• Educational Technology International
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• v.17 no.1
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• pp.59-85
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• 2016

The purpose of the study is to report differences in the effects of learner characteristics on the self-regulated learning (SRL) abilities between Chinese and Korean distance learners by using a structured SRL scale. A standardized 54-item self-regulated learning scale (SRAS) was used. The reliability was tested both in China and Korea which showed the scale had good reliability. The comparative study were conducted by administering the SRAS on 1999 Chinese distance learners from the Open Distance Education Center of Beijing Normal University and 1941 Korean distance learners from H Cyber University. Data on four dimensions of SRL - planning, control, regulating, and evaluation - were analyzed using 't-test' and 'ANOVA' with regards to the learner characteristics such as gender, age, prior education level, semesters, location and major. Results indicated that the average participant had an above medium level of SRL ability in all of the four dimensions. There were significant differences in the self-regulated learning ability between Chinese and Korean distance learners. Chinese distance learners scored higher in SRAS than Korean distance learners. The effects of learner characteristics on the SRL ability showed different patterns in the two countries. As for gender, male learners scored better in SRL than female learners in China, whereas it was just the opposite in Korea. No age differences were found in China, but Korean data exhibited a consistent age effect in all dimensions. In Korea, the age group older than 46 scored the highest, followed by the group between 35 to 45 years old, the group between 26 to 35 years old and the group younger than 25. As for location, Korean distance students from metropolitan were better than those from other regions, whereas it was on the contrary in China, albeit the location effect was not statistically significant. Prior education level had a clear and consistent effect on the SRL ability in both countries: the distance learners from junior colleges had better planning, regulating and evaluating abilities than those who came from senior high schools. These results have been discussed in various contexts of distance/online education as well as in relation to different culture between China and Korea. The results will also have implications for designing distance and online learning generally.

• Journal of IKEEE
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• v.23 no.4
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• pp.1408-1414
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• 2019

Deep artificial neural network with transfer learning is applied to compressed sensing cardiovascular MRI. Transfer learning is a method that utilizes structure, filter kernels, and weights of the network used in prior learning for current learning or application. The transfer learning is useful in accelerating learning speed, and in generalization of the neural network when learning data is limited. From a cardiac MRI experiment, with 8 healthy volunteers, the neural network with transfer learning was able to reduce learning time by a factor of more than five compared to that with standalone learning. Using test data set, reconstructed images with transfer learning showed lower normalized mean square error and better image quality compared to those without transfer learning.

• ETRI Journal
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• v.36 no.3
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• pp.429-438
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• 2014

Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-the-art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.

• Journal of the Korean earth science society
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• v.23 no.2
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• pp.166-179
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• 2002

This study investigated preservice teachers' perceived constraints in implementing their ideal pedagogies and the influence of the teacher education program on their pedagogical beliefs changes. Unique features that the university-based coursework and field experiences had on preservice teachers' learning to teach were also explored. This preservice teacher education program employs constructivist aspects of teacher education and generates applications of constructivism to the practice of teaching. Major findings include: preservice teachers' having traditional pedagogy as the default, recovery of prior beliefs, constraints on implementing constructivist pedagogy, and being overly confident in themselves as teachers. With the influence of constructivist epistemology, these preservice teachers' pedagogical beliefs evolved and were refined over time as they incorporated various constructivist ideas. The benefits and influences of the M.Ed. program's theoretical coursework and the field experiences on these teachers' learning-to-teach experiences are addressed with rich data. The implications for teacher educators as well as for the instructional practices of preservice teacher education programs are discussed. Recommendations for future research are also presented.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.173.5-173
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• 2001

Q-Learning is a most widely used reinforcement learning, which addresses the question of how an autonomous agent can learn to choose optimal actions to achieve its goal about any one problem. Q-Learning can acquire optimal control strategies from delayed rewards, even when the agent has no prior knowledge of the effects of its action in the environment. If agent has an ability using previous knowledge, then it is expected that the agent can speed up learning by interacting with environment. We present a novel reinforcement learning method using domain knowledge, which is represented by problem-independent features and their classifiers. Here neural network are implied as knowledge classifiers. To show that an agent using domain knowledge can have better performance than the agent with standard Q-Learner. Computer simulations are ...

• Korean Journal of Child Studies
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• v.23 no.1
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• pp.55-70
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• 2002

The influence of peer collaboration on children's analogical abilities was studied with 120 9-year-old participants. After the pre-test, which determined the analogical level of the children, each child was assigned to 1 of 4 different learning conditions: cued/non-cued peer collaborative learning, or cued/non-cued individual learning conditions. The post-test showed changes in their analogical abilities. That is, results showed that cued peer collaborative learned improved the analogical abilities of the children, but the pattern of improvement was different by prior level of analogical abilities. We explained improvement in analogical ability by the context effect of peer collaborative learning and by the interactive effect of context with basic cognitive abilities of the children. We suggested implications of the present results for educational practice.

• Asian Journal of Innovation and Policy
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• v.7 no.1
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• pp.55-78
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• 2018

This paper investigates the linkage between the mode of transformation of entrepreneurial learning into outcomes and the subsequent impact of these learning outcomes in enhancing the survival of high-tech startups in India. The study uses data from 45 high-tech startups headquartered across different locations in India for the purpose of analysis. Survival Analysis of the data is conducted to determine which mode of learning transformation and what type of en trepreneurial decision making preference have a significant influence on the survival of Indian high-tech startups and to what extent do they impact their survival. The results indicate that entrepreneur's prior startup experience, explorative mode of learning transformation, causal decision making of the entrepreneur and availability of funding for the startup as the key factors that reduce the time to survival of Indian high-tech startups. They also provide key insights on how these factors impact the startup survival in this region.

• Transactions of the Society of Information Storage Systems
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• v.3 no.4
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• pp.167-172
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• 2007

The recently-developed off-line learning control approaches for the rejection of periodic disturbances utilize the specific property that the learning system tends to oscillate in steady state. Unfortunately, the prior works have not clarified how closely the learning system should approach the steady state to achieve the rejection of periodic disturbances to satisfactory level. In this paper, we address this issue extensively for the class of linear systems. We also attempt to remove the effect of other aperiodic disturbances on the rejection of the periodic disturbances effectively. In fact, the proposed learning control algorithm can provide very fast convergence performance in the presence of aperiodic disturbance. The effectiveness and practicality of our work is demonstrated through mathematical performance analysis as well as various simulation results.

• The Korean Journal of Emergency Medical Services
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• v.25 no.2
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• pp.157-167
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• 2021

Purpose: The study was a qualitative study to examine the synchronous and asynchronous distanced learning experience of online paramedic students during the COVID-19 pandemic. Methods: The subjects included 10 students enrolled in the department of emergency medical service at J City C University. Written consent was provided by the subjects prior to the study, and focus group interviews were then conducted with sufficient explanation. The interviews were recorded and were directly transcribed immediately after the interview. Research results were then derived through content analysis. Results: A total of 4 domains and 9 categories were derived from the experiences of paramedic students on distanced learning. The 4 domains included "distanced lectures type," "student's adaptation and non-adaptation," "change of evaluation," and "learning anxiety." Conclusion: Contents of each domain derived from this study are expected to be used as basic data for the design of the distanced learning in the future.