• Journal of Korean Elementary Science Education
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• v.29 no.4
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• pp.389-400
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• 2010

This study is focused on analyzing effects of Self-regulated learning on Academic self-regulation, Science achievement and Science Related Affective Domains. The subjects of this study were sampled from fifth grades of a elementary school in Seoul, 61 students. One class (31 students) out of selected two classes was applied to Self regulated learning Teaching Model, the other (30 students) took conventional methods of teaching. The experiment proceeded for 21 weeks, 51 times of classes. According to the results of this study, Self-regulated learning improved the children's Academic self regulation ability. Self-regulated learning improved the children's science achievement. Self-regulated learning improved the children's Science Related Affective Domains. Furthermore, six distinct dimensions of Academic self-regulation have correlation with scientific attitudes, interests of Science Related Affective Domains.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.366-372
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• 2018

The purpose of this study was to investigate the effects of educational evaluation with the application of flipped learning on undergraduate students' self-directed learning ability (cognitive domains, motive domains, conductive domains) and cognitive learning competency (knowledge and thought, creation, problem solving). An educational evaluation class, which applied flipped learning designed on the basis of pre-class, in-class, and post-class, was taught to 57 undergraduate students for twelve of the sixteen weeks of a semester. It was held each week on Thursdays for two (Ed- I don't understand 'for two'). The study results showed that, applying the flipped learning educational education class for undergraduate students improved self-directed learning ability (motivation domains, behavior domains) and cognitive learning competence (higher order thinking, metacognition, creativity tendency, problem-solving process). This study provides meaningful suggestions on exploring instructional design and effective teaching and learning methods applied to flipped learning.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.153-176
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• 1994

In this paper we introduce machine learning theory to business domains for business decisions. First, we review machine learning in general. We give a new look on a previous framework, version space approach, and we introduce PAC (probably approximately correct) learning paradigm which has been developed recently. We illustrate major results of PAC learning with business examples. And then, we give a theoretical analysis is decision tree induction algorithms by the frame work of PAC learning. Finally, we will discuss implications of learning theory toi business domains.

• Convergence Security Journal
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• v.23 no.5
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• pp.65-72
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• 2023

This paper proposes a method for detecting malicious domains considering human habitual characteristics by building a Deep Learning model based on LSTM (Long Short-Term Memory). DGA (Domain Generation Algorithm) malicious domains exploit human habitual errors, resulting in severe security threats. The objective is to swiftly and accurately respond to changes in malicious domains and their evasion techniques through typosquatting to minimize security threats. The LSTM-based Deep Learning model automatically analyzes and categorizes generated domains as malicious or benign based on malware-specific features. As a result of evaluating the model's performance based on ROC curve and AUC accuracy, it demonstrated 99.21% superior detection accuracy. Not only can this model detect malicious domains in real-time, but it also holds potential applications across various cyber security domains. This paper proposes and explores a novel approach aimed at safeguarding users and fostering a secure cyber environment against cyber attacks.

• Journal of IKEEE
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• v.27 no.4
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• pp.537-547
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• 2023

In many domains, lack of data inhibits adoption of advanced machine learning models. Recently, Few-Shot Learning (FSL) has been actively studied to tackle this problem. Utilizing prior knowledge obtained through observations on related domains, FSL achieved significant performance with only a few samples. In this paper, we present a survey on FSL in terms of data augmentation, embedding and metric learning, and meta-learning. In addition to interesting researches, we also introduce major benchmark datasets. FSL is widely adopted in various domains, but we focus on image analysis in this paper.

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.1029-1060
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• 2014

The purpose of this study are to figure out whether there are the meaningful differences between learner's learning achievements and contents preference in accordance with the delivery strategies (instructor-focused model, learner-focused model) of learning materials suggested by Bloom in web-based instruction, and to suggest the various options on the contents delivery strategies to improve the learner's learning achievements of each learning domains. Learning domains were divided by the cognitive domain, the affective domain, and the psychomotor domain. The result of research with 182 learners showed that learner-focused model in the cognitive domain caused higher learning achievements and preference than instructor-focused model. And instructor-focused model in the psychomotor domain compared with learner-focused model caused higher learning achievements and preference. However, there were less meaningful differences in the affective domain. In other words, learner-focused model is appropriate to the feature of the cognitive domain while instructor-focused model is appropriate to the feature of the psychomotor domain. The results suggest that delivery strategies should be chosen by domains of learning contents in order to improve learner's learning achievements in web-based instruction. Learner-focused delivery strategies in the cognitive domain and instructor-focused delivery strategies in the psychomotor domain need to be considered positively. Delivery strategies should be studied and developed in order to lead higher learning achievements and preference.

• East Asian mathematical journal
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• v.40 no.2
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• pp.149-166
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• 2024

This study is to establish the domains and the standards of instructional evaluation on the teacher knowledge dealing with the knowledge of 'teaching and learning methods and assessment'. Especially, in this study, the instruction assessment standards are developed focused on the five types of mathematics competencies such as problem solving, communication, reasoning, connection, information and handling, which were emphasized in the mathematical curriculum revised in 2022. By the result, ten domains such as an instruction involving instruction goal and content, problem-solving competency, data treatment competency, learners' achievement level and attitude, communication competency, reasoning competency, connection competency, the assessment method and procedure based on the competency, the assessment tool development based on the competency, assessment result based on the competency were new established. According to those domains, the total 20 instructional evaluation standards were developed. This study is limited to consider the domain of 'teaching and learning methods and assessment' among the domains of teacher knowledge, while dealing with the elements of mathematics competencies in the standards. However, instructional evaluation standards reflecting these competencies should be developed in the other diverse domains of teacher knowledge.

• The Korean Journal of Emergency Medical Services
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• v.25 no.2
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• pp.157-167
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• 2021

Purpose: The study was a qualitative study to examine the synchronous and asynchronous distanced learning experience of online paramedic students during the COVID-19 pandemic. Methods: The subjects included 10 students enrolled in the department of emergency medical service at J City C University. Written consent was provided by the subjects prior to the study, and focus group interviews were then conducted with sufficient explanation. The interviews were recorded and were directly transcribed immediately after the interview. Research results were then derived through content analysis. Results: A total of 4 domains and 9 categories were derived from the experiences of paramedic students on distanced learning. The 4 domains included "distanced lectures type," "student's adaptation and non-adaptation," "change of evaluation," and "learning anxiety." Conclusion: Contents of each domain derived from this study are expected to be used as basic data for the design of the distanced learning in the future.

• The Journal of Korean Academic Society of Nursing Education
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• v.26 no.3
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• pp.238-247
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• 2020

Purpose: The purpose of this study was to identify the relationship between intrinsic motivation and learning outcomes of nursing college students who took the Fundamentals of nursing as a problem-based learning method. Methods: In this cross-sectional study, we identified the intrinsic motivations of 114 nursing students who completed problem-based learning using the Intrinsic Motivation Inventory. The t-test was conducted to identify differences according to intrinsic motivation, and correlation analysis was performed to confirm the relationship between intrinsic motivation and learning outcomes. Results: The group with higher intrinsic motivation showed higher scores in all domains of self-assessed learning outcomes than the lower group. It was the 'Relatedness with an instructor' that showed the highest correlation with the learning outcomes in the domains of intrinsic motivation. Conclusion: Problem-based learning is an effective learning method for cultivating the competencies needed for nurses. The intrinsic motivation of students is an important factor in the performance of problem-based learning. For the efficiency of problem-based learning, efforts should be made to develop and apply autonomy-supportive interventions that can enhance intrinsic motivation.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.