• Korean Journal of Child Studies
• /
• v.31 no.2
• /
• pp.151-168
• /
• 2010

The purpose of this study was to investigate which variables predicted parenting efficacy. The variables of interest were demographic variables regarding both the infants and mothers, infant temperament and development, maternal parenting knowledge, parenting belief, and parenting stress. The subjects consisted of 260 infants and mothers. Data on infant's temperament, parenting knowledge, parenting belief, parenting stress and parenting efficacy were gathered through maternal self-reported questionnaires. Furthermore, infant development was assessed by classroom teacher. Data were analyzed by descriptive statistics, correlation and regression analyses. Our results indicated that infant's sociability and activity, parenting knowledge about emotional development, parenting beliefs emphasizing the role of nature in infant development, low parenting stress all predicted parenting efficacy.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.20 no.3
• /
• pp.77-104
• /
• 1995

Intelligent Decision System support the decision analysis process in the managerial problems with decision analytic knowledge as well as domain specific knowledge. Influence Diagram has been one of the major knowledge representation in the intelligent decision system. In the development of intelligent decision system, knowledge acquisition is also known to be difficult. This paper suggests a developing tool using an influence diagram and Verbal Protocol Analysis which facilitates knowledge acquision for intelligent decision system. An ennvironmental decision making problem is used as an illustrative example and validation of the suggested developing tool is discussed. The suggested tool is very flexible to be expanded or applied to similar problems.

• Knowledge Management Research
• /
• v.18 no.2
• /
• pp.85-102
• /
• 2017

With rapid development of information and communication technologies, open collaboration can be eased through the Internet. Open source software, as a representative area of open collaboration, is developed and adopted to various fields. In this research, based on organizational learning theory, we examine the impacts of exploration and exploitation on innovation performance in open source software development projects. We define knowledge exploration as a number of developers from outside organization and knowledge exploitation as the ratio of member of an organization who participated in an open source software project managed by the organization. For analysis, we collect data of 4794 projects from github which is a representative open source software development platform using Web crawler developed by Python. As a result, we find that excessive exploration has curvilinear (invers U-shape) relationship on project performance. On the other hand, exploitation with enough external developers will positively impact on project performance.

• Research in Mathematical Education
• /
• v.16 no.1
• /
• pp.51-66
• /
• 2012

This study investigated the impact of entry level of mathematics subject knowledge on student teachers' mathematics pedagogical content knowledge development and performance in mathematics teaching practice. The sample consisted of 24 mathematics student teachers, 12 of whom passed A-Level mathematics and 12 of whom only passed O-level mathematics. They were all studying in a 4-year bachelor of education (Honours/Primary) programme; they were either majoring or minoring in mathematics. Results showed that student teachers' entry-level mathematics subject knowledge is not related to their mathematics pedagogical content knowledge development or their mathematics teaching performance. These findings may lead society to consider whether student teachers who have passed O-level mathematics are already eligible to be trained as professional primary mathematics teachers. As a consequence, this study raises the issues of how to develop student teachers' mathematics pedagogical content knowledge and whether we need to restructure our bachelor of education (Primary) programmes' curriculum in teacher professionalism.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.447-450
• /
• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• Knowledge Management Research
• /
• v.5 no.2
• /
• pp.67-98
• /
• 2004

New knowledge is created through the dynamic interaction of knowledge that depends largely on a social context within the organization. Social processes influence the nature of knowledge and learning. This paper is rooted in the concept of social capital. Social capital theory emphasizes the importance of social relationship. Using social capital theory, this paper suggests three factors that must be satisfied for the development of knowledge combinative capability. The first factor is that the opportunity exists to make the exchange or combination of knowledge. The second factor is that people is motivated for the creation of new knowledge. The third factor is that people must share the common knowledge. This paper examines the change case of KIPO (Korean Intellectual Property Office). This case provides evidence that the three factors can develop social relationship, and build knowledge combinative capability. The man finding from this research is that social factors play an important part in the creation of knowledge, and processes of knowledge exchange and combination heavily rely upon social patterns, practices and processes in ways which emphasize the value and importance of collective action and knowledge sharing. This research may have several implications for the development of the knowledge creation mechanisms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.3068-3085
• /
• 2021

The economics of big data knowledge, especially cloud computing and statistical data of consumer preferences, has attracted increasing academic and industry practitioners' attention. Firms nowadays require purchasing not only external private patent knowledge from other firms, but also proprietary big data knowledge to support their new product development. Extant research investigates pricing strategies of external private patent knowledge and proprietary big data knowledge separately. Yet, a comprehensive investigation of pricing strategies of these two types of knowledge is in pressing need. This research constructs an overarching pricing model of external private patent knowledge and proprietary big data knowledge through the lens of firm profitability as a knowledge transaction recipient. The proposed model can help those firms who purchase external knowledge choose the optimal knowledge structure and pricing strategies of two types of knowledge, and provide theoretical and methodological guidance for knowledge transaction recipient firms to negotiate with knowledge providers.

• Information Systems Review
• /
• v.11 no.1
• /
• pp.69-83
• /
• 2009

In a global software development project, knowledge transfer between a corporate headquarter and offshore development sites is considered to be important for effective software development. This article presents empirical research that investigates factors influencing knowledge transfer in global software development. The factors evaluated in this study are: 1) quality of communication infrastructure, 2) quality of partnership and 3) types of governance mechanism. Questionnaires were collected from offshore development sites in Mauritius. While the quality of both the partnership and communication infrastructure were found to be determinants to knowledge transfer, hierarchical governance had a negative impact on knowledge transfer. Although the results are similar to the previous studies done within an organizational boundary, implications of the results are far different due to the geographical distance among offshore locations. Future research is called for to investigate the relationships between governance type and knowledge transfer.

• Journal of Educational Research in Mathematics
• /
• v.22 no.4
• /
• pp.517-534
• /
• 2012

As observing the learning of middle school mathematics students for three years, I examined the relationship between students' procedural knowledge and their conceptual knowledge as they develop those knowledges in the rational number domain. In particular, I explored the implications of an unbalanced development in a student's conceptual knowledge and procedural knowledge by considering two conditions: (a) the case of a student who has relatively strong conceptual knowledge and weak procedural knowledge, and (b) the case of a student who has relatively weak conceptual knowledge and strong procedural knowledge. Results suggest that conceptual knowledge and procedural knowledge are most productive when they develop in a balanced fashion (i.e., closely iterative or simultaneously), which calls into question the assumption that one has primacy over the other.

• Proceedings of the IEEK Conference
• /
• 2002.07b
• /
• pp.1165-1168
• /
• 2002

Knowledge acquisition is the process that extracts the required knowledge from available sources, such as experts, textbooks and databases, for incorporation into a knowledge-based system. Knowledge acquisition is described as the first step in building expert systems and a major bottleneck in the efficient development and application of effective knowledge based expert systems. One cause of the problem is that the process of human reasoning we need to understand for knowledge-based system development is not available for direct observation. Moreover, the expertise of interest is typically not reportable due to the compilation of knowledge which results from extensive practice in a domain of problem solving activity. This is also a problem of modeling knowledge, which has been described as not a problem of accessing and translating what is known, but the familiar scientific and engineering problem of formalizing models for the first time. And this formalization process is especially difficult for knowledge engineers who are often faced with the difficult task of creating a knowledge model of a domain unfamiliar to them. In this paper, we propose an automated knowledge acquisition tool which is based on an implementation of the Inferential Modeling Technique. The Inferential Modeling Technique is derived from the Inferential Model which is a domain-independent categorization of knowledge types and inferences ［Chan 1992］. The model can serve as a template of the types of knowledge in a knowledge model of any domain.