• Title/Summary/Keyword: Prior learning.

Search Result 690, Processing Time 0.026 seconds

Performance Drivers of Entrepreneurial Restarts (재창업 기업의 성과 결정요인에 관한 연구)

  • Bae, Young Im
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
    • /
    • v.9 no.3
    • /
    • pp.13-22
    • /
    • 2014
  • This article investigates the effects of prior firm founding experience, prior firm's performance, same industry matters, entrepreneurship communities or educations on subsequent firm performance. This study also explores whether same industry matters and entrepreneurship communities or educations have interaction effects between entrepreneurial experience and subsequent firm performance. The results show that prior firm founding experience and prior firm's performance have a positive effect on subsequent firm performance. The rest of variables have no significant direct effects on firm performance. However, same industry matters and entrepreneurship communities or educations have interaction effects of entrepreneurial experience on firm performance. Based on results of this empirical study, this study draws some implications that entrepreneurs can become serial entrepreneurs and experience entrepreneurial success.

  • PDF

Structural Relationships among SEM CEO's Positive Leadership, Members' Positive Life Positions, Learning Organization Activities, Job Engagement, and Organizational Performance (중소기업경영자의 긍정적 리더십, 구성원의 긍정적 삶의 태도, 학습조직활동, 직무열의, 조직성과 변인간의 구조적 관계)

  • Park, Sooyong;Choi, Eunsoo
    • Journal of Distribution Science
    • /
    • v.13 no.12
    • /
    • pp.113-131
    • /
    • 2015
  • Purpose - In today's era of globalization, the competitive power of enterprises is growing fiercer, calling for organizations to be able to respond flexibly to survive and maintain predominance in competition. In turn, keen competition exists among enterprises for the systematic management of members' knowledge to secure predominance in such competition. Under such circumstances, SMEs must find and utilize positive causes for change that affect organizational performance. The objective of this study is to analyze the structural relationship between four factors known from prior research-a CEO's positive leadership, members' positive life positions, learning organization activities, and job engagement-and organizational performance. Research design, data, and methodology - To achieve this objective, this study established the following four research problems. First, do CEOs' positive leadership, members' positive life positions, learning organization activities, and job engagement affect organizational performance? Second, do CEOs' positive leadership, members' positive life positions, and learning organization activities affect job engagement? Third, do CEOs' positive leadership and members' positive life positions affect learning organization activities? Fourth, does CEOs' positive leadership affect members' positive life positions. Additionally, to achieve the objective of this study, the research model was selected on the basis of a documentary survey of 787 full-time employees at 100 SMEs, which was used to collect related data. Results - The following conclusions were drawn. First, a CEO's positive leadership directly affects members' positive life positions, learning organization activities, and job engagement. Second, positive leadership only indirectly affects organizational performance. That is, positive leadership has an indirect effect on organizational performance given the parameters of members' positive life positions, learning organization activities, and job engagement. Third, members' positive life positions directly affect learning organization activities and job engagement, but indirectly affect organizational performance with learning organization activities and job engagement as parameters. Fourth, learning organization activities directly affect job engagement and organizational performance. Additionally, learning organization activities indirectly affect organizational performance with job engagement as a parameter. Fifth, job engagement directly affects organizational performance. Conclusions - A CEO's positive leadership and members' positive life positions do not directly affect organizational performance but have a positive effect through learning organization activities and job engagement. In particular, CEOs' positive leadership was proven to be the major factor to affect members' positive life positions, learning organization attitudes, and job engagement, and learning organization activities and job engagement were found to be major factors that directly affect organizational performance. Considering these conclusions, the direct effect of a CEO's positive leadership on organizational performance is not statistically significant but seems to affect members' positive life positions, learning organization activities, and job engagement, which ultimately affects organizational performance. In addition, CEOs' positive leadership is an important factor that enhances the factors with the strongest effect on organizational performance-activities of learning organizations and job engagement.

A Study on the Construction of Intelligent Learning Platform Model for Faith Education in the Post Corona Era (포스트 코로나 시대 신앙교육을 위한 지능형학습플랫폼 모형 구성 연구)

  • Lee, Eun Chul
    • Journal of Christian Education in Korea
    • /
    • v.66
    • /
    • pp.309-341
    • /
    • 2021
  • The purpose of this study is to develop an intelligent learning platform model for faith education in preparation for the post-corona era. This study reviewed artificial intelligence algorithms, research on learning platform development, and prior research related to faith education. The draft of the intelligent learning platform design model was developed by synthesizing previous studies. The developed draft model was validated by a Delphi survey targeting 5 experts. The content validity of the developed draft model was all 1. This is the validation of the draft model. Three revised opinions of experts were presented on the model. And the model was revised to reflect the opinions of experts. The modified final model consisted of three areas: learning materials, learning activities, learning data, and artificial intelligence. Each area is composed of 9 elements of curriculum, learning content additional learning resources, learner type, learning behavior, evaluation behavior, learner characteristic data, learning activity data, artificial intelligence data, and learning analysis. Each component has 29 sub-elements. In addition, 14 learning floors were formed. The biggest implication of this study is the first development of a basic model of an intelligent learning platform for faith education.

Children's Inferring Word Meaning From Understanding of the Speaker's Mind (단어의미 추론에서 나타나는 아동의 마음이론)

  • Song, Young Joo
    • Korean Journal of Child Studies
    • /
    • v.27 no.2
    • /
    • pp.167-180
    • /
    • 2006
  • This study investigated how children rely on the mind of the speaker to infer unfamiliar words. Sixty 3 to 5-year-old children were interviewed individually with word inference and false belief tasks. Children's sensitivity to the speakers' intentions and prior experiences increased with age. Unexpectedly, their performance was not different with the condition of mind construct. Children's inferring word meanings were positively, but not significantly, correlated with understanding others' false beliefs.

  • PDF

Creativity Development in Probability through Debate

  • Oh, Taek-Keun;Lee, Kyeong Hwa
    • Research in Mathematical Education
    • /
    • v.16 no.4
    • /
    • pp.233-244
    • /
    • 2012
  • The purpose of this study is to investigate the relationship between creativity development and debate in solving a probability task. We developed the probability task with instructional strategies facilitating debating among students. 33 students in grade 11 who were identified as gifted participated in this study. The findings indicated that debating leads students to critical and reflective thinking on prior learning regarding probability concepts, which nurtured creative ideas on sample space.

Outlier Robust Learning Algorithm for Gaussian Process Classification (가우시안 과정 분류를 위한 극단치에 강인한 학습 알고리즘)

  • Kim, Hyun-Chul;Ghahramani, Zoubin
    • Proceedings of the Korean Information Science Society Conference
    • /
    • 2007.10c
    • /
    • pp.485-489
    • /
    • 2007
  • Gaussian process classifiers (GPCs) are fully statistical kernel classification models which have a latent function with Gaussian process prior Recently, EP approximation method has been proposed to infer the posterior over the latent function. It can have a special hyperparameter which can treat outliers potentially. In this paper, we propose the outlier robust algorithm which alternates EP and the hyperparameter updating until convergence. We also show its usefulness with the simulation results.

  • PDF

An Adaptive Iterative Learning Control and Identification for Uncertain Robotic Systems (불확실한 로봇 시스템을 위한 적응 반복 학습 제어 및 식별)

  • 최준영
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.10 no.5
    • /
    • pp.395-401
    • /
    • 2004
  • We present an AILC(Adaptive Iterative Learning Control) scheme and a sufficient condition for system parameter identification for uncertain robotic systems that perform the same tasks repetitively. It is guaranteed that the joint velocity and position asymptotically converge to the reference joint velocity and position, respectively. In addition, it is proved that a sufficient condition for parameter identification is the PE(Persistent Excitation) condition on the regressor matrix evaluated at the reference trajectory during the operation period. Since the regressor matrix on the reference trajectory can be easily computed prior to the real robot operation, the proposed algorithm provides a useful method to verify whether the parameter error converges to zero or not.

Separation of Single Channel Mixture Using Time-domain Basis Functions

  • Jang, Gil-Jin;Oh, Yung-Hwan
    • The Journal of the Acoustical Society of Korea
    • /
    • v.21 no.4E
    • /
    • pp.146-155
    • /
    • 2002
  • We present a new technique for achieving source separation when given only a single charmel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of time-domain basis functions that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single charmel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to the prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation, and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources. We show separation results of two music signals as well as the separation of two voice signals.

Development of tool condition monitoring system using unsupervised learning capability of the ART2 network

  • Choii, Gi-Sang
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1991.10b
    • /
    • pp.1570-1575
    • /
    • 1991
  • The feasibility of using an adaptive resonance network (ART2) with unsupervised learning capability for too] wear detection in turning operations is investigated. Specifically, acoustic emission (AE) and cutting force signals were measured during machining, the multichannel AR coefficients of the two signals were calculated and then presented to the network to make a decision on tool wear. If the presented features are significantly different from previously learned patterns associated with a fresh tool, the network will recognize the difference and form a new category m worn tool. The experimental results show that tool wear can be effectively detected with or without minimum prior training using the self-organization property of the ART2 network.

  • PDF

Study of an algorithm for intelligent digital protective relaying (지능형 디지탈 보호계전 알고리즘 연구)

  • 신현익;이성환;강신준;김정한;김상철
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1996.10b
    • /
    • pp.343-346
    • /
    • 1996
  • A new method for on-line induction motor fault detection is presented in this paper. This system utilizes unsupervised-learning clustering algorithm, the Dignet, proposed by Thomopoulos etc., to learn the spectral characteristics of a good motor operating on-line. After a sufficient training period, the Dignet signals one-phase ground fault, or a potential failure condition when a new cluster is formed and persists for some time. Since a fault condition is found by comparison to a prior condition of the machine, on-line failure prediction is possible with this system without requiring information on the motor of load characteristics.

  • PDF