• The Journal of Information Systems
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• v.32 no.4
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• pp.189-210
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• 2023

Purpose This study aims to investigate factors that positively influence the continuous Artificial Intelligence(AI) Learning Continuity of business major students. Design/methodology/approach To evaluate the impact of AI education, a survey was conducted among 119 business-related majors who completed a software/AI course. Frequency analysis was employed to examine the general characteristics of the sample. Furthermore, factor analysis using Varimax rotation was conducted to validate the derived variables from the survey items, and Cronbach's α coefficient was used to measure the reliability of the variables. Findings Positive correlations were observed between business major students' AI Learning Continuity and their AI Interest, AI Awareness, and Data Analysis Capability related to their majors. Additionally, the study identified that AI Project Awareness and AI Literacy Capability play pivotal roles as mediators in fostering AI Learning Continuity. Students who acquired problem-solving skills and related technologies through AI Projects Awareness showed increased motivation for AI Learning Continuity. Lastly, AI Self-Efficacy significantly influences students' AI Learning Continuity.

• Research in Mathematical Education
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• v.17 no.2
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• pp.133-152
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• 2013

In this research, we investigated students' understanding of the definitions of sequence of continuous functions and its uniform convergence. We selected three female and three male students out of the senior class of a university and conducted questionnaire surveys 4 times. We examined students' concept images of sequence of continuous functions and its uniform convergence and also how they approach to the right concept definitions for those through several progressive questions. Furthermore, we presented some suggestions for effective teaching-learning for the sequences of continuous functions.

• Korean Medical Education Review
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• v.11 no.1
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• pp.11-20
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• 2009

The purpose of this study was to introduce the concepts of experiential learning and the Kolb's model, and to review some applications of experiential learning theory in graduate medical education. The published literature on GME and education for general practitioners applying the experiential theory and the Kolb's model was reviewed. Experience learning defined the cyclical learning process which emphasizes the learners' reflective thinking of the learners' concrete experiences and their active participation in continuous learning actives. Kolb includes this 'cycle of learning' as a central principle in his experiential learning theory. This is typically expressed as a four-stage cycle of learning. Kolb's cycle moves through concrete experience(CE), reflective observation(RO), abstract conceptualization(AC) and active experimentation(AE). Components of continuing education of the adult learner were based on autonomy, context of learning, and competence and performance as educational objectives. Some strategies for graduate medical education were reflective thinking, self-directed learning, morning reporting and feedback with peer review, etc. Opportunities for learning from experience in practical life can be made to enhance reflective thinking and performance of practitioners. Strategies to develop reflective practice among physicians should be explored by further research.

• Journal of Digital Convergence
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• v.3 no.2
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• pp.143-157
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• 2005

Many papers point out that the e-Learning is one of the most important industries, and the effect on other industries can be more powerful than any other business. Therefore, we think about social, cultural, industrial and technological effect of the e-Learning in order to enlarge industry scale as well as educational performances. In many cases of developed countries, various kinds of study have been performed for the e-Learning quality assurance because quality of the e-learning should operate on effective and efficient learning and continuous market development of education industries. The e-Learning quality assurance has import function not only for learning contents reusability like a SCORM and metadata but also for learning system, solution and service operation, so activities for the quality assurance should consider of cultural and tactical approach when it is applied in the e-learning business. In this paper, we present the concept, domain and purpose of the e-Learning quality assurance. Furthermore, this paper proposes the process and methodology in order to make the quality assurance standard model which is consist of 6 phase such as Environment Research, Needs Analysis, Framework, Metrics, Development and Implementation, Evaluation and Feedback through the analysis and comparison of pre-studied worldwide quality control, management and assurance documents.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.155-170
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• 2015

This study aims to investigate the Learning and fun aspects of dance games by examining their instructive values. Dance Central 2 was analysed with a focus on its educational functions by observing dance moves. Therefore this study looked into the game's educational elements that create learning effects and fun elements that attract continuous and voluntary participation. Analysis was implemented based on the physical, cognitive, and emotional aspects for Learning elements and the aspects of graphics, sound, video, rewards, target-setting, difficulty levels, and control for fun elements. As a result, it was found that the practice and performance modes of Dance Central 2 have elements that can improve players dance skills. Of course, there may be difference in players' participation and effects depending on their goals and objectives. Consequently, the continuous use of the dance game supported by player's dance skills and commitment will improve learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.15-23
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• 2022

Among the learning methods for Continuous Learning environments, "Learning without Forgetting" has fixed regularization strengths, which can lead to poor performance in environments where various data are received. We suggest a way to set weights variable by identifying the features of the data we want to learn. We applied weights adaptively using correlation and complexity. Scenarios with various data are used for evaluation and experiments showed accuracy increases by up to 5% in the new task and up to 11% in the previous task. In addition, it was found that the adaptive weight value obtained by the algorithm proposed in this paper, approached the optimal weight value calculated manually by repeated experiments for each experimental scenario. The correlation coefficient value is 0.739, and overall average task accuracy increased. It can be seen that the method of this paper sets an appropriate lambda value every time a new task is learned, and derives the optimal result value in various scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.365-380
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• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.

• Journal of Korean Elementary Science Education
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• v.20 no.2
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• pp.165-175
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• 2001

The study aimed to examine the effects of cooperative learning in science teaching on scientific knowledge, inquiry ability, and science - related attitudes. This study has carried out LT strategy. The cooperative learning was applied to the experimental group for 8 weeks with worksheets developed for this study, The results were statistically analyzed by SPSS. The results are as follows: Students of cooperative learning made significant progress in scientific knowledge(p<.05), inquiry ability(p<.01), and science - related attitudes(p<.05). The students in the cooperative classes have shown that the cooperative learning was good for causing interest. inducing active participation, and helping students' learning. and students showed strong wish for the continuous cooperative loaming.

• Journal of Educational Research in Mathematics
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• v.9 no.2
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• pp.439-457
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• 1999

In this paper, we tried to find out what makes the students feel anxious to mathematics and the ways to decrease their anxiety by comparing two learning types, self-directed-learning and learning in a body, by means of continuous observation and interviews. To perform this study, two classes of self-directed-learning and other two classes of learning-in a body were chosen from the third year-students in Wando Middle School in Chollanamdo. In this study, we obtained the following results: 1. In high group in math grade, students in self-directed-learning are less anxious than students in learning-in a body. 2. In average group in math grade, students in self-directed-learning are much more anxious than students in learning-in a body. 3. In low group in math grade, both students in self-directed-learning and students in teaming-in a body feel anxious about math and there is no difference between them. 4. Anxiety about math hove positive influence on high group in math grade. 5. Anxiety about math have negative influence on average and low groups in math grade. Especially, low group students had no interests about mathematics because of their math anxiety. We observed that some students got over the math anxiety to some meaningful extent by means of interviews or appropriate advices, and became to have confidence and interests in mathematics.

• International conference on construction engineering and project management
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• 2020.12a
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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.