• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.250-267
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• 2017

Maker activity, mainly practiced in informal or non-formal education environments activities, was expanded to the form of maker education' due to its various educational values and effects. Yet, one of the difficulties in practicing the maker education in school education is the lack of makerspace as a space for the maker activities. In this context, this study aimed to examine the process of how the students make the makerspace in their school and to define its educational effects defined as 'maker spirits.' For this purpose, this study developed a maker education program for 22 $10^{th}$ graders in an high school for 8 weeks who had participated in the project of 'Making Makerspace'. The results of the program were analyzed through data collected from reflective journals, interview, and observation journals. In conclusion, this study presented a practical and helpful way to make 'Makerspace' in school and at the same time, confirmed Maker education as constructivist learning environments re-encountered in the $21^{st}$ and as an alternative learning approach suitable for the $4^{th}$ Industrial Revolution Age.

• Journal of The Korean Association For Science Education
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• v.35 no.4
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• pp.573-584
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• 2015

Inquiry has been consistently emphasized in science education as a crucial element for learning. Although many researchers came to agree on the importance of scientific inquiry, authentic inquiry activities are hard to be actualized in an educational context. Therefore it is required to critically examine what teachers have difficulty in teaching inquiry. In this article, we looked into inquiry-based science activities in a small group setting where pre-service science teachers faced dilemmas between cognition and practice of inquiry. A case study was conducted on eight undergraduate students who are majoring in science education. The participants attended a weekly science program for middle school students in low SES as teaching assistants and mentors, and took full care of his/her mentees during open-inquiry activities. The results were drawn by analyzing participants' personal and group interviews, participant observations, self-reports, and others. The pre-service teachers viewed the knowledge and procedure of science as an essential factor in inquiry activities along with student's spontaneous attitude. However, in the process of performing inquiry, they faced several dilemmas between ideal cognition and real activities. The aspects of dilemmas could be summarized in three pairs of opposing concepts: 'diverging inquiry or converging science', 'interest-centered inquiry or learning-centered inquiry', and 'student as the subject or student with the insufficient expertise.' We discussed ways of resolving dilemmas and alternative perspectives on scientific inquiry.

• Journal of Educational Research in Mathematics
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• v.22 no.2
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• pp.203-227
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• 2012

This was to investigate how the slow learners who specially belonged to low-SES, or North Korean defectors showed their errors in mathematical learning. To conduct the study, two groups for each minority group participated in the study volunteerly during the Winter vacation, in 2011. Based on the preliminary interviews, a total of 15 units were given, focusing on building mathematical basic concepts. As results, they had some errors in common. They both were in lack of understanding of the terminologies and not able to apply the meanings of definitions and theorems to a problem. Because of uncertainty of basic knowledge of mathematics, they easily lost their focus and were apt to make a mistake. Also, they showed clear differences. North Korean defectors were not accustomed to using or understanding the meanings of Chines or English in Korean words in expressing, writing mathematical terminologies and reading data on the context. Technical errors, and misinterpreted errors were found. However, students from the low SES showed that they were familiar with mathematical words and terminologies, but their errors mostly belonged to carelessness because of the lack of mastering mathematical concepts.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.157-165
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• 2018

The purpose of this study is to investigate the effectiveness of general use of English literary works. It will be helpful for both general English learners and college students majoring English Education in ESL or EFL context. English literature is very useful pedagogical tool in the language class due to its unique valuable characteristics including authenticity, cultural and linguistic value, and personal enrichment, which impact on fostering English ability of EFL students. For this reason, it is unavoidable to develop a theory and practice regarding using English literature as an educational resource for college students in Korea. In this study several considerations will be discussed in terms of selection of the literary works to be applied for language learning purpose in the classrooms of universities in Korea. Such attentions will include fours skills of English such as reading, writing, listening and speaking. Finally, some effects and implications of using literary text as a pedagogical tool in the EFL language classrooms will be discussed.

• Journal of Educational Research in Mathematics
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• v.13 no.3
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• pp.287-307
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• 2003

This study analyzed the concept of equal symbol(=) that is the most symbol used in learning of mathematics and investigated students' understanding of that. The equal symbol is endowed with the 'same', 'equal', and 'equivalent' meaning, represented by =, but students interpret the meaning of equal symbol according to the mathematical con text. Thus, we analyzed the equal symbol on the basis of the theory of conceptual fields. In the theory of conceptual fields, concept is a three-tuple of three sets of situation, operational invariants and symbolic representations, and the operational invariants are the concept-in-action and the theorems- in-action. With the analysis contents, we investigated how students read = by korean, what equals in the expression containing = or by what meaning students used =, and which they could correct the error for =. This study imply that we should consider the symbol notation agreed by mathematical society, the meaning, and the situational context that it used, when we teach the mathematics symbols.

• Clinical and Experimental Pediatrics
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• v.64 no.11
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• pp.575-581
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• 2021

Background: Toxocariasis is a zoonotic parasitic disease caused by Toxocara canis and Toxocara cati in humans. Various types of T. canis are important. Purpose: The current study aimed to investigate the prevalence of Toxocara spp. in pediatrics in the context of a systematic review and meta-analysis. Methods: The MEDLINE (PubMed), Web of Sciences, Embase, Google Scholar, Scopus, and Cumulative Index of Nursing and Allied Health databases were searched to identify peer-reviewed studies published between January 2000 and December 2019 that report the prevalence of Toxocara spp. in pediatrics. The evaluation of articles based on the inclusion and exclusion criteria was performed by 2 researchers individually. Results: The results of 31 relevant studies indicated that the prevalence of Toxocara spp. was 3%-79% in 10,676 cases. The pooled estimate of global prevalence of Toxocara spp. in pediatrics was 30 (95% confidence interval, 22%-37%; I²=99.11%; P=0.00). The prevalence was higher in Asian populations than in European, American, and African populations. Conclusion: Health policymakers should be more attentive to future research and approaches to Toxocara spp. and other zoonotic diseases to improve culture and identify socioeconomically important factors.

• Journal of People, Plants, and Environment
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• v.24 no.3
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• pp.301-309
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• 2021

Background and objective: The purpose of this study is to analyze the case of healing experience for lifestyle and environmental diseases through life and activities in the forest from the perspecitive of critical realism, and how the causal power and mechanism of the healing experience relate to forest healing factors and programs. Methods: 93 video data of people who started living in the forest for disease treatment were analyzed using a qualitative content analysis method from the perspective of critical realism. Categories for analysis include general categories (age, duration, occupation, disease name), forest therapy categories (climate therapy, plant therapy, water therapy, diet therapy, kinesiotherapy, psychotherapy), and other categories (ecology, learning and management, life tools), etc., and the unit of analysis is the context unit. Results: 1) The diseases that motivated life in the forest were digestive system diseases, lung diseases, cardiovascular diseases, endocrine system diseases, and various lifestyle-related diseases and environmental diseases in similar proportions. This indicates that forest life does not have specificity to respond to specific diseases, but provides treatment and recovery for all lifestyle and environmental diseases. 2) Among the forest therapies, climate therapy and plant therapy are related to the climatic and residential environment in the forest where 'natural persons' live. And others such as water therapy, diet therapy, kinesiotherapy, psychotherapy indicate the change from the lifestyle that caused the disease to the lifestyle for treatment and recovery. Conclusion: Life and activities in the forest provide an environment for treatment and recovery in which the healing principles such as aromatherapy, nutritional and dietary therapy, kinesiotherapy, and emotional psychotherapy are integrated in the 'real world'.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.