• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• Smart Media Journal
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• v.10 no.2
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• pp.16-21
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• 2021

Financial time series analysis plays a very important role economically and socially in modern society and is an important task affecting global development, but due to difficulties such as a lot of noise and uncertainty, financial time series analysis prediction is a difficult research topic. In this paper, we propose a market prediction method (MPIL) by converting unstructured data and structured data into images. For market prediction, it analyzes SNS and news data, which is unstructured data for n days, and converts the market data, which is structured data, to an image with the GADF algorithm, and predicts an ultra-short market that predicts the price of n+1 days through image learning. MPIL has an average accuracy of 56%, which is higher than the 50% average accuracy of the model that predicts the market with LSTM by using sentiment analysis used for existing market forecasting.

• Journal of Korean Biological Nursing Science
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• v.21 no.3
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• pp.231-238
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• 2019

Purpose: In 2015, South Korea had the highest global smartphone penetration (88%). However, smartphone addiction can seriously disrupt daily life and have a major negative impact on academic achievement. Methods: A structured questionnaire was completed by 250 nursing students for this descriptive study. Results: Students who were older, more satisfied with their major, exercised, and used their smartphone for less than 30 minutes before sleeping had higher learning engagement than those who were younger, less satisfied, did not exercise and used their smartphone for more than three hours. Quality of sleep and smartphone addiction were negatively correlated as was quality of sleep and daytime sleepiness. Interestingly, sleep pattern did not impact learning engagement directly. Conclusion: Smartphone usage influences learning engagement of nursing students rather than their sleeping patterns, which suggests a need to develop self-disciplining strategies for smartphone use to enhance learning engagement.

• Educational Technology International
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• v.24 no.1
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• pp.81-114
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• 2023

The study aimed to qualitatively examine middle school students' engagement in distance instruction during the COVID-19 pandemic. The participants comprised 119 students from a girls' middle school in Seoul, South Korea. To gain an in-depth understanding of the students' experiences, we collected their reflective journals, which included structured items about their learning engagement at three timepoints in 2020: April, July, and December. The following are the results: 10 themes and 18 concepts were derived, and they were integrated into causal conditions (sudden transition due to COVID-19), contextual condition (technology readiness, school education context), central phenomena (high level of behavioral engagement, low emotional engagement), interventional conditions (recognizing the potential of online learning, situational awareness about COVID-19 and online learning), action/interaction phenomena (development and use of self-regulated learning strategies), and consequences (changes in practices and perception towards online learning). Based on the findings, engagement patterns of the participants were classified into five types: proactive, conservative, receptive, reactive, passive learners. The present study demonstrated important findings that are essential for the improvement and development of engaging online learning strategies in the future.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2121-2142
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• 2011

In this paper, we propose an e-Learning process control model that aims to graphically describe and automatically generate the manifest of sequencing prerequisites in packaging SCORM's content aggregation models. In specifying the e-Learning activity sequencing, SCORM provides the concept of sequencing prerequisites to be manifested on each e-Learning activity of the corresponding tree-structured content organization model. However, the course developer is required to completely understand the SCORM's complicated sequencing prerequisites and other extensions. So, it is necessary to achieve an efficient way of packaging for the e-Learning content organization models. The e-Learning process control model proposed in this paper ought to be an impeccable solution for this problem. Consequently, this paper aims to realize a new concept of process-driven e-Learning content aggregating approach supporting the e-Learning process control model and to implement its e-Learning process modeling system graphically describing and automatically generating the SCORM's sequencing prerequisites. Eventually, the proposed model becomes a theoretical basis for implementing a SCORM-based e-Learning process management system satisfying the SCORM's sequencing prerequisite specifications. We strongly believe that the e-Learning process control model and its modeling system achieve convenient packaging in SCORM's content organization models and in implementing an e-Learning management system as well.

• International Journal of Contents
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• v.15 no.4
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• pp.89-96
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• 2019

This study examines the impact of online collaborative English language learning to enhance learner motivation and classroom engagement in university English instruction. The role of learner motivation and classroom engagement has gained much attention under the premises of current constructivist framework of English as a foreign language education. To promote learner motivation and classroom interaction in English instruction, participants in this study engaged in integrative English learning activities through online group collaboration and peer-tutoring. They exchanged productive peer response and shared their learning experiences throughout the integrative English learning activities. Digital technology played an integral role in motivating the learning process of the participants. Data for this study were gathered through an online questionnaire survey and semi-structured interviews. The data were analyzed based on the ARCS motivational model of instructional design to identify the motivational aspects of integrative English learning activities. This study reveals that participants of this study regarded online collaborative English learning activities as the positive and motivating learning experience. The online collaborative English reading instruction had positive effect on improving EFL university students' learning performance. Participants of this study also identified affective and metacognitive benefits of online collaborative EFL learning activities for learner motivation and classroom engagement. This study reveals that the social networking platform in online group collaboration played a crucial role for the participants in understanding the integration of online group collaboration as the positive and effective language learning strategy. This study may have implications in suggesting the effective instructional design for promoting learner motivation and classroom interaction in EFL education.

• Journal of The Korean Association For Science Education
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• v.34 no.2
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• pp.155-163
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• 2014

The present study tries to identify the characteristics of Problem Based Learning (PBL), which affects the development of middle school students' science self-efficacy. Additionally, we have tried to analyze the relationship within those characteristics to demonstrate the processes of science self-efficacy development. In line with this reasoning, we have developed a 20-module, problem-based learning science program and applied this program to 9th grade students (n=17). Two rounds of qualitative interviews have been conducted with each participant after the program, which has been analyzed with the well-documented method by Corbin and Strauss (2007). As a result, three characteristics of problem based learning have been identified to affect the development of science self-efficacy: a) authentic and ill-structured problem sets, b) small group activity, and c) result sharing. Further analysis has revealed that an authentic and ill-structured problem set as a condition precedent of self-efficacy development, while small group activity has worked as an acceleration condition. Lastly, sharing the result works as a transition condition to future interest on science-related activity or choosing science-related majors.

• Journal of Korean Academy of Nursing Administration
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• v.19 no.3
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• pp.404-413
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• 2013

Purpose: The aim of this study was to identify the effects of change commitment and learning orientation on the innovativeness of clinical nurse. Methods: The participants in this study were 268 nurses, working in hospitals in Seoul, Gyeonggi and Gangwon Provinces, and Daejeon City. Data were collected from June to August, 2012. A structured questionnaire was used for data collect and data was analyzed using the SPSS/WIN program. Results: The most significant predictors of innovativeness were education, normative commitment, continuance commitment and learning commitment. Continuance commitment negatively correlated with innovativeness. Conclusion: These findings suggest that nurses' commitment to change and learning commitment were strongly linked to innovativeness. Management-level workers in these hospitals should have the skills and strategies to promote commitment to change include developing positive expectations about change positive outcomes.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.131-138
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• 1996

In this paper, we consider an iterative learning control system(ILCS) consisting of an iterative learning controller, a feedback controller and a controlled plant in the frequency domain. At first, we review the convergence of ILCS. And we give some design guidelines of the ILCS using a nominal model of the plant. Then we present the structured and the unstructured uncertainty bound which guarantees the convergence of the designed iterative learning controller. In particular, we analyze the relationship between the convergence and the magnitude and phase uncertainties. In order to show the usefulness of the proposed analysis and design guidelines, we present some simulation examples. (author). 13 refs., 5 figs.