• Korean Medical Education Review
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• v.23 no.1
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• pp.56-67
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• 2021

Educational program evaluation can improve the quality of the curriculum, instructional methods, and resources and provide useful data for making educational decisions and policies. Developing and implementing a program evaluation system is essential in competency-based medical education. The purpose of this study was to explore and establish an educational program evaluation system adapting an integrated program evaluation model to promote competency-based medical education. First, an Educational Evaluation Committee was organized, consisting of faculty, staff members, and students. The committee established an integrated program evaluation model, combining Stufflebeam's Context, Input, Process, and Product (CIPP) model of a process-oriented approach and Kirkpatrick's four-level model of an outcome-oriented approach. Kirkpatrick's model was applied to the product evaluation of the CIPP model. The committee then developed evaluation criteria, indicators, and data collection methods according to the components of the CIPP model and the four levels (reaction, learning, behavior, and results) of Kirkpatrick's model, and collected and analyzed data. Finally, the committee reported the results of evaluation to a Medical Education Quality Improvement Committee, and the results were used to improve the curriculum and student selection. To enhance the quality of education, identifying educational deficiencies and developing various elements of education in a balanced way through educational evaluation will be needed. Furthermore, it will be necessary to listen to opinions of various stakeholders, work with all members involved in education, and communicate with decision-makers in the process of educational evaluation.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.445-457
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• 2022

The purposes of this research are to identify the subjective perception type of science and engineering graduates about good university and to analyze the differences of their undergraduates' experiences among types. Using Q methodology, 29 statements about a good university, reflecting on the previous research as well as quality assurance criteria in higher education, were administered to 16 science and engineering graduates for ranking using a Q-sort procedure. As a result 16 graduates were classified into three types according to their preference for 29 statements. Type 1, oriented student experience, recognized that a good university encourages students to participate in various activities and experiences. Type 2, oriented institutional outcomes, recognized that a good university is ranked high in criteria such as employment rate, research outcome, and entrance exam scores etc. Type 3, oriented educational activity, recognized that a good university is regarded as a community focusing on teaching and learning. Finally, considering the finding of the research, some pedagogical and administrational implications were suggested for quality improvement in higher education.

• Computers and Concrete
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• v.32 no.2
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

• Korean Journal of Schizophrenia Research
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• v.20 no.1
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• pp.15-22
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• 2017

Objectives : It has been shown that early intervention is crucial for favorable outcome in patients with schizophrenia. However, development of biomarkers for predicting prognosis of psychotic disorder still requires more research. In this study, we aimed to investigate whether baseline mismatch negativity (MMN) predict prognosis in patients with first episode psychosis (FEP). Methods : Twenty-four patients with FEP and matched healthy controls (HCs) were examined with MMN at baseline, and their clinical status were re-assessed after 1 year. Repeated-measures analysis of variance was performed to compare baseline MMN between the two groups. Multiple regression analysis was used to identify factors predicting prognosis in FEP patients during the follow-up period. Results : MMN amplitudes at baseline were significantly reduced in patients with FEP compared to healthy controls. In the multiple regression analysis, baseline MMN amplitude significantly predicted later improvement of performances on digit span and delayed recall of California Verbal Learning Test. However, baseline MMN did not predicted improvement of clinical symptoms. Conclusion : These results indicate that MMN may be a possible predictor of improvement in cognitive functioning in patients with FEP. Future study with larger sample and longer follow-up period would be needed to confirm the findings of the current study.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.26 no.1
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• pp.17-30
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• 2022

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.493-509
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• 2024

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

• Journal of The Korean Association For Science Education
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• v.25 no.7
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• pp.746-753
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• 2005

In order to be efficient teachers should understand the current level of leaners through diagnostic evaluation. However, it is arduous to administer a diagnostic examination in every class because of various limitations. This study examined, the major issues arising from the development of a new science diagnostic evaluation system by incorporating the using knowledge state analysis method. The proposed evaluation system was based on the knowledge state analysis method. Knowledge state analysis is a method where by a distinguished collection of knowledge uses the theory of knowledge space. The theory of knowledge space is very advantageous when analyzing knowledge in strong hierarchies like mathematics and science. It helps teaching plan through methodically analyzing a hierarchy viewpoint for students' knowledge structure. The theory can also enhance objective validity as well as support a considerable amount of data fast by using the computer. In addition, student understanding is improved through individualistic feedback. In this study, an evaluation instrument was developed that measured student learning outcome, which is unattainable from the existing method. The instrument was administered to pre-service physics teachers, and the results of student evaluation was analyzed using the theory of knowledge space. Following this, a revised diagnostic evaluation system for facilitating student individualized learning was constructed.

• Journal of Educational Research in Mathematics
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• v.14 no.2
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• pp.207-219
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• 2004

There have been studies reporting the increase in student confidence in mathematics when using technology. However, past studies indicating a positive correlation between technology and confidence in mathematics do not explain why they see this positive outcome. With increased availability and easy access to the Internet in schools and the development of free online virtual manipulatives, this research was interested in how the use of virtual manipulatives in mathematics can affect students confidence in their mathematical abilities. Our hypothesis was that the classes using virtual manipulatives which allows students to connecting dynamic visual image with abstract symbols will help students gain a deeper conceptual understanding of math concept thus increasing their confidence and ability in mathematics. The participants in this study were 46 fifth-grade students in three ability groups: one high, one middle and one low. During a two-week unit on fractions, students in three groups interacted with several virtual manipulative applets in a computer lab. Data sources in the project included a pre and posttest of students mathematics content knowledge, Confidence in Learning Mathematics Scale, field notes and student interviews, and classroom videotapes. Our aim was to find evidence for increased level of confidence in mathematics as students strengthened their understanding of fraction concepts. Results from the achievement score indicated an overall main effect showing significant improvement for all ability groups following the treatment and an increase in the confidence level from the preassessment of the Confidence in Learning Mathematics Scale in the middle and high ability groups. An interesting finding was that the confidence level for the low ability group students who had the highest confidence level in the beginning did not change much in the final confidence scale assessment. In the middle and high ability groups, the confidence level did increase according to the improvement of the contest posttest. Through interviews, students expressed how the virtual manipulatives assisted their understanding by verifying their answers as they worked and facilitated their ability to figure out math concept in their mind and visually.

• 대한공업교육학회지
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• v.34 no.2
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• pp.248-266
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• 2009

The purpose of the research was to find out the difference in students' academic achievement in Technology Education between students with a lecture, those who perform a content-related project after a lecture, and those who carry out a content-related project without a lecture. The results of this study are as follows. First, taking advantage of both a lecture and project-based lesson led to better achievement than using only a project in Technology Education subject of an academic high school in academic achievement in 'knowledge' area. I infer that it is because they reviewed what they had learned in a lecture and the preparation and practice of the project caused them to memorize it. Second, there was not a meaningful difference in academic achievement in 'understanding' area among the group with a lecture, the one with both a lecture and a project, and the one with only a project. However, considering the comparison of averages and the p-value of F-test, I can deduce that the test outcome influences students with a lecture and a project positively in terms of academic achievement. Third, there was not a meaningful difference in the academic achievement in 'adaptation' area among the group with a lecture, the one with a lecture and a project, and the one with a project. I can conclude that those results are because the difficulty level of evaluation was high and they produced a model just by copying textbook contents.