• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.611-618
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• 2022

The purpose of this study was to confirm the degree of other behaviors among university students in the department of radiology(science) who experienced online classes in the COVID-19 situation and to investigate the effect of self-directed learning ability, flow and academic achievement on other behaviors. The research method was descriptive research. Data were 200 students collected from June 1 to 30 in 2022 using structured questionnaires as students in the Department of Radiology(science). Collected data were analyzed using descriptive statistics, t-test, ANOVA, Cronbach's pearson's correlation, multiple regression analysis with SPSS/WIN 21.0. The result of the study showed that the other behaviors were in the order of 'having s different thought, and 'sending text messages'. other behaviors was 1.75, self-directed learning ability was 3.60, flow was 3.23 and academic achievement was 4.29. There was a significant negative correlation between other behaviors and self-directed learning ability, flow, academic achievement. Factors influencing other behaviors were academic achievement, age, flow, self-directed learning ability in that order. As a result of the above research. it is expected that specific measures and various teaching methods to be flowed in the class are need as the way to lower the other behaviors of university students in the Department of Radiology(science) is to increase academic achievement.

• Asia Pacific Journal of Corpus Research
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• v.5 no.1
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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.

• Proceedings of the KSME Conference
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• 2003.04a
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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.

• Proceedings of the IEEK Conference
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• 2003.11b
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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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• v.15 no.6
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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.

• Journal of The Korean Association For Science Education
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• v.21 no.3
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• pp.487-496
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• 2001

In a 7th graders' cooperative science class, verbal behaviors were categorized and their relationships with the improvement of learning strategies used. motivation, and attitudes were investigated. Students' perceptions of cooperative learning were also studied by the achievement level. Verbal behaviors in cooperative learning were positively related with the improvement of monitoring and organization strategies used, self-efficacy, and attitude toward science class. In the analyses of students' perceptions of cooperative learning, medium- and low-achieving students had positive perceptions but some high-achieving students had negative ones. In the aspect of effectiveness of cooperative learning, especially, medium- and low-achieving students perceived that they could learn more and better due to verbal interactions with peers. To be contrary, high-achieving students perceived that they learned less and superficially.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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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.

• The Journal of Korean Academic Society of Nursing Education
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• v.12 no.1
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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.

• Journal of Families and Better Life
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• v.29 no.4
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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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• v.23 no.12
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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.