This study explored the effects of pre-service elementary teachers' inquiries into science class dilemmas. By closely examining the characteristics of the pre-service teachers' inquiry processes and changes in their educational decisions, the effectiveness of using dilemmas as part of teacher education was determined. Twenty fourth-year university pre-service teachers participated and conducted inquiries into science class dilemmas over seven weeks. Based on pre- and post-questionnaires, KWHL tables, inquiry reports, discussions, and group class presentations, the major factors that influence the pre-service teacher's decision-making changes were extracted. The pre-service teachers found the science inquiry process meaningful when exploring the science topics covered in the dilemmas, and claimed that elementary school students would be able to engage in meaningful science explorations if they learned science through inquiry. Furthermore, the pre-service teachers explored the thinking processes and background knowledge of the students in different ways. Documents such as teacher's guides and the curriculum were examined and the students' thought processes were identified through interviews with the teachers and students, which were found to reflect their educational decision-making. Moreover, it was recognized by the pre-service teachers that depending on the situation, alternative teaching methods were possible. The focus on the unstructured dilemma problems provided the pre-service teachers with problem-solving situations that triggered scientific inquiry and exploration of student thinking and revealed the complexity of science teaching and learning. Based on these results, the teacher education implications for using dilemma cases are discussed.
Journal of the Korea Society of Computer and Information
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v.29
no.3
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pp.217-225
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2024
To foster the core talents of the future, the development of diverse and substantial SW·AI education programs is required, and a systematic system that can assist public education in SW and AI must be established. In this study, we develop and combine SW·AI education modules to construct a SW and AI education program applicable to public education. We also establish a systematic education system and provide sustainable SW·AI education to elementary, middle, and high school students through 'Job's Garage Camp' based on various sharing platforms. By creating a sustainable follow-up educational environment, students are encouraged to continue their self-directed learning of SW and AI. As a result of conducting a pre-post survey of students participating in the 'Job's Garage Camp', the post-survey values improved compared to the pre-survey values in all areas of 'interest', 'understanding and confidence', and 'career aspirations'. Based on these results, it can be confirmed that students had a universal positive perception and influence on SW and AI. Therefore, if the operation case of 'Job's Garage Camp' is improved and expanded, it can be presented as a standard model applicable to other SW and AI education programs in the future.
Purpose: To date, there is no region-specific guideline for pediatric endoscopy training. This study aimed to illustrate the current status of pediatric endoscopy training in Asia-Pacific region and identify opportunities for improvement. Methods: A cross-sectional survey, using a standardized electronic questionnaire, was conducted among medical schools in the Asia-Pacific region in January 2024. Results: A total of 57 medical centers in 12 countries offering formal Pediatric Gastroenterology training programs participated in this regional survey. More than 75% of the centers had an average case load of <10 cases per week for both diagnostic and therapeutic endoscopies. Only 36% of the study programs employed competency-based outcomes for program development, whereas nearly half (48%) used volume-based curricula. Foreign body retrieval, polypectomy, percutaneous endoscopic gastrostomy, and esophageal variceal hemostasis, that is, sclerotherapy or band ligation (endoscopic variceal sclerotherapy and endoscopic variceal ligation), comprised the top four priorities that the trainees should acquire in the autonomous stage (unconscious) of competence. Regarding the learning environment, only 31.5% provided formal hands-on workshops/simulation training. The direct observation of procedural skills was the most commonly used assessment method. The application of a quality assurance (QA) system in both educational and patient care (Pediatric Endoscopy Quality Improvement Network) aspects was present in only 28% and 17% of the centers, respectively. Conclusion: Compared with Western academic societies, the limited availability of cases remains a major concern. To close this gap, simulation and adult endoscopy training are essential. The implementation of reliable and valid assessment tools and QA systems can lead to significant development in future programs.
Jungwoo Lee;Jungsoo Lee;Ji Hun kwon;Minyi Cha;Kyu Tae Kim
Journal of the Institute of Convergence Signal Processing
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v.25
no.2
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pp.100-112
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2024
This study investigates the ethical and legal implications of utilizing artificial intelligence (AI) in human resource management, with a particular focus on AI interviews in the recruitment process. AI, defined as the capability of computer programs to perform tasks associated with human intelligence such as reasoning, learning, and adapting, is increasingly being integrated into HR practices. The deployment of AI in recruitment, specifically through AI-driven interviews, promises efficiency and objectivity but also raises significant ethical and legal concerns. These concerns include potential biases in AI algorithms, transparency in AI decision-making processes, data privacy issues, and compliance with existing labor laws and regulations. By analyzing case studies and reviewing relevant literature, this paper aims to provide a comprehensive understanding of these challenges and propose recommendations for ensuring ethical and legal compliance in AI-based HR practices. The findings suggest that while AI can enhance recruitment efficiency, it is imperative to establish robust ethical guidelines and legal frameworks to mitigate risks and ensure fair and transparent hiring practices.
Urbanization increases the amount of impervious surface and artificial heat emission, resulting in urban heat island (UHI) effect. Local climate zones (LCZ) are a classification scheme for urban areas considering urban land cover characteristics and the geometry and structure of buildings, which can be used for analyzing urban heat island effect in detail. This study aimed to examine the UHI effect by urban structure in Suwon and Daegu using the LCZ scheme. First, the LCZ maps were generated using Landsat 8 images and convolutional neural network (CNN) deep learning over the two cities. Then, Surface UHI (SUHI), which indicates the land surface temperature (LST) difference between urban and rural areas, was analyzed by LCZ class. The results showed that the overall accuracies of the CNN models for LCZ classification were relatively high 87.9% and 81.7% for Suwon and Daegu, respectively. In general, Daegu had higher LST for all LCZ classes than Suwon. For both cities, LST tended to increase with increasing building density with relatively low building height. For both cities, the intensity of SUHI was very high in summer regardless of LCZ classes and was also relatively high except for a few classes in spring and fall. In winter the SUHI intensity was low, resulting in negative values for many LCZ classes. This implies that UHI is very strong in summer, and some urban areas often are colder than rural areas in winter. The research findings demonstrated the applicability of the LCZ data for SUHI analysis and can provide a basis for establishing timely strategies to respond urban on-going climate change over urban areas.
Journal of Korea Entertainment Industry Association
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v.15
no.1
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pp.131-142
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2021
Purpose: Clinical practicums via non-face-to-face methods were inevitable due to the COVID-19 pandemic. We developed an online program for emergency nursing care and identified the feasibility of the program and the learning achievements of students. Methods: This was a methodological study. The program was developed by three professors who taught theory and clinical practicum for adult nursing care and clinical experts. Students received four hours of video content and two task activities every week in four-week program. Real-time interactive video conferences were included. Qualitative and qualitative data were collected. Results: A total of 96 students participated in the program. The mean score for overall satisfaction with the online program was 4.72(±1.02) out of 6. Subjects that generally had high learning achievement scores were basic life support care, fall prevention, nursing documentation, infection control, and anaphylaxis care. As a result of a content analysis of 77 reflective logs on the advantages of this program, students reported that "experience in applying nursing process," "case-based learning and teaching method," and "No time and space constraints" were the program's best features. Conclusion: Collaboration between hospitals and universities for nursing is more important than ever to develop online content for effective clinical practicum.
In the meantime, research on corporate informatization focuses on the completeness of information technology itself and its financial effects, so there is insufficient research on whether information technology can support business strategy. It is necessary to verify whether the management strategy implementation of the company can be led through the informatization of the enterprise and the relation between the main mechanism factors and the informatization performance. In this study, what a mechanism factor is applied in the process of result creation of informatization from three mechanism perspectives such as selecting mechanism, learning mechanism and coordinating mechanism with cases of representative domestic IT company and what an importance mechanism factors have been ascertained. This study results in 8 propositions. For a main agent of companies, securement of information capability of organizations has been selected to realize informatization results and investment of informatization has been selected to solve organizational decentralization problems as the most important factor. Additionally, as competition in the industry gets fierce, investment on informatization has been changed to a utility way of implementation of strategies and decision on investment has been made through the official process and information technology. Differentiated company capability has been made based on acquisition of technical knowledge and company information has been expanded to its whole employees through the information system. Also, informatization change management and outside subcontractor management have been acknowledged as an important adjustment factor of company. The first implication of this study is that since case studies on mechanism factors that preceding studies on informatization results did not empirically cover have directly been dealt with based on experiences of executives in charge of business and in charge of informatization, this study can provide practical views about factors that should be mainly managed for informatization results of IT companies. Secondly, since ser-M framework has been applied for IT companies for the first time, this study can academically contribute to companies in other fields about main mechanism factors for result creation of informatization based on deeper understanding and empirical cases.
Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.
Journal of The Korean Association For Science Education
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v.40
no.2
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pp.97-111
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2020
In the 2015 revised curriculum, 'Integrated Science' was established to increase convergent thinking and designated as a common subject for all students to learn, regardless of career. In addition, the 2015 revised curriculum introduced 'competence' as a distinctive feature from the previous curriculum. In the 2015 revised curriculum, competencies are divided into core competencies of cross-curricular character and subject competencies based on academic knowledge and skills of the subject. The science curriculum contains five subject competencies: scientific thinking, scientific inquiry, scientific problem solving, scientific communication, scientific participation and life-long learning. However, the description of competencies in curriculum documents is insufficient, and experts' perceptions of competencies are not uniform. Therefore, this study examines the perceptions of science subjects in science high school teachers by deciding that comprehension of competencies should be preceded in order for competency-based education to be properly applied to school sites. First, we analyzed the relationship between achievement standards and subject competencies of integrated science through the operation of an expert working group with a high understanding of the integrated science achievement standards. Next, 31 high school science teachers examined the perception of the five subject competencies through a descriptive questionnaire. The semantic network analysis has been utilized to analyze the teachers' responses. The results of the analysis showed that the three curriculum competencies of scientific inquiry, scientific communication, scientific participation and life-long learning ability are similar to the definitions of teachers and curriculum documents, but in the case of scientific thinking and scientific problem solving, there are some gaps in perception and definition in curriculum documents. In addition, the results of the comprehensive analysis of teachers' perceptions on the five competencies show that the five curriculum competencies are more relevant than mutually exclusive or independent.
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