• English Language & Literature Teaching
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• v.10 no.2
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• pp.133-158
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• 2004

The purpose of the present study was to scrutinize computer games that can motivate elementary school students through their interactive "edutainment" effects. The types of elements in computer games that students find interesting as learning media and their impact were studied. The current status of Korean computer games, issues related to learning English, and methods to stimulate the motivation and interest in learning by elementary school students were explored. A computer game model for efficiently teaching English to elementary school students through a connection between computer games and education was suggested. In this model, overall games were designed with the focus on the integration of curriculum and content subjects related to learning activities. For games not to be biased toward entertainment and to have systemized learning steps, the games are composed of an introduction, presentation, practice, production and evaluation, in that order. The model suggested by this plan and composition make it possible to approach learning efficiently with entertaining games based on a systematic learning curriculum. As shown above, developing the model of educational computer games can be seen as an opportunity, which can provide amusement and interests and a broad learning experience as an additional learning method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• Journal of The Korean Association For Science Education
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• v.16 no.1
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• pp.13-34
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• 1996

The purpose of this study is to develop appropriate science teaching models which can be applied effectively to relevant situations. Five science teaching models; cognitive conflict teaching models, generative teaching model, learning cycle teaching model, hypothesis verification teaching model and discovery teaching model, were identified from the existing models. The teaching models were modified and in primary and secondary students using a nonequivalent pretest-posttest control group design. Major findings of this study were as follows: 1. For teaching science concepts, three teaching models were found more effective; cognitive conflict teaching model, generative teaching model and discovery teaching model. 2. For teaching inquiry skills, two teaching models were found more effective; learning cycle teaching model and hypothesis verification teaching model. 3. For teaching scientific attitudes, two teaching models were found more effective; learning cycle teaching models and discovery teaching model. Each teaching model requires specific learning environment. It is strongly suggested that teachers should select a suitable teaching model carefully after evaluating the learning environment including teacher and student variables, learning objectives and curricular materials.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.153-158
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• 2013

Target learning model is not recognized in this category or not classified clearly failed to determine if the vocabulary recognition is reduced. Form of classification learning model is changed or a new learning model is added to the recognition decision tree structure of the model should be changed to a structural problem. In order to solve these problems, a decision tree learning model for classification learning algorithm is proposed. Phonological phenomenon reflected sound enough to configure the database to ensure learning a decision tree learning model for classifying method was used. In this study, the indoor environment-dependent recognition and vocabulary words for the experimental results independent recognition vocabulary of the indoor environment-dependent recognition performance of 98.3% in the experiment showed, vocabulary independent recognition performance of 98.4% in the experiment shown.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.141-148
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• 2023

As the direction of education in the fourth industry in the 21st century, convergence talent education that emphasizes the connection and convergence between core competency-based education and academia is emerging to foster creative talent. The purpose of this paper is to present the criteria for evaluating the competency of learning outcomes in order to develop a strategic model for innovation in engineering teaching-learning. In this paper, as a study to establish the direction of implementation of convergence talent education, a creative innovation teaching method support system was established to improve the quality of convergence talent education. Firstly, a plan to develop a teaching-learning model based on computing thinking. Secondly, it presented the development of a teaching-learning model based on linkage and convergence learning. Thirdly, we would like to present educational appropriateness and ease based on convergence learning in connection with curriculum improvement strategies based on computing thinking skills. Finally, we would like to present a strategic model development plan for innovation in engineering teaching-learning that applies the convergence talent education program.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.1
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• pp.12-28
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• 2013

The purpose of this study was to develop storytelling Instructional model for promote problem-solving in a Blended learning Environment. To achieve the purpose, the study was performed by dividing into two stages. First, the draft of storytelling Instructional model was proposed by performing a literature survey and a case study. Second, the draft model was applied to the actual work. And the draft was modified and developed to the final model on the basis of the draft model's strength and implemented to 28 students who were the sophomore of child care education department and enrolled the profession class of at S University for 6 weeks. From the implementation result of the model, it was obtained that there was the positive reaction on applying storytelling technique to the beginning stage of learning. Instructional model storytelling consists phases Preparing to perform Storytelling, Building the team and role sharing team, Problem providing, Planning for problem solving, Brend Story structuralization, Cooperative Learning and Problem solving, announcement of the results and evaluating and reflection of general. And then learning supporting components for a facilitator and a learner were prepared for each process. Established in a Blended learning Environment was created based on all-line, how to teach and learning supporting organization. Final Model was suggested as a blueprint for stages actual learning which was consisted of a introductory storytelling part, an main storytelling part and a post storytelling part.

• Korean Educational Research Journal
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• v.38 no.2
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• pp.81-108
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• 2017

The purpose of this study is to develop a standardized learning model for a free-learning semester, and to provide a practical framework of its curriculum. This paper is a state-funded study to design an instructional model for the free-learning semester. Instructional models that have been implemented in the practical school were constructed through collecting opinions from school teachers. The instructional model for a free-learning semester in this current study is a modification of the existing learning model into the Learning (Meaningful reception learning)-Practice-Production stage. These are designed to reflect the UNESCO proposals that emphasize knowledge, skills, and character, respectively. It is not easy to construct the instructional model for the free-learning semester activities. A three-step strategy that encompasses the UNESCO proposals will be a useful framework for teachers to systematically design and implement free-learning semester teaching.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.97-97
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• 2021

Hydrometeorological variables contain highly complex system for temporal revolution and it is quite challenging to illustrate the system with a temporal linear and nonlinear models. In recent years, deep learning algorithms have been developed and a number of studies has focused to model the complex hydrometeorological system with deep learning models. In the current study, we investigated the temporal structure inside deep learning models for the hydrometeorological variables such as streamflow and climate indices. The results present a quite striking such that each hidden unit of the deep learning model presents different dependence structure and when the number of hidden units meet a proper boundary, it reaches the best model performance. This indicates that the deep dependence structure of deep learning models can be used to model selection or investigating whether the constructed model setup present efficient or not.