• Knowledge Management Research
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• v.18 no.1
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• pp.117-138
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• 2017

While prior work has noted the importance of knowledge creation in gaining competitive advantages, much less is understood about why firms do not actually use what they create. Building upon institutional approaches to organization studies, we offer a new framework to explain the gap between knowledge creation and utilization. We test our framework in an empirical context of sustainable innovation and environmental technologies where ideas of environmental sustainability have recently gained public popularity and shaped how interested audiences make evaluative assessments of firms. In such a context, firms are apt to perceive the social attention toward sustainability to be a normative pressure, which causes them to create new knowledge and develop technologies consistent with the pressure. Using data from the government-initiated certification system for green technologies, our study finds that firms do not always fully implement new environmental technologies they develop in response to the certification program, the situation we refer to as knowledge decoupling. We also examine a set of conditions under which knowledge decoupling becomes more or less amplified. Taken together, our findings show how a firm's knowledge creation and utilization is shaped by its external institutional environment as well as internal learning processes.

• School Mathematics
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• v.11 no.4
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• pp.745-771
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• 2009

To acquire the hints of the development of children's multiplication strategies, this study tried to find the differences between the students who learned multiplication and the students who didn't. And we also tried to explore their acquired computational resources. As a result, we confirm that there is a certain direction on the development of children's multiplication strategies according to their grades and the level of acquirement of mathematical knowledge. Moreover, we comprehend that commutative law is an important part of the strategies on two-digit multiplication and that acquisition of the computational resources must precede the learning of multiplication strategies. In the end part, this article proposes a new taxonomy of strategies for multiplication. To support our proposal, we integrated the prior researches with our findings.

• Research in Mathematical Education
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• v.18 no.3
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• pp.171-185
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• 2014

Mathematically deductive reasoning skill is one of the major learning objectives stated in senior secondary curriculum (CDC & HKEAA, 2007, page 15). Ironically, student performance during routine assessments on geometric reasoning, such as proving geometric propositions and justifying geometric properties, is far below teacher expectations. One might argue that this is caused by teachers' lack of relevant subject content knowledge. However, recent research findings have revealed that teachers' knowledge of teaching (e.g., Ball et al., 2009) and their deductive reasoning skills also play a crucial role in student learning. Prior to a comprehensive investigation on teacher competency, we use a case study to investigate teachers' knowledge competency on how to teach their students to mathematically argue that, for example, two triangles are congruent. Deductive reasoning skill is essential to geometry. The initial findings indicate that both subject and pedagogical content knowledge are essential for effectively teaching this challenging topic. We conclude our study by suggesting a method that teachers can use to further improve their teaching effectiveness.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.341-347
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• 2020

In this paper, we propose to discover the certification items and to study the content system for SW education instructors, including SW education based on basic teaching-learning capabilities and Computational Thinking(CT). To this end, SW education instructor qualification were divided into three classes using methods such as prior case studies, Delphi surveys, and expert meetings, and the certification evaluation areas were divided into large areas of 'Teaching and Learning Method' and 'Software Education' reflecting primary and secondary curriculum. Sub-areas and content elements for each series were set and verified through expert Delphi survey. Such research is expected to contribute to the spread and dissemination of SW education by being used meaningfully when establishing a system that fosters SW education instructors and maintains and manages the quality of instructors.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• The Journal of Economics, Marketing and Management
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• v.11 no.4
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• pp.31-39
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• 2023

Purpose: This study attempted to systematize a support system that can enhance teaching core competencies by establishing a scale for diagnosing teaching core competencies at University A. Research design, data and methodology : To this end, the first Delphi was conducted With six experts related to university core competency modeling research by extracting factors and designing structured questionnaires through a literature review process that collects and analyzes prior research related to domestic and foreign university teaching competency. The derived questions were diagnosed on 27 professors, and independent sample t-verification and ANOVA were conducted using SPSS 24.0 for analysis by key teaching competency factors. Result: What is the standard suitability of KMO. It was shown as 929 (KMO standard conformity value is close to 1), and Barlett's sphericity verification showed χ2=5773.295, df=1081, p<.It appeared as 001 and confirmed that it was suitable for conducting factor analysis. Conclusions: The core competencies of A University teachers were set based on the educational goals of A University, such as basic teaching competency, creative teaching competency, practical teaching competency, and communication teaching competency. This means that the concept and factors of the core competency of professors are likely to change, and in the end, continuous efforts to upgrade and apply research on core competency of professors are essential to quickly and organically respond to changes in competency required to increase the competitiveness of universities.

• Fourth Industrial Review
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• v.2 no.1
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• pp.9-17
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• 2022

Purpose -In general, elementary school is the time to take the first social step away from family relationships with parents or siblings. Recently, AI technology has been widely used in everyday life and society. The purpose of this study is to propose a program that can cultivate AI literacy and self-expression for elementary school students according to the trend of the times. Research design, data, and methodology - In this study, prior to developing a self-expression class program for cultivating AI literacy, we looked at the related literature on what AI literacy is. In addition, the digital learning program was analyzed considering that the current AI literacy is based on the cutting edge of digital technology and is located in the same area as digital literacy. Result -This study developed a curriculum for self-expression and AI literacy cultivation. The main feature of this study is that the education program of this study allows 3rd, 4th, and 5th graders of elementary school to express themselves and to express their career problems by combining culture and art with AI programs. Conclusion -Self-expression activity education for cultivating AI literacy should be oriented toward holistic education and should be education as a way to express oneself in order to improve the quality of life of learners

• Research in Mathematical Education
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• v.26 no.1
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• pp.1-17
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• 2023

Over the last three decades, there has been an increasingly strong emphasis on group-centered approaches to mathematics teaching. One primary responsibility for teachers who use group-centered instruction is to "check in", or intervene, with groups to monitor group learning and provide mathematical support when necessary. While prior research has contributed valuable insight for successful teacher interventions in mathematics group work, there is a need for more fine-grained analyses of interactions between teachers and students. In this study, we co-conducted research with an exemplary middle grade teacher (Ms. Green) to learn about fine-grained details of her intervention practices, hoping to generate knowledge about successful teacher interventions that can be expanded, replicated, and/or contradicted in other contexts. Analyzing Ms. Green's practices as an exemplary case, we found that she used exceptionally short interventions (35 seconds on average), provided space for student dialogue, and applied four distinct strategies to support groups to make mathematical progress: (1) observing/listening before speaking; (2) using a combination of social and analytic scaffolds; (3) redirecting students to task instructions; (4) abruptly walking away. These findings imply that successful interventions may be characterized by brevity, shared dialogue between the teacher and students, and distinct (and sometimes unnatural) teaching moves.