짓을 낮출 수 있는 방법이 학업성취도를 높이는 것임으로 수업에 몰입할 수 있는 구체적 방안 및 다양한 교수법이 필요할 것으로 기대된다.

• 아시아태평양코퍼스연구
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• 제5권1호
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• pp.23-36
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• 2024

This research centers on the Taiwan Eye-Movement Corpus of Spanish (TECS), a specially created corpus comprising eye-tracking data from Chinese-speaking learners of Spanish as a third language in Taiwan. Its primary purpose is to explore the broad utility of TECS in understanding language learning processes, particularly the initial stages of language learning. Constructing this corpus involves gathering data on eye-tracking, reading comprehension, and language proficiency to develop a machine-learning model that predicts learner behaviors, and subsequently undergoes a predictability test for validation. The focus is on examining attention in input processing and their relationship to language learning outcomes. The TECS eye-tracking data consists of indicators derived from eye movement recordings while reading Spanish sentences with temporal references. These indicators are obtained from eye movement experiments focusing on tense verbal inflections and temporal adverbs. Chinese expresses tense using aspect markers, lexical references, and contextual cues, differing significantly from inflectional languages like Spanish. Chinese-speaking learners of Spanish face particular challenges in learning verbal morphology and tenses. The data from eye movement experiments were structured into feature vectors, with learner behaviors serving as class labels. After categorizing the collected data, we used two types of machine learning methods for classification and regression: Random Forests and the k-nearest neighbors algorithm (KNN). By leveraging these algorithms, we predicted learner behaviors and conducted performance evaluations to enhance our understanding of the nexus between learner behaviors and language learning process. Future research may further enrich TECS by gathering data from subsequent eye-movement experiments, specifically targeting various Spanish tenses and temporal lexical references during text reading. These endeavors promise to broaden and refine the corpus, advancing our understanding of language processing.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.816-821
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• 2003

Researchers have utilized artificial evolution techniques and learning techniques for studying the interactions between learning and evolution. Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors. We investigate the effects of learning in evolutionary process by comparing the performance of the proposed realtime evolutionary learning method with that of evolutionary method only. Also, we investigate an interactive evolutionary algorithm to overcome the difficulties in evaluating complicated tasks.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 컴퓨터소사이어티 추계학술대회논문집
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• pp.137-140
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• 2003

This paper proposes a pursuing system utilizing the learning method where multiple cooperating agents emulate social behaviors of animals and insects and realize their group behaviors. Each agent contains sensors to perceive other agents in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used fir behavior decision controller. The input of the neural network is decided by the existence of other agents and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation.

• Advances in nano research
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• 제15권6호
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• 한국과학교육학회지
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• 제21권3호
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• pp.487-496
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• 2001

본 연구에서는 중학교 과학 수업에 협동학습을 실시하여 학생들 사이에서 발생하는 언어적 행동 유형을 범주화하고 이들과 학습 전략의 사용, 학습 동기, 태도 증진과의 관계를 조사하였다. 또한 협동학습에 대한 학생들의 인식을 성취 수준별로 조사하였다. 협동학습 과정에서 나타나는 언어적 행동 유형들은 점검 및 조직화 전략의 사용이나 자아효능감, 과학 수업에 대한 태도의 증진과 많은 관련이 있었다. 협동학습에 대한 인식 조사 결과, 중위와 하위 수준 학생들은 전반적으로 협동학습에 대하여 긍정적으로 인식하였으나, 상위 수준 학생들에서는 부정적인 인식도 나타났다. 특히, 학습의 효과성 측면에 있어서 중위 및 하위 수준 학생들의 경우 동료와의 언어적 상호작용을 통해 학습 내용에 대하여 더 잘 알게 되었다는 인식이 많았던 것에 반해 상위 수준 학생들은 배웅의 깊이가 작다고 지적하였다.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.157-163
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• 2023

The attack technique targeting end-users through phishing URLs is very dangerous nowadays. With this technique, attackers could steal user data or take control of the system, etc. Therefore, early detecting phishing URLs is essential. In this paper, we propose a method to detect phishing URLs based on supervised learning algorithms and abnormal behaviors from URLs. Finally, based on the research results, we build a framework for detecting phishing URLs through end-users. The novelty and advantage of our proposed method are that abnormal behaviors are extracted based on URLs which are monitored and collected directly from attack campaigns instead of using inefficient old datasets.

• 한국간호교육학회지
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• 제12권1호
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• pp.86-94
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• 2006

Purpose: The purpose of this study is to identify the nursing student's e-Learning status. Method: The 48 nursing students were subject to this study. They were introduced to use 'understanding of ABGA' site. And the web log data was analysed. Result: General learning status in nursing education, difference of learning status in each learning type, and 'quiz' area learning status were analysed to see the nursing student's e-Learning status. The result of study showed that the participants didn't get learning that site designer had in mind to give them. Conclusion: It is important to figure out students' actual learning behaviors and reactions of feedbacks. Also, web log data could provide useful data that affect student's behaviors. Based on this study result, the following is suggested. The way to give them effective learning should be considered by the instructor who knows the unsincere type students through web log data.

• 가정과삶의질연구
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• 제29권4호
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• pp.89-107
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• 2011

The purpose of this study was to develop Parent Education Program for Learning Coaching which enhances parent's learning-support behaviors, as well as, children's self-Efficacy and self-regulated learning capability, and to implement and evaluate the program for the families with school aged children. The results of this study were as follows. First, the contents of the experimental model of 'Parent Education Program for Learning Coaching' were specified as five factors namely; offering options, offering democratic rules, pursuing appropriate results, offering school-related information, offering self-regulated learning skills for children. Second, significant differences in the experiment group were found in pre- and post-test scores of parent's learning-support behaviors and children's self-efficacy and self-regulated learning capability, but not for the control group. Thus, based on these findings, a modified model of 'Parent Education Program for Learning Coaching' was presented as a conclusion.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.107-114
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• 2023

The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.