• Speech Sciences
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• v.11 no.2
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• pp.27-38
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• 2004

This study attempted to determine the effects of a cooperative and cognitive group therapy compared to individual therapy in 24 aphasic subjects. Two dependent variables were measured overall language performance, functional communication skills. 18 subjects with different types and severity of aphasia participated in the group therapy. 6 aphasic subjects participated in the individual therapy and they functioned as a control group. The subjects were ranged from 27 to 59 years in age. The group therapy using the cooperative learning utilized the following procedures. First, 6 aphasics constituted 1 group where each subject peformed a task and they monitored one another. Second, 2 aphasics consisted 1 group and they cooperated to perform a task. Third, 3 groups with 2 aphasics in a group competed one another in a task where the 2 aphasics had to cooperated. Finally, the investigator gave the feedback to the group and she and the subjects discussed the overall procedures of the therapy. The above mentioned 2 tests were administered pre- and post-treatment. A repeated two-way ANOVA was performed for analysis. The results showed that the group therapy was more effective in improving overall language performance as compared to the individual therapy. And, the group therapy was more effective in increasing functional communication skills as compared to the individual therapy.

• International Journal of Contents
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• v.17 no.4
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• pp.62-78
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• 2021

This ethnographic multiple case study, based on Zappa-Hollman and Duff's construct of individual networks of practice (INoPs), explored English as a second language (L2) competence development and socialization process of a group of English-major undergraduates through their social connections and interactions at a public university located in an underdeveloped city in Northwest China. The study lasted for one academic semester and three students were selected as primary participants. Semi-structured interviews, student observations in English-related micro-settings, and associated texts were used to collect data. These data were coded to identify the thematic categories, and then data triangulation and member checking were conducted to select the most representative evidence to provide an in-depth description of students' perspective about mediating their English L2 socialization by their INoPs. Findings showed that factors in the formation of students' INoPs, including intensity, density, and nature, played significant roles in their academic or affective returns from their English learning, both of which had a substantial influence on the students' English L2 socialization. Considering that the macro-setting was a non-English, underdeveloped monolingual society, both educational institutions and individual students need to seek and create more English-mediated interactional opportunities to develop their English proficiency and adapt to local English learning communities.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.231-238
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• 2023

As the demand for distance education increases, interest in the management of learners' rights is increasing. Blockchain technology is a technology that guarantees the integrity of the learner's learning history, and enables learner-led learning control, data security, and sharing of learning resources. In this paper, we proposed a blockchain technology-based learning management system based on Hyperledger Fabric that can be verified through permission between nodes among blockchain platforms. Learning resources can be shared differentially according to the learning progress. Also the percentage of individual learners that can be managed. As a result of the study, the superiority of the platform in terms of convenience compared to the existing platform was demonstrated. As a result of the performance evaluation for the research in this paper, it was confirmed that the convenience was improved by more than 5%, and the performance was 4-5% superior to the existing platform in terms of learner satisfaction.

• Educational Technology International
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• v.18 no.2
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• pp.101-129
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• 2017

This study attempted to collect learners' emotion and state motivation, analyze their inner states, and measure state motivation using a non-self-reported survey. Emotions were measured by learning segment in detailed learning situations, and they were used to indicate total state motivation with prediction power. Emotion was also used to explain state motivation by learning segment. The purpose of this study was to overcome the limitations of video-based learning environments by verifying whether the emotions measured during individual learning segments can be used to indicate the learner's state motivation. Sixty-eight students participated in a 90-minute to measure their emotions and state motivation, and emotions showed a statistically significant relationship between total state motivation and motivation by learning segment. Although this result is not clear because this was an exploratory study, it is meaningful that this study showed the possibility that emotions during different learning segments can indicate state motivation.

• Journal of The Korean Association For Science Education
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• v.25 no.7
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• pp.728-735
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• 2005

In this study, relationships between student the cognitive affective characteristics and conceptual understanding from individual computer-assisted instruction were investigated. Tests regarding field dependence-independence, learning strategy, self-regulated ability, visual learning preference, goal orientation, self-efficacy on ability, and computer attitude were administered. After having been taught by means of a CAl program, a conception test on molecular motion was administered. It was found that student conceptual understanding was significantly related to field independence, learning strategy, self-regulated ability among the cognitive characteristics and visual learning preference, goal orientation, self-efficacy on ability among the affective characteristics. Multiple regression analysis of the cognitive characteristics on conceptual understanding found that field dependence-independence was the most significant predictor. Self-regulated ability and a deep learning strategy were also found to have predictive power. Lastly, analysis of the affective characteristics, visual learning preference and self-efficacy on ability exposed them to be significant predictors of student conceptual understanding.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.61-68
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• 2021

Recently, various technologies that use machine learning to classify malicious code have been studied. In order to enhance the effectiveness of machine learning, it is most important to extract properties to identify malicious codes and normal binaries. In this paper, we propose a feature extraction method for use in machine learning using recursive methods. The proposed method selects the final feature using recursive methods for individual features to maximize the performance of machine learning. In detail, we use the method of extracting the best performing features among individual feature at each stage, and then combining the extracted features. We extract features with the proposed method and apply them to machine learning algorithms such as Decision Tree, SVM, Random Forest, and KNN, to validate that machine learning performance improves as the steps continue.

• Journal of The Korean Association of Information Education
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• v.8 no.3
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• pp.335-349
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• 2004

Recently, there is change the environment of education due to development of Science Technology Specially, As education information on web increased by internet, using education web data by mean of medium that aids learning by computer. Also It studied method that used the Computer as learning medium through the CAI(Computer Assisted Instruction), ICAI(Intelligent CAI), and ITS(Intelligent Tutoring System). But legacy system are not support efficient method that learns to vary learner suitable learning method by individual level. Specially It is not suitable the education course to direct current course of education, and not consider different of student capability, aptitude, need, interesting, not maximized the individual growable power and effect of education. To solve the this problem, our paper suggest the web-based self-directed MITS(Multimedia ITS) that supply the needed the information on web, make the environment that can self-directed learning. To maximized effect of individual learning, our paper structured coursed, characterized, related learning contents in region of numeral at mathematics of primary school. And then integrated contents and class, design and implement the web-based MITS that consist of 4 module to escape from limitation of learner grade, learning time, learning place.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.290-297
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• 2024

We investigate the efficacy of ensemble learning methods, specifically the soft voting technique, for enhancing heart disease prediction accuracy. Our study uniquely combines Logistic Regression, SVM with RBF Kernel, and Random Forest models in a soft voting ensemble to improve predictive performance. We demonstrate that this approach outperforms individual models in diagnosing heart disease. Our research contributes to the field by applying a well-curated dataset with normalization and optimization techniques, conducting a comprehensive comparative analysis of different machine learning models, and showcasing the superior performance of the soft voting ensemble in medical diagnosis. This multifaceted approach allows us to provide a thorough evaluation of the soft voting ensemble's effectiveness in the context of heart disease prediction. We evaluate our models based on accuracy, precision, recall, F1 score, and Area Under the ROC Curve (AUC). Our results indicate that the soft voting ensemble technique achieves higher accuracy and robustness in heart disease prediction compared to individual classifiers. This study advances the application of machine learning in medical diagnostics, offering a novel approach to improve heart disease prediction. Our findings have significant implications for early detection and management of heart disease, potentially contributing to better patient outcomes and more efficient healthcare resource allocation.

• 대한공업교육학회지
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• v.30 no.1
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• pp.19-36
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• 2005

The purpose of this study is to develop the Public Practice Center's teaching-learning model by applying blended learning strategies which is complementary to the expected problems such as expansion of the educational object and diversity of the curriculum to maximize the educational effect and to analyze activation types of the Practical Practice Center to expand the Public Practice Center's function and role by studying the document. Blended Learning Strategies are established in consideration of the following eight (8) factors ; learning environment, learning purpose, learning contents, learning time, learning place, learning type, learning media, type of interaction. It is redesigned and amended to the KEDI's individual confirmation instruction model for skill learning (1975) which is considered to be effective in the filed of education by applying features, educational contents of the Public Practice Center's teaching and merit of Blended Learning Strategies simultaneous. This model is composed of six (6) steps as shown below; 1. Understanding on the purpose and orientation 2. Observation for demonstration of fundamental skill 3. Ex on-line learning 4. Acquirement of element skill 5. Confirmation for acquirement of fundamental skill 6. After on-line learning. Further to this, this model is designed so that the above eight factors will be applied to the students effectively and the merit of e-learning and off-line practice will be mixed to the learner's expectation and satisfaction.

• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.357-381
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• 2009

Although it is known that there are personality characteristic variances in the sensitivity to environmental feedback, the trait individual difference has scarcely been explored in the context of recognition memory decision. The present study investigated this issue by examining the relationship between the feedback-based adaptive flexibility of recognition criterion positioning and personality differences in general sensitivity to non-laboratory outcomes. Experiment 1 demonstrated that veridical feedback itself had little effect on the recognition decision criterion whereas Experiment 2 demonstrated that biased feedback manipulations selectively restricted to high confidence errors, induced shifts even in the overall Old/New category criterion. Critically, individual differences in stable personality characteristic linked to reward seeking(Behavioral Activation System-BAS) and anxiety avoidance (Behavioral Inhibition System-BIS) has been shown to predict the sensitivity of subjects to this form of feedback-induced criterion learning. This data further support the idea that incremental reinforcement-based learning mechanism not often considered important during explicit recognition decisions may play a key role in criterion setting.