Background: Competency-based education is increasingly emphasized in physical therapy, yet limited research has assessed student competencies using validated tools. Objectives: This study examined differences in physical therapy students’ competencies by gender, grade level, and admission type using a newly developed assessment tool. Design: Cross-sectional comparative study. Method: A total of 88 students at U University completed the assessment, which comprised six Sub-Competenc ies (Communication skills, Client understanding competency, Learning Competency for Physical Therapy, Problem- Solving Competencies for Physical Therapy, Factor application capabilities, Professional development competency). Data were analyzed using t-tests and ANOVA with Scheffé post-hoc tests. Results: No significant differences were observed among gender and grade levels (P>.05). By admission type, regular admission students scored significantly higher than transfer students in the Learning Competency for Physical Therapy and the Factor application capabilities (P<.05). Conclusion: Competency development appears unaffected by gender or grade level but may differ by admission type. Findings suggest the need for academic support and supplementary education for transfer students, and provide evidence to guide competency-based curriculum design in physical therapy.

The chip processing system of large scale machine tool, such as planomiller, turning machine, boring machine and CNC machine, has been continuously used in many industrial fields. As the performance of chip processing system is improved, cutting work with high-precision is also required. This study aims to study the characteristics of the edged part of cutter depending on removing the cutter support in cutter assembly. As the results, the damaged spot in edged part of cutter was different whether the cutter support was installed or not. By removing the cutter support, the safety factor of edged part of cutter was decreased about 4.7 times and furthermore there were some advantages in less than 1.7kN of cutting force.

최근 4차 산업 혁명의 도래와 함께 기술의 발전에 따라 자율운항 선박에 대한 관심이 높아지고 있다. 미래의 선박은 고기량 선원들 의 육상 근무 선호도 증가와 승선 인원 제한으로 인해 점차적으로 낮은 기량의 선원들이 승선하며 선원 수가 감소될 전망이다. 따라서 선박의 안전 운항을 위한 운동 및 조종의 제어뿐만 아니라 자율운항 선박의 원활한 유지보수를 위해 증강현실 기반의 원격 유지보수 시스템이 필요하며 현재 개발이 활발히 진행 중이다. 증강현실 기반 원격 유지보수 시스템에서는 3D모델만 가시화하는 것은 활용성 이 떨어진다. 또한, 애니메이션을 개발하는 것은 개발 플랫폼에 대한 의존도가 높기 때문에 호환성 및 활용성이 떨어진다. 이러한 문 제를 해결하기 위해 손쉽게 정비 애니메이션을 만들 수 있는 저작도구가 필요하다. 따라서, 본 연구에서는 이러한 문제를 해결하기 위 해 Json 파일 형식으로 정비 애니메이션 중립 포맷을 만들어 경량화 및 호환성 높은 정비 애니메이션을 위한 저작도구를 개발하였다. 또한, 프로토타입을 개발하여 활용성을 검증하였고, 다른 플랫폼과의 호환성 검증을 진행하였다. 마지막으로, 애니메이션을 제작할 수 있는 소프트웨어와의 비교를 통해 제안한 저작도구의 유효성을 입증하였다.

지금 우리의 시대를 지칭하는 용어로 얼마 전까지 사용하던 4차산업혁명시 대보다 ‘AI 시대’가 더 적합한 것으로 보인다. 4차산업혁명의 기술적 변화 가 운데 가장 직접적이고 가시적으로 그 변화를 체감하게 하기 때문이다. 이전에 도 기존의 기보(棋譜)를 암기, 학습하던 수준에서 스스로의 수(手)를 찾아낸 AlphaGo처럼, 특정 분야에서의 활용 위주로 설계된 AI들도 상당한 능력을 보여준 바 있다. 하지만 이렇게 특정 분야에 특화된 것이 아니라, 다양한 목 적에 적용될 수 있는 범용인공지능이 그 학습속도와 역량이 역시 비약적으로 발전되어 스스로 질문하고 답하는 단계에 이른다면 그 영향력은 무엇을 상상 하든 그 이상이 될 것이다. 물론 GPT, 라마(LLaMA) 등 범용인공지능의 능 력이 개발사 혹은 그 추종자들이 홍보, 찬양하고 있는 것만큼 발전되어 있는 가에 대한 의문도 있다. 또한 기술적 진보를 중시하고 그에 의한 부작용 역시 기술적으로 극복가능할 것이라고 보는 낙관론자와 달리 기술적 진보가 가져 올 부작용을 두려워하는 비관론자 혹은 그 과도기적 상황에서 나타날 문제를 더 크게 보는 사람도 있다. 그리고 부인하기 힘든 것은 우리가 가진 미래에 대한 인상은 앞선 시대에 예술적 천재들이 미리 엿보고 와서 그려낸 기술적 dystopia가 그려진 영화, 드라마, 소설의 영향에서 자유롭지 못하다는 점이 다. 지금의 기술적 진보수준을 정확하게 평가하기 어렵고, 그 미래는 더더욱 예측하기 힘든 나의 입장에서 더 크게 보이는 것은 기술적 진보가 해결해줄 문제들보다 기술적 진보가 초래하는 새로운 문제들이다. AI와 관련된 문제는 앞으로도 오랫동안 우리 시대의 화두가 될 것으로 보 인다. 그러나 적어도 지금의 단계에서 AI는 막연한 두려움이나 기대의 후광 으로 인해 그 정확한 실체를 알아보기 힘들다. 또한 하루가 다르게 발전하는 관련 기술의 진보에 따라 오늘의 논의는 내일의 문제를 해결하기 위해서 무 용(無用)해지기 십상이다. 지나친 공포로 인해 관련기술의 발전을 억제하는 것도, 막연한 낙관으로 인해 우리가 통제할 수 없는 괴물을 우리 손으로 창조하 는 것도 경계할 필요가 있을 것이다. 다만 지금의 수준에서는 AI의 도구적 성격에 주목하여 그 유용성을 활용하면서도 부작용 혹은 그 가능성을 억제, 감시할 수 있어야 하고, 이러한 노력은 국가 단위를 넘어서 범지구적으로도 협력할 필요가 있을 것으로 보인다.

This study develops a machine learning-based tool life prediction model using spindle power data collected from real manufacturing environments. The primary objective is to monitor tool wear and predict optimal replacement times, thereby enhancing manufacturing efficiency and product quality in smart factory settings. Accurate tool life prediction is critical for reducing downtime, minimizing costs, and maintaining consistent product standards. Six machine learning models, including Random Forest, Decision Tree, Support Vector Regressor, Linear Regression, XGBoost, and LightGBM, were evaluated for their predictive performance. Among these, the Random Forest Regressor demonstrated the highest accuracy with R2 value of 0.92, making it the most suitable for tool wear prediction. Linear Regression also provided detailed insights into the relationship between tool usage and spindle power, offering a practical alternative for precise predictions in scenarios with consistent data patterns. The results highlight the potential for real-time monitoring and predictive maintenance, significantly reducing downtime, optimizing tool usage, and improving operational efficiency. Challenges such as data variability, real-world noise, and model generalizability across diverse processes remain areas for future exploration. This work contributes to advancing smart manufacturing by integrating data-driven approaches into operational workflows and enabling sustainable, cost-effective production environments.

This paper delves into the standard system for selecting aviation tools. Generally, standards are seen as foundational and fundamental. However, in actual practice, there are often instances where a thorough differentiation of the standard unit system isn't properly executed, resulting in product defects in certain companies. Therefore, through the insights gathered in this study, we aim to reaffirm the basic principles and move forward with the objective of manufacturing products of impeccable quality in accordance with future quality improvement policies. Ultimately, we aspire for K-Defense to emerge as a prominent leader in the global market.

Background: The shift from traditional education to competency-based education in response to societal demands, emphasizes the need for developing assessment tools to measure major competencies—comprising knowledge, skills, attitudes, and personal characteristics—required to perform specific tasks. Thus, It is essential to develop assessing tools to measure the existing major competencies in physical therapy, enabling more effective management of educational outcomes on competencies. Objectives: The purpose of this study is to develop an assessment tool for measuring major competencies within the Department of Physical Therapy. Design: Delphi survey research. Methods: This study was conducted based on the three major competencies and six sub-competencies of the Physical Therapy Department. To develop an assessing tool for measuring major competencies, conversion of the achievement factor into behavioral statements, and expert panel group Delphi survey was conducted. Results: The results of the Delphi survey indicated 0.88 to 1, exceeding the established threshold and demonstrating adequate validity. Conclusion: A total of 54 preliminary questions for the major competency assessing tool were developed through the Major Curriculum Committee. Through the Delphi survey, the validity of the 54 preliminary questions for major competencies was secured.

본 연구는 영유아 부모 개개인의 전반적인 성향 및 양육과 관련한 다양한 요인들을 파악하여 보다 효과적이고 바람직한 부모역할을 지원하기 위한 검사도구를 개발하고 표 준화하기 위하여 수행되었다. 이를 위해 전국의 영아(15～36개월)와 유아(만 3～7세)를 둔 부모를 대상으로 설문조사를 시행하였다. 총 4,237부의 설문을 수집하였으며 응답의 극단치를 나타내거나 응답신뢰도가 낮은 234명의 데이터를 제외하고 최종적으로 4,003부 (영아 1,772부, 유아 2,231부)를 분석에 반영하였다. 본 연구의 결과 영아용 검사도구는 영아 기질 12문항, 발달특성 9문항, 정서행동특성 9문항으로 구성되었으며, 유아용 검사 도구는 유아 기질 14문항, 발달특성 11문항, 정서행동특성 14문항으로 구성되었다. 부모 용 검사도구는 상호작용 12문항, 양육효능감 11문항, 양육스트레스 6문항으로 구성되었 다. 부모자녀 관계검사의 문항의 신뢰도와 타당도를 검증한 결과 문항의 내적일치도와 검사-재검사 신뢰도 모두 본 검사도구의 신뢰도에 문제가 없는 것으로 나타났다. 또한, 검사도구의 타당도를 검증하기 위해 하위요인 간 상관검사, 확인적 요인분석을 시행한 결과 모든 지표에서 본 검사 가 문제가 없는 것으로 나타났다.

Purpose: Nursing students' competence in virtual simulation-based learning is a key factor in its success. This study explored the validity and reliability of a virtual-simulation-based learning competency self-evaluation tool for nursing students. Methods: Data were collected from a web-based survey. First, 11 nursing professors participated in a focus group interview, and 7 simulation education experts participated in the preliminary item content validity. The participants in these two aspects were not the same. Then, a preliminary survey was conducted with 15 fourth-year nursing students in I City. Next, based on these three efforts, a final survey comprising 20 evaluation items was developed. This survey was administered to third- and fourth-year nursing students at four nursing colleges in Korean provinces (Seoul, Gyeonggi, Gangwon, and Gyeongsan-do); 222 complete questionnaires were used for the final analysis. Further, Kirkpatrick’s evaluation model was used for four steps each of tool development and verification processes of the associated psychometric aspects, for a total of eight steps. An exploratory factor analysis was performed on the collected survey data, and verify the tool's validity and reliability. Results: Four factors comprising 15 items explained 66.59% of the variance: learning preparation and start-up (4 items), nursing assessment (3 items), data interpretation (3 items), and problem solving (5 items). The Cronbach's α of the tool was 0.74, and that of the factors ranged from 0.72 to 0.80. Conclusions: The tool's validity and reliability were demonstrated using established methodologies. This tool can be useful for evaluating Korean nursing students' virtual simulation learning competence.

This study analyzes students’ use of ChatGPT prompts to explore its potential as a supplementary feedback tool in English writing classes. Thirty-one pre-service teachers participated and were divided into high, middle, and low groups based on their self-evaluation, standardized test scores, and essay scores prior to receiving ChatGPT feedback. The data sources included their two essays, ChatGPT prompts, questionnaires, and transcripts from the second writing conference. The ChatGPT prompts and questionnaires were analyzed using descriptive statistics, and the writing conference transcripts were examined to understand the participants’ use of prompts. The results showed participants used prompts 40 times in the first assignment and 175 times in the second assignment. The average prompt usage increased from 1.5 times in the first assignment to 6.7 times in the second assignment. In terms of students’ levels, the high group used more prompts (5.58 times) than the middle (5 times) and the low groups (1.75 times). Notably, students who used ChatGPT commands five times or more were mostly from the high and middle groups. Differences in prompt usage patterns were also identified, with the high and middle groups engaging in more continuous and interactive conversations with ChatGPT. Students expressed satisfaction with ChatGPT’s feedback, particularly in vocabulary selection, grammar correction, and sentence generation.