• Journal of the Korean Chemical Society
• /
• v.66 no.1
• /
• pp.42-49
• /
• 2022

Many elementary school teachers' lack of scientific knowledge reveal as several problems in teaching science. Thus, elementary school teachers need to improve their scientific knowledge, but there is the limit to improve the teachers' scientific knowledge through activities based on lecture that conducted in process of training and retraining them. Therefore, Improvement for training science teacher to improve scientific knowledge of elementary school teachers would be searched in this study. Depth interview was conducted toward three elementary school teachers, who had conducted action research, to improve content knowledge of material domain within teaching process and elementary school science. Based on result of the interview, difficulty and development that the three teachers were commonly experienced in process of conducting scientific inquiry in action research were analyzed. One of the difficulties of the inquiry were to figure out how the three teachers, who participated in the interview, understand specific concept, what they do not understand, and what they should study more to understand the concept. And there was a circumstance that the teachers did not know how to apply procedural knowledge, which learned explicitly in the process of setting plan for conducting research, into real context. Since there was difference between knowledge that they understand disjunctively and context that observed in real circumstance, they faced difficulty. However, the teachers conducted analysis of topic, planning research, conducting research, discussion of the result by themselves with those difficulties Thus, the teachers mentioned that not only content knowledge had been improved, but understanding of procedural knowledge, which is not intended to improve, had been also improved. Besides, they also mentioned that comprehensive understanding content knowledge, which they already understood, was also helpful. And the teachers suggest that if there were chance to discuss and examine the scientific practices by consisting of group with colleagues rather than conducting it individually it would more efficient studying. Considering their suggestion, direction of training elementary school teachers for improving their scientific knowledge should be improved in a way to understand science concepts based on direct research about context that is generated in circumstance of studying group of the teachers. Consequently, it would contribute to improvement of teaching science by combining teachers' practice and understanding.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.9
• /
• pp.3856-3868
• /
• 2011

Until recently, research trend in real estate has been focused on real estate market and the market analysis. But the studies on real estate training program development for real estate agents to improve their job performance are relatively short in numbers. Thus, this study shows empirical analysis of the needs for the training programs for real estate agents in Cheonan to improve their job performance. The results are as follows. First, in the survey of asking what educational contents they need in order to improve real estate agents' job performance, most of the respondents show their needs for the analysis of house's value, legal knowledge, real estate management, accounting, real estate marketing, and understanding of the real estate policy. This is because they are well aware that the best way of responding to the changing clients' needs comes from training programs. Secondly, asked about real estate marketing strategies, most of respondents showed their awareness of new strategies to meet the needs of clients. This is because new forms of marketing strategies including internet ads are needed in the field as the paradigm including Information Technology changes. Thirdly, asked about the need for real estate-related training programs, 92% of the respondents answered they need real estate education programs run by the continuing education centers of the universities. In addition, the survey showed their needs for retraining programs that utilize the resources in the local universities. Other than this, to have effective and efficient training programs, they demanded running a training system by utilizing the human resources of the universities under the name of the department of 'Real Estate Contract' for real estate agents' job performance. Fourthly, the survey revealed real estate management(44.2%) and real estate marketing(42.3%) is the most chosen contents they want to take in the regular course for improving real estate agents' job performance. This shows their will to understand clients' needs through the mind of real estate management and real estate marketing. The survey showed they prefer the training programs as an irregular course to those in the regular one. Despite the above results, this study chose subjects only in Cheanan and thus it needs to research more diverse areas. The needs of programs to improve real estate agents job performance should be analyzed empirically targeting the real estate agents not just in Cheonan but also cities like Pyeongchon, Ilsan and Bundang in which real estate business is booming, as well as undergraduate and graduate students whose major is real estate studies. These studies will be able to provide information to help develop the customized training programs by evaluating elements that real estate agents need in order to meet clients satisfaction and improve their job performance. Many variables of the program development learned through these studies can be incorporated in the curriculum of the real estate studies and used very practically as information for the development of the real estate studies in this fast changing era.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.205-225
• /
• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.