• Journal of the Korean Institute of Educational Facilities
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• v.27 no.3
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• pp.3-14
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• 2020

The purpose of this study is to discuss the planning direction of educational spaces to support children's healthy and creative learning based on bio_philic theory. This study analyzed the characteristics of the application of biophilic patterns in children's education space through case analysis. The conclusion of this study is summarized as follows. As a result of the analysis of children's classroom space, the pattern of 'A(Visual connection with nature), F(Dynamic & Diffuse Light), K(Prospect)' shows high application rate, but the pattern of 'C(Non-Rhythmic Sensory Stimuli), G(Connection with Natural Systems), I(Material Connection with Nature)' shows low application rate. In particular, there is a lack of connection with patterns such as hearing, smell, touch, taste stimulation and water experience, and curiosity through exploration of nature about 'B(Non-visual connection with nature), E(Presence of Water), N(Risk/Peril)' changes in nature and ecosystem. In the corridor and rest space, the pattern of 'A(Visual connection with nature), D(Thermal & Airflow Variability), F(Dynamic & Diffuse Light), G(Connection with Natural Systems), K(Prospect)' shows high application rate, but 'B(Non-visual connection with nature)' shows low application rate. In addition, the application of patterns related to the stimulation of curiosity through direct exploration of nature and the exploration of the patterns of 'E(Presence of Water), N(Risk/Peril)' is insufficient. Therefore, in the case of classroom spaces, the active use of nature as it is should be considered within the scope that does not cause visual confusion, and it should provide an area that can be experienced through the five senses. And corridors and rest spaces should be designed to introduce more active natural elements as spaces to recover stress caused by learning. In other words, the characteristics of children's education facilities need to be connected between classroom space, corridor, rest space and external space. This study is meaningful in that it analyzes and derives the application characteristics of 'biophilic design' which affects the 'Attention Restoration' of children's educational spaces through foreign cases.

• Asia pacific journal of information systems
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• v.32 no.1
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• pp.151-167
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• 2022

This paper is motivated by social influence theory implying the multilevel nature of knowledge management (KM) in an organization. Organizational knowledge is generated and distributed by individuals from different groups across organizational boundaries. Its transfers are supported by IS/IT practice, i.e., the individual and collective use of the technology available in the organization. I propose a multilevel perspective to explain how IS/IT practice supports multilevel KM capabilities to manage organizational knowledge successfully and how the effectiveness of multilevel KM capabilities expands into the improvement of multilevel task-related organizational performance. The multilevel KM theory extends the knowledge-based view of the firm by describing the dynamic process through which strategic values of knowledge are generated by IS/IT practice across the organizational levels. This paper also discusses multilevel insights on the strategic value of organizational learning based on the social context of organizations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.4
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• pp.815-825
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• 2007

The existing web-based self-directed learning systems are in short for the ability of learning skills assessment. Even worse, hey only give test scores as an indicate for test skills, which is also not a good measure for learning skills assessment and makes it difficult to assess learning skills objectively and to present clear assessment criterion. In this paper, we proposed an improved self-directed learning system using fuzzy logic, which can be controlled by learners themselves and helps to evaluate their on learning process. We also implemented the system on the written examination of Engineer Information Processing. The purposed system lust calculates membership functions of learning tine, learning frequency, testing time, and test score. Using them the final membership functions of learning and test skills are calculated and presented in a graphical, i.e. mon understandable, way to user. The purposed system helps learners to assess their achievement and to plan future schedule, and the survey result on the students used the system also supports that.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2895-2912
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• 2014

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.

• Journal of The Korean Association of Information Education
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• v.10 no.1
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• pp.143-152
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• 2006

The aim of this study was to analyze effects of a blended learning program based on case study approach and to suggest implications in appropriately evaluating blended learning in practices for corporate education and training. In order to achieve the goal, issues such as the ones related to blended learning including development and status quo of blended learning programs in the field of corporate education and training and operation models for the blended learning were reviewed. Then, the outcomes of a blended learning program were completely analyzed through systems approach. The methodology of the study was a mixed research method which was comprised of quantitative and qualitative approaches. The results of quantitative analysis showed that blended learning itself seemed to have significant effects on the leadership capability in general assessment and self assessment. The most viable effects of blended learning in leadership training are said to be actual change of actions and activities in leadership capability of the participants followed by changes in their job tasks contributing to improving the managerial performance of the company, good transfer to current job tasks, and implementation of the practice plans.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.263-283
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• 2024

Users' comments after online shopping are critical to product reputation and business improvement. These comments, sometimes known as e-commerce reviews, influence other customers' purchasing decisions. To confront large amounts of e-commerce reviews, automatic analysis based on machine learning and deep learning draws more and more attention. A core task therein is sentiment analysis. However, the e-commerce reviews exhibit the following characteristics: (1) inconsistency between comment content and the star rating; (2) a large number of unlabeled data, i.e., comments without a star rating, and (3) the data imbalance caused by the sparse negative comments. This paper employs Bidirectional Encoder Representation from Transformers (BERT), one of the best natural language processing models, as the base model. According to the above data characteristics, we propose the F_MixBERT framework, to more effectively use inconsistently low-quality and unlabeled data and resolve the problem of data imbalance. In the framework, the proposed MixBERT incorporates the MixMatch approach into BERT's high-dimensional vectors to train the unlabeled and low-quality data with generated pseudo labels. Meanwhile, data imbalance is resolved by Focal loss, which penalizes the contribution of large-scale data and easily-identifiable data to total loss. Comparative experiments demonstrate that the proposed framework outperforms BERT and MixBERT for sentiment analysis of e-commerce comments.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.481-500
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• 2010

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

• Journal of Information Technology Services
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• v.18 no.3
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• pp.143-154
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• 2019

The purpose of this study is to explore innovative adaptation of IT systems worldwide to support process-focus assessment. To this end, the study presents four cases from Australia, Singapore, the U.S.A., and Korea to inform educational policy and technology researchers and practitioners. Based on comparing the four chosen cases as benchmarks, the study concluded that IT systems and technologies can expedite and improve school interventions to enhance student learning in terms of time and quality. Also, educational big data and learning analytics can be used to systematically monitor and communicate individual student's progress among school stakeholders (i.e., teachers, students, parents, and administrators). Lastly, the study made some suggestions to support process-focused assessment in schools as following : 1) A more evidence-based, systems approach is needed to integrate the curriculum, instruction, and assessment to bridge the gap between educational policy and school practice; 2) It is critical to create ICT-friendly school environments so that meaningful data could be collected, analyzed, and stored from individual students and school units; 3) Teacher professional development is another area that needs special considerations and support to successfully implement process-focused assessment in schools.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.