• Journal of The Korean Association For Science Education
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• v.19 no.1
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• pp.137-145
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• 1999

This study investigated the influences of the cooperative and the individualistic learning strategies on the academic achievement and the attitudes toward science instruction and science by the level of achievement. These two learning strategies endowed students with the responsibility of learning and emphasized student-centered learning which included higher order thinking activities. Cooperative learning group students studied the tasks through small group discussion, and individualistic learning group students solved the same ones individually. In the traditional group. teacher-centered expository lesson was used. The subjects of this study were 7th graders of coed middle school, and were taught about separation of mixture for 10 class periods. Two-way ANCOVA results revealed that the test scores of academic achievement for cooperative learning group were significantly higher than those of individualistic and traditional learning groups. The attitudes toward science instruction and science were also more positive in cooperative learning group than the others. No interactions between the treatment and the level of previous achievement indicated that the cooperative learning strategy was effective regardless of the level of achievement.

• Toxicological Research
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• v.17
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• pp.257-261
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• 2001

Recently, a traditional medicine called Gouteng-san, which consists of eleven herbs, was reported to be effective in treating vascular dementia with a double-blind, placebo-controlled study. Gout-eng-san is also used for patients with vascular dementia in combination with Si-Wu-Tang. The effect of Gouteng-san and Si-Wu-Tang on deficit of learning behavior was investigated using step-down passive avoidance task in mice. Hot-water extract of Gouteng-san (1.5 and 6 g/kg, p.o.) significantly prolonged the step-down latency shortened by scopolamine. The extract of Uncaria hook (150 mg/kg, p.o.), one of the component herb of Gouteng-san, significantly prevented the decrease in the latency after scopolamine. Hot-water extract of Si-Wu-Tang (1.5 and 6 g/kg of dried herbs, p.o.) prevented dose-dependently scopola-mine-induced disruption qf learning behavior. Si-Wu-Tang also prevented the ischemia-induced deficit of learning behavior. Both hot water extract of peony and angelica (1.5 g/kg, p.o.), which are component herbs qf Si-Wu-Tang, prevented the scopolamine-induced learning behavior deficit. Scopolamine (10 uM) suppressed long-term potentiation (LTP) of population spike in the CA1 region of the rat hippocampal slices. Peoniflorin (0.1~ 1uM) extracted from paeony root significantly ameliorated scopolamine-induced inhibition of LTR These results suggest that improvement of deficit of learning behavior by Gouteng-san and Si-Wu-Tang is mediated by direct and/or indirect activation of the cholinergic system in the brain.

• Journal of Korean Elementary Science Education
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• v.29 no.4
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• pp.567-575
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• 2010

The purpose of this study is to investigate the effectiveness of academic achievements, science process skill and scientific attitude. The subjects of this study were 68 fourth-grade elementary school students who were 33 students for the 5E learning cycle instruction and 35 students for traditional instruction. The control group was taught with traditional teaching method, while the experimental group was taught 'the change to the volume of material due to heat' unit of 4th grade with the developed learning cycle model. The results were as fellows: First, the learning cycle instruction is more effective for understanding of a concept related to the change to the volume of material due to heat. Second, the learning cycle model seems more effective for the expansion of both scientific inquiry ability and scientific attitude.

• Journal of The Korean Association For Science Education
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• v.19 no.2
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• pp.266-274
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• 1999

This study investigated the effects of cooperative and individual computer-assisted instructions upon middle school students' science conceptions, achievement, perception of learning environment, and motivation. The cooperative, individual, and traditional learning groups were selected from a middle school, and taught about the motion of molecule for 5 class hours. Data analyses indicated that the students with cooperative computer-assisted instruction scored significantly higher than those with traditional instruction in the tests of conceptual understanding, perception of learning environment and motivation. Better understanding of the cooperative learning group was also found in a retention test of conceptions. In addition, there were significant interactions between the instruction and the level of prior achievement in the tests of retention of conceptions and motivation. Educational implications are discussed.

• Smart Structures and Systems
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• v.22 no.2
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.85-90
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• 2023

Data analysis methodology in the healthcare field is shifting from traditional statistics-oriented research methods to predictive research using machine learning. In this study, we survey various machine learning tools, and compare several programming models, which utilize R and Spark, for applying R, a statistical tool widely used in the health care field, to machine learning. In addition, we compare the performance of linear regression model using scala, which is the basic languages of Spark and R. As a result of the experiment, the learning execution time when using SparkR increased by 10 to 20% compared to Scala. Considering the presented performance degradation, SparkR's distributed processing was confirmed as useful in R as the traditional statistical analysis tool that could be used as it is.

• The Journal of the Korea Contents Association
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• v.14 no.6
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• pp.513-528
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• 2014

The landscape of Christian higher education is changing. Students once spent most of their time in a traditional classroom with a professor, but now they take online and hybrid courses (face to face and online). Some students complete their entire degree in a fully online program. Nearly every type of college in the United States offers online courses. Online learning has clearly moved from a fad to a fixture, and nowhere is that more apparent than at one of the largest universities in the country. As the demand for online course and programs increase, teachers and administrators in Christian universities and colleges face new challenges. Even though some teachers and administrators still believe online education is inferior to traditional face-to-face learning, we found no statistically significant differences in standard measures of learning outcomes between students in the traditional classes and students in the hybrid-online format classes. In this situation, since online education will develop continuously, Christian universities should utilize it variously through complete understanding and research about it predicting the future of online education style.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.2
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• pp.179-199
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• 2019

In this paper, we propose optimal methodologies for classifying patent literature by examining various feature extraction methods, machine learning and deep learning models, and provide optimal performance through experiments. We compared the traditional BoW method and a distributed representation method (word embedding vector) as a feature extraction, and compared the morphological analysis and multi gram as the method of constructing the document collection. In addition, classification performance was verified using traditional machine learning model and deep learning model. Experimental results show that the best performance is achieved when we apply the deep learning model with distributed representation and morphological analysis based feature extraction. In Section, Class and Subclass classification experiments, We improved the performance by 5.71%, 18.84% and 21.53%, respectively, compared with traditional classification methods.

• Research in Mathematical Education
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• v.19 no.4
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• pp.211-227
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• 2015

Information Technology influenced on classroom to change the teaching and learning method. Recently, flipped learning method became a hot issue in education by using Information Technology. Learning management system that is introduced in our university in the spring semester 2015, made it possible to apply flipped learning method. So, we used the flipped learning method in a calculus course. In this paper, we found that flipped learning in Calculus we was a little bit affirmative in the aspect of motivation and active learning from students' response on flipped learning method. We analyzed the reason that students were not so positive in continuing flipped learning even though they liked flipped learning a little bit better than traditional learning. We suggest what we pay attention to for applying the flipped learning method effectively.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.176.4-176
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• 2001

In this paper, a novel type of iterative learning controller is studied. The proposed learning algorithm utilizes not only the error signal of the previous iteration but also the delayed error signal of the current iteration. The delayed error signal is adopted to improve the convergence speed. The convergence condition is examined and the result shows that the proposed learning algorithm shows the fast convergence speed under the same convergence condition of the traditional iterative learning algorithm. The simulation examples are presented to confirm the validity of the proposed ILC algorithm.