• Journal of the Korean Society of Earth Science Education
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• v.5 no.1
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• pp.75-87
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• 2012

The purpose of this study is to find out the effects of internet resource-based problem-based learning on the academic achievement in science and the attitude toward science of elementary school students. One experiment class and one control class of grade 6 students were selected to perform a prior investigation on the academic achievement in science and the attitude toward science, then the experiment class attended 4 weeks of lessons that was applied the internet resource-based problem-based learning, and the control class attended the traditional lessons based on the guidelines of teachers. After conducting lessons, a post investigation was performed for each class and the results were analyzed to produce the following conclusions. First, the internet resource-based problem-based learning could be seen to be effective in improving the students' academic achievements in science. The internet resource-based problem-based learning seemed to make students recognize the lesson details better and grasp well the questions given during lessons from the process of finding solutions among many informations and data on the internet. Second, the internet resource-based problem-based learning had a positive effect on all attitudes' areas toward science of students. It looked like that the internet resource-based problem-based learning taught the students to use the internet resources and gave them a friendly feeling, so the children could actively participate in class and had positive recognition on science. Third, from teacher observation and the result of the student recognition investigation, we could know that the students showed lots of interests in the internet resource-based problem-based learning, and they were able to understand the scientific theories in the process of solving problems that were relevant to real life, and thought science in a positive way.

• Journal of Korean Society of Archives and Records Management
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• v.8 no.1
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• pp.143-165
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• 2008

This study is concerned with educational services provided for archives with a focus on programs for teachers and students in the classroom. The purpose of this study is to develop the archival-resource based learning model. And the other purpose is to find out the influence of the archival-resource based learning. The researcher and teachers designed two lessen plans for archival-resource based learning and general learning. To compare with the archival-resource based learning and the general learning, the researcher divided into two comparison classes of 6th graders of two elementary schools. Statistical analysis was conducted by analysis of covariance using SPSS WIN 12.0 for t-test.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.310-315
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• 2001

We are focusing on an approach which handle a general Web as a resource in order to support self-directed learning for a student. Then, we are developing a Web based learning environment "Web-Retracer"for utilizing Web as teaching materials by a user′s Annotation. Although the learner can share the Web resource that the others utilized in this environment, Web resources unsuitable for a student′s needs becomes hindrance about her/his self-directed learning. In this paper, we propose a recommending method of the resource united with a student′s needs on the basis of a student′s learning and Web browsing history. This method analyzed the feature peculiar to a resource, and extracts the resource with which the needs of the feature and a student agreed.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.97-105
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• 2016

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.

• International Commerce and Information Review
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• v.12 no.1
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• pp.271-295
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• 2010

In this study, we investigate the relationships among organizational market orientation, learning orientation, information technology(IT) support for firm resource, IT support for strategy, and balanced scorecard(BSC) performance in export firms. The development of the research model is based on the empirical studies of strategy and resource-based view. The data from the survey was analyzed using Partial Least Squares(PLS). The results from the empirical model suggest that IT support for firm resource is effected by market orientation and learning orientation. And, IT support for strategy is enhanced by IT support for firm resource. Finally, BSC performance of export firms is effected by IT support for strategy.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.205-215
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• 2019

Researchers have confirmed the relationship between ambidexterity learning and innovation performance, but according to the resource-based theory, the relationship between ambidexterity learning and innovation performance is also affected by the internal resources of the organization. Internal resources are an important factor affecting the transformation of learning outcomes into performance. In addition, few scholars have pointed out whether different types of learning have different effects on different types of innovation performance. This study collects data from 170 High-tech enterprises in Shandong, china, and discusses the effects of exploitative learning and explorative learning on management innovation performance and technological innovation performance. This study further examines the moderating role of slack resource on the relationship between ambidexterity learning and innovation performance. Results show that ambidexterity learning has positive effect on innovation performance. Compared with exploitative learning, explorative learning has a greater impact on management innovation performance; compared with explorative learning, exploitative learning has a greater impact on technological innovation performances. Slack resource has positive moderating role between the relationship of exploitative learning, explorative learning and technology innovation performance. But Slack resource has no moderating role between the relationship of exploitative learning, explorative learning and management innovation performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.153-162
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• 2016

This paper proposes an optimal dynamic resource allocation method in IoT (Internet of Things) parking guidance system using Q-learning resource allocation model. In the proposed method, a resource allocation using a forecasting model based on Q-learning is employed for optimal utilization of parking guidance system. To demonstrate efficiency and availability of the proposed method, it is verified by computer simulation and practical testbed. Through simulation results, this paper proves that the proposed method can enhance total throughput, decrease penalty fee issued by SLA (Service Level Agreement) and reduce response time with the dynamic number of users.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.209-225
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• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.8-14
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• 2023

The majority of IoT devices already employ AIoT, however there are still numerous issues that need to be resolved before AI applications can be deployed. In order to more effectively distribute IoT edge resources, this paper propose a machine learning-based approach to managing IoT edge resources. The suggested method constantly improves the allocation of IoT resources by identifying IoT edge resource trends using machine learning. IoT resources that have been optimized make use of machine learning convolution to reliably sustain IoT edge resources that are always changing. By storing each machine learning-based IoT edge resource as a hash value alongside the resource of the previous pattern, the suggested approach effectively verifies the resource as an attack pattern in a distributed AIoT context. Experimental results evaluate energy efficiency in three different test scenarios to verify the integrity of IoT Edge resources to see if they work well in complex environments with heterogeneous computational hardware.