Earticle

이 연구의 목적은 EFL (외국어로서 영어를 사용하는) 학생들이 Padlet에 프로필을 게시하여 다른 학생들이 볼 수 있도록 하는 것이 학생들의 영어 글쓰기 자기 효능감(WSE), 글쓰기 품질 및 글쓰기 경험에 미치는 영향을 확인하는 것이다. 이 연구에서는 두 그룹의 학생에게 세 문단 으로 구성된 데이트 프로필을 작성하도록 하였으며 실험 그룹만이 자신의 프로필을 온라인에 게시하도록 하였다. 학생들은 SEWS (Self-Efficacy for Writing Scale) 및 LWS (Likeing Writing Scale)를 완료하였으며, 연구자는 수정된 Educational Testing Service (ETS) 작문 기준 표를 사용하여 프로필의 품질을 평가함으로써 글쓰기 품질의 차이를 확인하였다. 마지막으로, 학생들의 글쓰기 경험의 차이를 확인하기 위해 주제에 대해 학기말 인터뷰를 분석하였다. 두 그 룹의 SEWS 점수와 LWS 점수 간에는 유의미한 차이가 발견되지 않았다. 그러나 실험 그룹은 프 로필의 쓰기 평균 점수와 세 문단 중 두 문단의 쓰기 평균 점수가 상당히 높았다. 학생들이 프로 젝트를 논의하면서 제기한 주제에서도 두 그룹 간에 차이가 있었다. 프로필을 Padlet에 게시하 지 않은 학생들은 인터뷰에서 자신을 언급한 경우가 더 많았던 반면, 프로필을 Padlet에 게시한 학생들은 친구나 온라인 프로필에 대해 이야기하려는 경향이 더 컸다. 이 연구는 Padlet에 학생 프로필을 게시하는 것이 EFL 학생들이 영어 글쓰기를 배우는 데 도움이 된다는 결론을 내렸다. 연구 규모의 한계로 인하여 연구에 참여한 학생들을 넘어 결과를 추정하기는 어렵지만, 부분적 으로는 비교 그룹의 학생들이 주제를 벗어난 글쓰기를 하여 실험 그룹 학생들의 글쓰기 품질이 비교 그룹 학생들의 글쓰기 품질보다 월등히 높았으며, 두 그룹 학생들의 글쓰기 경험에도 여러 가지 차이점들이 있었다.

The purpose of this study was to determine the effects of posting student profiles to Padlet, to be accessible to other students, on writing self-efficacy (WSE), writing quality, and the writing experience of English as a Foreign Language (EFL) students. In the study, two groups of students were assigned to compose three-paragraph dating profiles and only the experimental group posted their profiles online. The students completed a Self-Efficacy for Writing Scale (SEWS) and a Liking Writing Scale (LWS) and the profiles were evaluated for quality using a modified Educational Testing Service (ETS) writing rubric to determine differences in writing quality. Finally, end of semester interviews were analyzed for themes to determine differences in the students writing experience. No significant difference was found between the SEWS or the LWS scores of the two groups. However, the experimental group had a significantly higher average writing score for the profile and also for two of the three paragraphs. There were also differences between the two groups in the themes that were brought up by students while discussing the project. While more students who did not post their profiles mentioned themselves in interviews, students who posted their profiles were more inclined to talk about friends or online profiles. The study concluded that posting student profiles to Padlet did have benefits for EFL students learning writing. Although, due to the small size of the study, it is difficult to extrapolate the results beyond the students in the study, it showed a significantly higher quality of writing for the students under treatment partly due to writing off topic by the control students. The student experience also had several differences between groups.

이 연구의 목적은 EFL (외국어로서 영어를 사용하는) 학생들이 Padlet에 프로필을 게시하여 다른 학생들이 볼 수 있도록 하는 것이 학생들의 영어 글쓰기 자기 효능감(WSE), 글쓰기 품질 및 글쓰기 경험에 미치는 영향을 확인하는 것이다. 이 연구에서는 두 그룹의 학생에게 세 문단 으로 구성된 데이트 프로필을 작성하도록 하였으며 실험 그룹만이 자신의 프로필을 온라인에 게시하도록 하였다. 학생들은 SEWS (Self-Efficacy for Writing Scale) 및 LWS (Likeing Writing Scale)를 완료하였으며, 연구자는 수정된 Educational Testing Service (ETS) 작문 기준 표를 사용하여 프로필의 품질을 평가함으로써 글쓰기 품질의 차이를 확인하였다. 마지막으로, 학생들의 글쓰기 경험의 차이를 확인하기 위해 주제에 대해 학기말 인터뷰를 분석하였다. 두 그 룹의 SEWS 점수와 LWS 점수 간에는 유의미한 차이가 발견되지 않았다. 그러나 실험 그룹은 프 로필의 쓰기 평균 점수와 세 문단 중 두 문단의 쓰기 평균 점수가 상당히 높았다. 학생들이 프로 젝트를 논의하면서 제기한 주제에서도 두 그룹 간에 차이가 있었다. 프로필을 Padlet에 게시하 지 않은 학생들은 인터뷰에서 자신을 언급한 경우가 더 많았던 반면, 프로필을 Padlet에 게시한 학생들은 친구나 온라인 프로필에 대해 이야기하려는 경향이 더 컸다. 이 연구는 Padlet에 학생 프로필을 게시하는 것이 EFL 학생들이 영어 글쓰기를 배우는 데 도움이 된다는 결론을 내렸다. 연구 규모의 한계로 인하여 연구에 참여한 학생들을 넘어 결과를 추정하기는 어렵지만, 부분적 으로는 비교 그룹의 학생들이 주제를 벗어난 글쓰기를 하여 실험 그룹 학생들의 글쓰기 품질이 비교 그룹 학생들의 글쓰기 품질보다 월등히 높았으며, 두 그룹 학생들의 글쓰기 경험에도 여러 가지 차이점들이 있었다.

The purpose of this study was to determine the effects of posting student profiles to Padlet, to be accessible to other students, on writing self-efficacy (WSE), writing quality, and the writing experience of English as a Foreign Language (EFL) students. In the study, two groups of students were assigned to compose three-paragraph dating profiles and only the experimental group posted their profiles online. The students completed a Self-Efficacy for Writing Scale (SEWS) and a Liking Writing Scale (LWS) and the profiles were evaluated for quality using a modified Educational Testing Service (ETS) writing rubric to determine differences in writing quality. Finally, end of semester interviews were analyzed for themes to determine differences in the students writing experience. No significant difference was found between the SEWS or the LWS scores of the two groups. However, the experimental group had a significantly higher average writing score for the profile and also for two of the three paragraphs. There were also differences between the two groups in the themes that were brought up by students while discussing the project. While more students who did not post their profiles mentioned themselves in interviews, students who posted their profiles were more inclined to talk about friends or online profiles. The study concluded that posting student profiles to Padlet did have benefits for EFL students learning writing. Although, due to the small size of the study, it is difficult to extrapolate the results beyond the students in the study, it showed a significantly higher quality of writing for the students under treatment partly due to writing off topic by the control students. The student experience also had several differences between groups.

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

This paper presents a real-time vehicle detection and tracking system using an unmanned aerial vehicle (UAV) to address challenges in dynamic urban environments. The system combines a convolutional neural network (CNN) for vehicle detection with a deep Q-network (DQN)-based navigation policy for continuous tracking. Input images are enhanced using contrast limited adaptive histogram equalization (CLAHE) and unsharp masking. The CNN jointly predicts vehicle center coordinates and probabilistic heatmaps, while a self-attention module captures long-range spatial dependencies to improve detection under clutter and occlusion. The DQN is trained on multi-step spatiotemporal states to learn optimal UAV movement strategies under diverse weather and structural conditions. Experiments conducted in a three-dimensional (3D) urban simulation environment using Unity’s machine learning agents (ML-Agents) show that the self-attention design reduced pixel-level localization error by about 7%, and the DQN-based tracking policy achieved stable convergence after approximately 2000?3000 episodes. These results demonstrate high tracking accuracy and system stability, highlighting the potential of the proposed approach for real-world UAV-based traffic monitoring applications.

Task–based reading activities are of crucial value today, and consequently learners’ proficiency is more important than their abstract knowledge of language rules. It seems that learners’ familiarity with task-based reading activities may increase learners’ proficiency. Therefore, this study investigated the effect of task-based reading activities such as text completion and pupil generated questions on vocabulary learning and retention of Iranian intermediate EFL learners. To conduct the study, three intact classes of learners who had already finished Top Notch Fundamental A and B (Saslow & Ausher, 2011) in previous semesters in an English language institute were selected as the participants of the study. To ensure the homogeneity of the participants, those who got a score between 30-47 from the total score of 60 in OPT were selected as the intermediate level for main participants of the study (N=47). As the data were normally distributed, one way ANOVA and repeated measure ANOVA were employed for the statistical analyses of the study. The findings indicated that using task-based reading activities such as text completion and pupil-generated questions has significant and meaningful impacts on Iranian EFL learners’ vocabulary learning and retention. The implementations of the study are discussed.

With the emergence of sociolinguistic approach in second language learning, instructors have begun to acknowledge the native language (L1) as a legitimate tool with the potential to facilitate second language (L2) learning mainly in output-based tasks. This study focuses on how the use of L1 by L2 learners affects their task achievement. Two communicative tasks were used, and tasks were carried out by 32 high school students in L2 only, or in L1 and L2 during group test sessions held on different days. Tape-recorded transcripts of learners when performing the tasks, interviews, and questionnaires were collected and analysed to investigate effect on the use of L1. The findings suggest that during performing the tasks, learners used their L1 to complete their tasks for a variety of functions. Through the L1, they explained and negotiated the task each other, or checked their understanding or compared answers to the task against their peers. To allow the learners to use of their L1 was even more effective than to urge them to use L2 only in a foreign language class using task. Further investigation indicates that, if one of goals of output-based introduction is considered as the successful completion of the task, the learners’ use of L1 may be beneficial to attain the goal when performing completely in L2 is impossible or beyond the learners’ linguistic ability.

Background: This pilot study aims to develop an automated, multi-task deep learning system as a proof of concept for both the classification of pregnancy status and gestational day prediction in Korean crossbred goats. Methods: Two 1-year-old goats were followed longitudinally until mid to late gestation. A complete data set containing 11,092 high-resolution image frames (6,647 pregnant and 4,445 non-pregnant) was created from the video sequences. The dataset was segmented at the frame level with 80% allocated to the training partition (n = 8,875), 10% to the validation partition (n = 1,110), and 10% to the test partition (n = 1,107). Multi-task Four convolutional neural networks (ResNet18, EfficientNet-B0, MobileNetV3-Large, and ConvNeXt-Tiny) were trained following a multi-task learning strategy with the AdamW optimizer, OneCycle scheduling, and a hybrid Binary Cross- Entropy and Huber loss function. Results: Accuracy was achieved for the four architectures, with more than 99.5%. The MobileNetV3-Large model performed the best in terms of frame-level classification accuracy, with 100% sensitivity and specificity. For the regression-based task for predicting gestational days, EfficientNet-B0 got the best performance with a framelevel Mean Absolute Error (MAE) of 1.3 days. The video-level MAE of 0.882 days and the competitive video MAE of 1.204 days were the outcomes for the aggregated video sequences using ConvNeXt-Tiny and EfficientNet-B0, respectively. Conclusions: The present pilot study lays the foundation for the methodological feasibility of small optimized deep learning models for accurate monitoring of fetal development and for the automation of reproductive diagnostics.

연구목적: 본 연구는 플랜트 건설현장에서 유해·위험요인을 체계적으로 식별하고 평가할 수 있는 딥러닝 기 반 작업 위험성평가 모델을 구축하는 데 목적이 있다. 특히, 경험과 지식이 부족한 근로자도 쉽게 활용할 수 있도록 체계적인 시스템을 설계하여, 기존의 경험기반 평가 방식의 문제를 해결하고자 한다. 연구방법: 국내 플랜트 건설사인 A사의 976,140건의 위험성평가 데이터를 기반으로 데이터 정제 및 전처리를 수행하고, LDA 기반 토픽 모델링과 심층 신경망(DNN)을 활용하여 위험성 평가 모델을 구축하였다. 데이터셋 18,000 개를 사용하여 학습 및 테스트를 진행했으며, 최종 검증은 1,026건의 신규 위험성평가 데이터를 활용하였다. 연구결과: 개발된 모델은 작업조건에 따라 유해·위험 요인과 재해 유형 등을 체계적으로 추천하였고, 추천 결 과의 95.6% 이상이 전문가에 의해 적합하다고 평가되었다. 최종검증을 통해 8개 공종의 위험성평가 1,026개 에 대해 추천된 6,380개 전체가 추천 적합도 80 이상으로 모델 설계 기준을 만족하였고, 전 공종에서 일관된 추천 품질과 높은 적합성을 지님을 입증하였다. 결론: 대규모 비정형 안전 데이터를 정형화하여 데이터 기반 의 체계적인 위험성평가 모델을 구축함으로써, 기존의 경험 및 주관적인 평가 방식에 한계를 보완하였다. AI 기술을 활용을 통해 위험 요소 도출의 일관성과 효율성을 제고하고 위험성 평가의 실효성을 높였다.

Purpose: This study aims to develop a deep learning-based task risk assessment model capable of systematically identifying and evaluating hazardous and risk factors at plant construction sites. The model is specifically designed systematically to be easily utilized by workers with limited experience or knowledge, thereby addressing the issues associated with traditional experience-based assessment methods. Method: A total of 976,140 risk assessment records from domestic construction Company A. were cleansed and pre-processed. A risk assessment model was then constructed using LDA-based topic modeling and a deep neural network(DNN). The model was trained and tested using a dataset of 18,000, with final validation conducted on 1,026 new risk assessments. Result: The developed model systematically recommends hazardous and risky factors and types of accidents based on working conditions, with more than 95.6% of the recommendations evaluated as appropriate by experts. In the final validation, the model recommended 6,380 items for 1,026 assessments across eight work types, all achieving suitability score of above 80, thereby meeting the model design criteria and demonstrating consistent recommendation quality and high applicability across all construction domains. Conclusion: By structuring large-scale unstructured safety data into a data-driven risk assessment model, this study overcomes the limitations of traditional subjective evaluation methods. The application of AI enhances consistency and efficiency in hazard identification, improving the effectiveness of risk assessments.

This paper has dual aims. One is to examine the appropriateness of teaching procedures for task-based learning focused on language skills and communicative activities in elementary school English lessons. The other is to discuss some strategies for adapting elementary school English textbooks to do task-based teaching (TBT) for fifth and sixth graders. The results are as follows: (1) Presentation-practice-production (PPP) is useful in a focus on forms lesson, where grammar points are taught particularly to lower-level third and fourth graders. Whereas TBT is useful in a skills lesson for higher-level fifth and sixth graders, where focus on form may occur as a result of something they hear or read. (2) Fifth and sixth graders should be taught based on TBT procedures with an initial focus on meaning which enable them to use the language they have already learned or known as maximally as possible while doing tasks. (3) The current textbook lessons based on PPP for fifth and sixth graders should be adapted to suit TBT procedures by reordering textbook activities, turning the activities into tasks or doing the skills lesson first and the form-focused work last. (4) The sample application of TBT procedures to a textbook lesson for sixth graders will help teachers reconsider the weakness and strength of PPP and TBT called ‘PPP upside down' and adapt textbooks for TBT procedures with focus on meaning first and on form last.

Purpose: This study investigates patterns of small group interaction and examines the influence among graduate nursing students of online collaborative learning strategies on small group interaction patterns, task performance and learning attitude in web-based team learning environments. Method: To analyze patterns of small group interaction, group discussion dialogues were reviewed by two instructors. Groups were divided into two categories depending on the type of feedback given (passive or active). For task performance, evaluation of learning processes and numbers of postings were examined. Learning attitude toward group study and coursework were measured via scales. Results: Explorative interactions were still low among graduate nursing students. Among the students given active feedback, considerable individual variability in interaction frequency was revealed and some students did not show any specific type of interaction pattern. Whether given active or passive feedback, groups exhibited no significant differences in terms of task performance and learning attitude. Also, frequent group interaction was significantly related to greater task performance. Conclusion: Active feedback strategies should be modified to improve task performance and learning attitude among graduate nursing students.

This study aims to see if task-based flipped learning (TBFL) can be effective in developing university students’ self-regulated learning. The specific research question is: Does TBFL have a positive impact on learners’ self-regulated learning attitude in cognitive, motivational, and behavioral domains? The research took place at a university English class where a total of 12 students participated for 15 weeks in the fall semester of the year 2023. In order to answer the research question, the self-regulated learning attitude inventory was used along with focus-group interviews and class observation. Due to the limited number of participants, frequency analysis was made for the quantitative data. Major findings were revealed as follows: Firstly, a high level of self-regulated learning attitude was found in all factors in the cognitive domain except for ‘Planning and Monitoring.’ Secondly, positive responses shown in the entire factors in the motivational domain led to greater interest and self-efficacy. Lastly, in the behavioral domain, positive responses were revealed except for ‘Time Management & Environment Setting,’ which seems to be related to the problems found in the cognitive domain’s ‘Planning and Monitoring.’

목적 : 본 연구의 목적은 오차배제학습과 시간차회상을 이용한 작업기반 훈련이 경도 혈관성 치매환자의 과제학습과 만족도에 미치는 영향을 알아보기 위함이다. 연구방법 : 치매 진단을 받은 경도 인지 수준의 환자 3명을 대상으로 ABAʹ + follow-up 설계를 사용하였다. 중재기간동안 오차배제학습과 시간차회상을 이용한 작업기반훈련을 적용하였으며, 매회기마다 과제학습 정도를 측정하였고, 중재 전과 후에 만족도의 변화를 알아보았다. 결과분석은 그래프를 통한 시각적 분석과 막대그래프를 통해 제시하였다. 결과 : 오차배제학습과 시간차회상을 이용한 작업기반 훈련 후 모든 대상자의 과제수행 능력이 향상되었으며, 중재 후 만족도 또한 향상되었다. 결론 : 본 연구는 초기 혈관성 치매환자에게 오차배제학습과 시간차회상을 이용한 작업기반 훈련이 과제학습과 만족도를 향상시키는데 효과가 있다는 것을 확인하였고, 임상적 적용을 위한 근거를 제공했다는 것에 의의가 있다.

Objective : This study was to verify the effects of occupation-based training with errorless learning and spaced retrieval on task learning and satisfaction of elderly with mild vascular dementia. Methods : The subjects of this study were 3 geriatric individuals with mild vascular dementia, ABA? + follow-up design was used. Intervention period was provided occupation-based training with errorless learning and spaced retrieval. The dependent variable was converted to ability of task performance every session and satisfaction before and after intervention. Result analysis was suggested through visual analysis and bar graph. Results : After the occupation-based training with errorless learning and spaced retrieval, Ability of task performance and satisfaction improved for all subjects. Conclusions : From this study, occupation-based training with errorless learning and spaced retrieval training was found to be an effective mediation for improving independence of task and satisfaction for people with mild dementia. This study could provide evidence for clinical application for occupation-based training.

The purpose of this study was to assess the effects of problem-based learning (PBL) on task value, learning satisfaction, and academic self-efficacy in nursing students and was a primitive experimental study with a one-group pretest-posttest design. The subjects of this study were 100 grade 1 students taking a physiology course, and the data collection period was from September 2 to October 4, 2024. The general characteristics of the subjects were obtained by frequency, percentage, mean, and standard deviation and the effects of PBL on task value, learning satisfaction and academic self-efficacy were tested using a paired t-test. The results of this study showed that nursing students’ academic self-efficacy (t=-5.35, p<.001), task value (t=-2.46, p=.016), and learning satisfaction (t=-4.14, p<.001) improved after applying PBL compared to before applying it. The positive results suggest the use of problem-based learning as a learning strategy for lower-grade student in nursing education. In the future, follow-up research will be required to develop and apply various problem solving scenarios based on the learning content and verify their effectiveness.

本研究旨在探讨应用问题导向学习的课程对护理大学生学业自我效能感, 任务价值及学习满意 度的影响, 采用单组前后对比实验研究. 方法 : 研究对象为护理学专业一年级在读的100名生 理学课学生, 资料收集时间为2024年9月2日至10月4日. 研究对象的一般特征通过频率, 百分 比, 均值和标准差计算, 问题导向学习对学业自我效能感, 任务价值和学习满意度的影响通过 配对t检验分析. 结果􎩁研究结果表明, 接受问题导向学习后, 护理大学生的学业自我效能感 (t=-5.35, p<.001), 任务价值(t=-2.46, p=.016)和学习满意度(t=-4.14, p<.001)均有所提高. 结 论􎩁因此, 建议在护理教育中, 将问题导向学习应用在低年级学生的学习策略上. 今后需要开 发并适用随着专业基础学习内容而出现的不同的问题解决情境, 并验证其效果的后续研究.

다중작업학습(Multi-Task Learning, MTL) 기법은 하나의 신경망을 통해 다양한 작업을 동시에 수행하고 각 작업 간에 상호적으로 영향을 미치면서 학습하는 방식을 말한다. 본 연구에서는 전통문화 말뭉치를 직접 구축 및 학습데 이터로 활용하여 다중작업학습 기법을 적용한 개체명 인식 모델에 대해 성능 비교 분석을 진행한다. 학습 과정에서 각각 의 품사 태깅(Part-of-Speech tagging, POS-tagging) 과 개체명 인식(Named Entity Recognition, NER) 학습 파 라미터에 대해 Bi-LSTM 계층을 통과시킨 후 각각의 Bi-LSTM을 계층을 통해 최종적으로 두 loss의 joint loss를 구한 다. 결과적으로, Bi-LSTM 모델을 활용하여 단일 Bi-LSTM 모델보다 MTL 기법을 적용한 모델에서 1.1%~4.6%의 성능 향상이 있음을 보인다.

Multi-Task Learning(MTL) is a training method that trains a single neural network with multiple tasks influences each other. In this paper, we compare performance of MTL Named entity recognition(NER) model trained with Korean traditional culture corpus and other NER model. In training process, each Bi-LSTM layer of Part of speech tagging(POS-tagging) and NER are propagated from a Bi-LSTM layer to obtain the joint loss. As a result, the MTL based Bi-LSTM model shows 1.1%~4.6% performance improvement compared to single Bi-LSTM models.

본 연구의 목적은 에니어그램 학습경험자들이 중년기의 자신의 발달과업을 달성해 가는 과정에서 에니어그램의 학습경험을 어떻게 활용하는지 탐색하고자 하였다. 이를 통해 심리학적 성격특성 도구로서 중년기 발달과업 달성에 도움이 되는 프로그램으로의 실천적 방향을 모색하고, 중년기에 자신의 문제를 지속적 해결해 나가는데 주체성을 확보하고 유지증진 하는데 필요한 학습경험의 활용에 대한 의미를 제시하는 데 있다. 본 연구는 중년기에 해당하는 40~60세로 H연구소에서 에니어그램 전문가과정을 이수한 학습경험자를 중심으로 심층면담을 통해 진행하였다. 수집된 자료는 Strauss & Corbin(2008)의 근거이론 분석 절차에 따라 개방코딩-축코딩-선택코딩 순서에 따라 진행하고 분석 내용을 제시하였다. 본 논문의 연구 결과는 다음과 같다.개방코딩을 실시한 결과 335개의 개념이 도출되었으며, 유사 개념들을 묶어서 54개로 하위 범주화하였고, 공통적인 의미를 드러내거나 방향성이 같은 내용을 상위범주로 하여 25개로 도출하였다. 25개의 상위범주를 패러다임 모형을 통해 재배열하는 과정에서 드러난 중심현상은 ‘자기이해를 도와준 에니어그램’이었다. 이러한 중심현상의 인과적 조건들은 ‘자아정체성 혼란’, ‘새로운 도전 의욕’, ‘가정밖에서도 가치 있고 싶은 나’, ‘에니어그램 학습 동기’로 구성되었다. 이러한 중심현상의 맥락적 조건들은 ‘흔들리는 경제 입지’, ‘한계에 부딪힌 직장생활’로 구성되었다. 또한 중재적 조건들로는 ‘체감되는 노화’, ‘가치관의 변화’, ‘어려워진 가족관계’, ‘부모부양의 어려움’, ‘에니어그램 학습 공유하기’, ‘에니어그램 학습 지속하기’, ‘같이할 때 성장하는 학습’으로 드러났다. 이들에게서 나타난 작용/상호작용 전략들은 ‘객관적으로 시각 갖고자 노력하기’, ‘일상생활에서 에니어그램 활용하기’, ‘진정성 있게 반성하기’, ‘있는 그대로 타인 수용하기’, ‘긍정적으로 소통하기’, ‘에니어그램을 활용하여 문제 해결하기’가 도출되었다. 이러한 과정을 거쳐 나타난 결과들은 ‘긍정적으로 변한 나’, ‘나의 변화에서 시작한 관계의 변화’, ‘에니어그램 활용으로 향상된 직업역량’, ‘학습 즐기기’의 상위범주가 상정되었다. 이를 통해 본 연구의 핵심범주는 “자기이해를 주축으로 중년기 발달과업을 달성하는데 지속적 성장과 변화에 활용되는 에니어그램”으로 나타났다. 이와 같은 핵심범주를 중심으로 에니어그램 학습경험자의 중년기 발달과업 달성과정에 대해 분석한 결과 ‘자아성장형’, ‘관계회복형’, ‘직업활용형’, ‘지속학습형’의 네 가지 유형을 도출하였다.이상에서 얻은 결과를 통해 중년기 발달과업 달성과정에서 에니어그램 활용을 통하여 지속적 문제해결에 주체성을 확보하고 유지 증진하는데 에니어그램 학습경험의 의미를 제시하였다.

This study aims to explore people’s use of their Enneagram learning experience in the process of achieving their development tasks in their middle age. The study intends to find practical directions by using a program that helps achieve middle-age development tasks as a tool for psychological personality characteristics. It also attempts to put forth the meaning of the use of learning experiences necessary to secure and maintain self-reliance in actively and continuously solving one’s problems in middle age. The participants of this study comprised people aged between 40 and 60 years who completed the Enneagram expert course offered at the H Research Institute. The data collection period was from August to November 2021. Data were collected through in-depth interviews. For the participants, eight categories were derived as developmental tasks to be solved or achieved in middle age, and it was found that the Enneagram learning experience had a significant impact on this. The data collected through in-depth interviews were analyzed in accordance with Strauss and Corbin’s (2008) procedures for analyzing evidence theory. The analysis procedure was conducted in the order of open axial and selective coding, and the analyzed contents were presented. The results of this study are given as follows.As a result of open coding, 335 concepts were derived; similar concepts were grouped and sub-categorized into 54, and 25 concepts were derived “with common meanings or contents with the same direction as the upper category”. The central phenomenon revealed in the process of rearranging the 25 top categories through the paradigm model was “the Enneagram that helped self-understanding”. The causal conditions of this central phenomenon comprised “self-identity confusion”, “motivation for new challenges”, “myself who wants to be valuable outside home” and “the Enneagram learning motivation”. The contextual conditions of this central phenomenon comprised “shaken and unstable financial position” and “work life facing limitations”. Additionally, interventional conditions were “feeling aging”, “value changes”, “difficult family relationships”, “difficulties in supporting parents”, “sharing the Enneagram learning”, “continuing the Enneagram learning” and “learning and growing when together”. Their action/interaction strategies were derived from “effort to view things objectively”. “using the Enneagram in daily life”, “reflecting sincerely”, “accepting others as they are”, “communicating positively” and “solving problems using the Enneagram”. The results that appeared through this process were assumed to be the top categories of “positively changed me”, “changes in relationships that started with changes in me”. “improved job competency through the use of the Enneagram” and “enjoying learning”.Through this, the core category of this study was found to be “the Enneagram used for continuous growth and change in achieving middle-age development tasks centered on self-understanding” Focusing on these core categories, the following four types were derived as a result of the analysis on the process of those who experienced the Enneagram to achieve their middle-age development tasks: “self-growth type”, “relation recovery type”, “work utilization type” and “continuous learning type”.Based on the above results, this study proposed the significance of the Enneagram learning experience. It contributed to securing and maintaining self-reliance in continuous problem-solving through the Enneagram program for completing middle-age development tasks.

차세대 네트워크에서는 제한된 자원을 가진 수많은 장치가 실시간 응용 서비스를 위해 지연에 민감 하고 복잡한 작업을 수행해야 함에 따라 작업 오프로딩과 자원 할당이 점차 중요해지고 있다. 이는 복 잡한 결정 문제로 사전 지식 없이 최적 결정을 선택할 수 있는 강화 학습을 통해 해결할 수 있다. 본 연구는 이를 위한 연합 심층 강화 학습의 연구 동향을 정리하고, 자원 제약과 학습 구조를 중심으로 분석한 후 향후 연구 방향을 제시하고자 한다.

This study is describes the development of an adaptive learning system that supports the adaptability of learning content and takes into consideration the learner’s learning style for more effective learning. The purpose of this adaptive learning system is to facilitate university students’ computer science learning. There are many studies on adaptive learning systems, but most of them provide adaptive courseware that considers only the learning style. Therefore, with an understanding that the task affects learning more, the purpose of this study attempts to provide an adaptive instruction model and adaptability according to a learning style concurrently. Applying C. M. Reigeluth’s categorization method, the learning content is classified into conceptual knowledge, principal knowledge, and procedural knowledge; serving the concept-attainment model, the inquiry-based model, and the cognitive-apprenticeship learning model respectively. Applying the A. F. Gregorc learning style, learners’ learning styles are classified into concrete-sequential, concrete-random, abstract-sequential, and abstract-random; this adaptively services the respective content. Reliability was secured by conducting a blind test targeting an expert group with an attempt to prove the validity of the instruction model and the learning style provided by this adaptive learning system.

The difference of this paper is that it analyzes the latest machine learning and deep learning tools for various tasks of program such as program search, understanding, completion, and review. In addition, the purpose of this study is to increase the understanding of various tasks of program by examining specific cases of applying various tasks of program based on tools. Recently, machine learning (ML) and deep learning (DL) technologies have contributed to automation and improvement of efficiency in various software development tasks such as program search, understanding, completion, and review. This study examines the characteristics of the latest ML and DL tools implemented for various tasks of program. Although these tools have many strengths, they still have weaknesses in generalization in various programming languages and program structures, and efficiency of computational resources. In this study, we evaluated the characteristics of these tools in a real environment.