• 대한공업교육학회지
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• v.36 no.2
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• pp.1-23
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• 2011

The purpose of the study was to investigate the generality and applicability of Team-Based Learning model in a technical high school, based on the interviews with students and a teacher. Team-Based Learning model seems to be an effective way in improving the performance of groups as well as the individualized learning and team interaction. We applied a formative research method and identified the strengths of the model including learners' motivation and interests, learner-centered learning, self-efficacy through learning in advance, and concept acquisition from the repetitive learning process. However, we also found the weakness of the model including impracticality of instructional design, a lack of field-oriented problem banks, and needs for identifying learner characteristics and role in instruction. Finally, we analyzed the implications for the Team-Based Learning in the technical high schools in light of team formation, discussion types, active participation, and learners' prior knowledge and attitude, and pre-determined instructional design.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.245-255
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• 2017

This study was conducted to investigate the effects of neurofeedback (NFB) on attention in college students with ADHD. Participants were 27 university students, 10 in an NFB training group (experiment group), eight in a CBT group (comparison group) and nine in a no-treatment group. The score of CAARS-K decreased significantly in both the NFB group and the CBT group relative to the no-treatment group. Additionally, the score of the digit span test increased significantly in the NFB group, CBT, and no-treatment group, and the scores of these three groups differed significantly. Moreover, significant changes in EEG were found in the NFB Group, while the CBT group showed no significant changes in EEG. The significant change in EEG implies that NFB training improved the stability of brain function on the cerebral neurological level. The effects of improved attention remained after 5 weeks in both the NFB and CBT group. Finally, implications, limitations, and suggestions for future studies were discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.327-331
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• 2018

The purpose of this study was to design a competence teaching-learning model that could help college students improve their computational thinking among core competences in SW education. A competence teaching-learning model, UDDPAAP (Unplugged-Demonstration-Decomposition-Pattern Recognition-Abstraction-Algorithm-Programming), was designed by analyzing competences of learners with no experience in software coding, by reconstructing DMM, DDD, and DPAA among the five existing SW-based teaching-learning models, and by analyzing unplugged activity and the Bebras challenge computational thinking scale carefully. The unplugged activity partially adapted to instruction for college students and some items chosen from the Bebras challenge computational thinking scale were applied to the existing teaching-learning model. To determine the effects of the study, pretest was conducted in freshmen for computational thinking and self-confidence on the basis of the experience in SW and computer information literacy education, and posttest following instruction applying the UDDPAAP teaching-learning model. The students provided with SW education based on the UDDPAAP teaching-learning model saw their computational thinking competence improved.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.343-354
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• 2018

This sturdy proposed and tested whether the six core treatment processes of Acceptance and Commitment Therapy (ACT) reduce self-criticism and dichotomous thinking in Evaluative concerns perfectionists and even diminish depression and ultimately verify it. The sample consisted of 22 subjects who displayed high EC perfectionism and depression. The subjects were randomly assigned to ACT groups or control groups (n-11/each). The ACT program was administered in eight sessions. All participants completed scale about EC perfectionism, self-criticism, dichotomous thinking, depression and acceptance & action at the pre-test, post-test, and the six week follow-up. The results showed that evaluative concerns perfectionism, self-criticism, dichotomous thinking, and depression decreased more in the treatment group than in the control group, whereas acceptance & action increased in the former. Finally, the implications and limitations of this study, along with suggestions for future study were discussed.

• Journal of The Korean Association For Science Education
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• v.25 no.2
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• pp.162-172
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• 2005

The purpose of this study was to examine the students' responses when students were confronted with anomalous situations in physics. 16 students were selected from one middle school in Busan by examining the pre-test results. To measure students' responses and cognitive conflict levels, written Cognitive Conflict Levels Test(CCLT) developed in a previous study was used together with interviews. Students' responses were tape-recorded. Two kinds of anomalous situations were presented. One was a quantitative demonstration with scale, the other was a qualitative demonstration without scale. In the quantitative group, all students observed anomalous situations correctly. However, in the qualitative group, many of their observations of anomalous situations were incorrect. The students who observed anomalous situations based on preconceptions tended not to abandon their preconceptions, and suggested confirmation experiments which were supposedly to support their preconceptions. The students who recognized results very differently from their preconceptions when confronted with anomalous situations abandoned their preconceptions and suggested alternative experiments. The students who changed their beliefs about preconceptions showed higher cognitive conflict levels than who didn't abandon their preconceptions.

• Journal of the Korean Society of Child Welfare
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• no.32
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• pp.69-97
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• 2010

The purpose of this study is to investigate the effects of high school students' academic problems such as academic stress and low academic achievement on suicidal ideation and the mediating effects of individual-level risk factors (e.g., depression, aggression) and protective factors (self-esteem, emotion regulation) on the relationship between academic problems and suicidal ideation. This study used data of three waves(2005-2007) from the Korea Youth Panel Survey(KYPS). The analyses were conducted on 2,093 academic high school students, who participated in this panel study. The results from structural equation modeling show that academic stress has positive effects on depression and aggression, but negative effect on self-esteem. Low academic achievement has positive effect on aggression but negative effect on self-esteem. Depression and aggression as individual-level risk factors have positive effects on suicidal ideation. In contrast, self-esteem and emotion regulation as individual-level protective factors have negative effects on suicidal ideation. The relationship between academic problems and suicidal ideation is mediated by depression, aggression, and self-esteem. Based on the study findings, practice implications for youth welfare are discussed to screen high-risk youths and to prevent adolescent suicide in advance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.651-658
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• 2021

Because bolts with anti-loosening coatings are used mainly for joining safety-related components in automobiles, accurate automatic screening of these coatings is essential to detect defects efficiently. The performance of the convolutional neural network (CNN) used in a previous study [Identification of bolt coating defects using CNN and Grad-CAM] increased with increasing number of data for the analysis of image patterns and characteristics. On the other hand, obtaining the necessary amount of data for coated bolts is difficult, making training time-consuming. In this paper, resorting to the same VGG16 model as in a previous study, transfer learning was applied to decrease the training time and achieve the same or better accuracy with fewer data. The classifier was trained, considering the number of training data for this study and its similarity with ImageNet data. In conjunction with the fully connected layer, the highest accuracy was achieved (95%). To enhance the performance further, the last convolution layer and the classifier were fine-tuned, which resulted in a 2% increase in accuracy (97%). This shows that the learning time can be reduced by transfer learning and fine-tuning while maintaining a high screening accuracy.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• Korean Journal of Social Welfare Studies
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• v.41 no.4
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• pp.81-105
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• 2010

This study is aimed to investigate the effects of stress on suicidal impulse, considering the mediating effects of existent spirituality, family support, and depression. The data, collected from 1,000 adults were examined by the statistics software SPSS 17.0 and AMOS 17.0, in which descriptive statistics, structural equation model analysis, and multi-group simultaneous analysis are utilized. The study shows that, from the structural equation modeling, the stress has positive effects on depression and suicidal impulses, but negative effects on existent spirituality. Existent spirituality acts as a protective factor, negatively affecting the suicidal impulse. Depression has positive effects on suicidal impulse. Therefore, existent spirituality and depression have mediational effects on the relationship between stress and suicidal impulse. The results of multi-group simultaneous analysis imply that there are no age and sex differences. In conclusion, social workers need to make great efforts to exterminate stress, and treat depression at the first priority, because the depression is a major sign of suicide. As a protective factor, strengthening existent spirituality is a very effective way to prevent a suicidal impulse.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.57-67
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• 2024

This research investigates training methods for large language models to accurately identify sentiments and comprehend information about increasing and decreasing fluctuations in the financial domain. The main goal is to identify suitable datasets that enable these models to effectively understand expressions related to financial increases and decreases. For this purpose, we selected sentences from Wall Street Journal that included relevant financial terms and sentences generated by GPT-3.5-turbo-1106 for post-training. We assessed the impact of these datasets on language model performance using Financial PhraseBank, a benchmark dataset for financial sentiment analysis. Our findings demonstrate that post-training FinBERT, a model specialized in finance, outperformed the similarly post-trained BERT, a general domain model. Moreover, post-training with actual financial news proved to be more effective than using generated sentences, though in scenarios requiring higher generalization, models trained on generated sentences performed better. This suggests that aligning the model's domain with the domain of the area intended for improvement and choosing the right dataset are crucial for enhancing a language model's understanding and sentiment prediction accuracy. These results offer a methodology for optimizing language model performance in financial sentiment analysis tasks and suggest future research directions for more nuanced language understanding and sentiment analysis in finance. This research provides valuable insights not only for the financial sector but also for language model training across various domains.