Earticle

본 연구는 대학에서 이루어지고 있는 강의에 있어 전통적인 오프라인 강의실 강의에 정보기술 발달과 함께 급속도로 확산되고 있는 SNS(Social Network Service)를 접목할 경우 학습성과의 변화에 어떠한 영향을 미치는지를 살펴보기 위해 시작되었다. 이를 위하여 101명을 대상으로 설문조사를 실시하였고 분석결과는 다음과 같다. 첫째, 소셜 네트워킹 참여(online social networking engagement)와 사회적 수용(acculturation)은 교수와의 상호작용 품질(interaction quality with professors)에 영향을 미치는 것으로 나타났다. 또한 교수와의 상호작용품질은 협력학습(collaborative learning)과 학습성과(learning performance)에 유의한 영향을 미치는 것으로 나타났다.

The purpose of this study is to investigate the effect of SNS(Social Network Service) on individual learning performance. To do this, we distribute and collect data by using a survey method. Research results suggest that online social networking engagement and acculturation have an effect on interaction quality with professors. Interaction quality with professors influences individual learning performance as well as collaborative learning. The conclusion and implications are discussed.

본 연구는 중국 물류산업에 진출한 다국적 물류기업을 대상으로 현지 제휴 기업의 안정성, 네트워크가 진출 기업의 학습 성과와 시장 성과에 미치는 영향을 살펴볼 목적으로 진행하였다. 또한, 학습 성과의 매개효과를 분석하여 해당 요인이 현지 제휴기업의 특성인 안정성과 네트워크를 시장 성과에 어떻게 연결하고 있는지를 분석하였다. 전체 연구 모형에는 현지 물류기업의 특성으로 안정성과 네트워크를 고려했으며, 진출 다국적 물류기업의 성과를 학습 성과와 시장 성과로 나누어 살펴보았다. 분석 대상 기업은 우리나라 기업을 포함해 중국 시장에 진출한 179개의 다국적 물류기업이며, LISREL 구조방정식 모형을 이용해 매개효과를 포함한 7개 가설을 실증분석 하였다. 분석 결과 현지 제휴 파트너의 안정성과 네트워크는 모두 학습 성과에 긍정적 영향을 미치고 있었다. 그러나 시장 성과에서는 현지 제휴 기업의 안정성은 긍정적 영향을 준 반면 네트워크는 영향이 없는 것으로 나타났다. 매개효과 분석에서는 학습 성 과가 현지 제휴 파트너의 안정성과 시장 성과 사이에는 부분매개 효과를 보였으며, 네트워크와 시장 성과 사이에서는 완전매개 효과를 나타냈다. 분석 결과 현지 제휴 파트너의 특성을 시장 성과에 연결하는데 나타난 학습 성과의 중요성을 확인하였다. 더불어 분석 결과를 바탕으로 실무적, 이론적 시사점을 설명하였으며, 연구 한계와 앞으로 연구 방향을 제시하였다.

This study was conducted to explore the impact of the stability and networks of the local partners on the learning performance and market performance of logistics MNEs entered the Chinese logistics industry. In addition, this study analyzed how the mediated effect of learning performance between the stability and networks of the local partners and the market performance. In the research model of this study considered the stability and networks as the characteristics of the local alliance partner and investigated the performance of logistics MNEs by dividing the learning performance and market performance. The sample firms be analyzed were 179 logistics MNEs including the Korean logistics MNEs. The 7 hypothesis including moderated effect were analyzed by using LISREL as structural equation modeling. According to the path analysis, the stability and networks of the local alliance partner all had a positive impact on the learning performance. However, the stability of local alliance partnership had a positive influence on the market performance, on the other hand, there was no significant effect in the network. In the analysis of the mediated effect, the learning performance showed a partial mediated effect between the stability of the local alliance partner and the market performance and showed a full mediated effect between the networks of these MNEs and the market performance. In the result of the analysis, the importance of learning performance was confirmed for connecting the characteristics of the local alliance partners to the market performance. Based on the results of analysis, the practical and theoretical implications were described, and the research limitations and the future research directions were suggested.

본 연구는 대학에서 이루어지고 있는 강의에 있어 전통적인 오프라인 강의실 강의에 정보기술 발달과 함께 급속도로 확산되고 있는 SNS(Social Network Service)를 접목할 경우 학습성과의 변화에 어떠한 영향을 미치는지를 살펴보기 위해 시작되었다. 이를 위하여 101명을 대상으로 설문조사를 실시하였고 분석결과는 다음과 같다. 첫째, 소셜 네트워킹 참여(online social networking engagement)와 사회적 수용(acculturation)은 교수와의 상호작용 품질(interaction quality with professors)에 영향을 미치는 것으로 나타났다. 또한 교수와의 상호작용품질은 협력학습(collaborative learning)과 학습성과(learning performance)에 유의한 영향을 미치는 것으로 나타났다.

The benefits of an e-learning system will not be maximized unless learners use the system. This study proposed and tested models that seek to explain students’ satisfaction withe-learning systems. A survey was performed at a women’s college in Korea, where students experimentally could choose to register one same course either through e-learning or class-room learning. The questionnaire was filled up by students who took e-learning option. Independent variables include expected benefits, familiarity with technology, social influence, and accessibility. Dependent variables include the level of satisfaction, academic achievement, and the amount of the use of systems.

This study investigates energy efficiency issues of device-to-device (D2D) communications in heterogeneous networks. To minimize the total transmitted power, an approach based on Q-learning together with adaptive ? -greedy is proposed to optimize the connection of user equipment (UE) with base station (BS) or access point (AP). The proposed adaptive ? -greedy can conduct the adequate exploration and exploitation operations for effective optimization. Simulation results indicate that in the single-cell scenario, the proposed method can attain performance close to the best solution.

Background Frailty is an important condition to detect in its early stages to prevent progression to more severe stages in older adults. Age-related declines in physical performance are strongly associated with frailty. Purpose This study aims to develop a frailty classification model by comparing the performance of machine learning models based on physical performance measures in community-dwelling older adults. Study design A cross-sectional study Methods Physical performance data were collected from older adults aged ≥65 years. Frailty classification models were developed using logistic regression, support vector machine (SVM), K-nearest neighbors (KNN), decision tree, and random forest. Clinical features including short physical performance battery, single-leg stance, SARC-F, body mass index, and mini-mental state examination (MMSE) were used as input variables for model development. The performance of each model was evaluated using accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Permutation feature importance was employed to identify key predictors of frailty. Results The KNN model demonstrated the highest classification performance, achieving an accuracy of 0.93, an F1-score of 0.95, and an AUC of 0.86, indicating its suitability for frailty assessment. The logistic regression model achieved an accuracy of 0.86, an F1-score of 0.89, and an AUC of 0.98. The random forest model showed similar results, with an accuracy of 0.86, an F1-score of 0.88, and an AUC of 0.96. The SVM model recorded an accuracy of 0.79, an F1-score of 0.84, and an AUC of 0.80. The decision tree model showed the lowest performance, with an accuracy of 0.71, an F1-score of 0.78, and an AUC of 0.64. Feature importance analysis revealed that MMSE and SARC-F were the most influential predictors in the KNN model. Conclusions This study demonstrates that KNN is well-suited for identifying subtle variations in physical function that contribute to frailty. The results highlight its potential for clinical implementation in automated frailty screening. Feature importance analysis provides insight into key predictors, supporting personalized assessment strategies. However, due to the small sample size, further research is needed to assess the generalizability of frailty classification models in larger populations.

Background Logistics service workers face elevated injury rates, with ankle-related incidents comprising 23% of workplace injuries. Traditional binary foot posture classifications may oversimplify the complex biomechanical relationships affecting dynamic balance performance in occupational settings. Purpose To apply unsupervised machine learning clustering to identify distinct foot posture phenotypes among logistics service workers and compare dynamic balance performance between identified clusters using the Y-Balance Test. Study design Cross-sectional observational study Methods A total of 112 logistics service workers were analyzed using K-means clustering based on age, body mass index, work duration, navicular drop, and resting calcaneal stance position. Silhouette score analysis determined optimal cluster number. Dynamic balance was assessed using the Y-Balance Test, measuring reach distances in anterior, posteromedial, and posterolateral directions. Results Four distinct phenotypes emerged: rearfoot valgus-dominant pronated (n=22), midfoot collapse-dominant pronated (n=32), age-related (n=23), and supinated (n=35) foot types. Significant differences in dynamic balance performance were observed in the posterolateral direction (F=3.900, p=0.011). The supinated phenotype demonstrated superior posterolateral reach performance (77.76±13.63%) compared to midfoot collapse-dominant (66.18±16.37%, p=0.003) and age-related phenotypes (67.78±14.97%, p=0.018). Conclusions Unsupervised machine learning successfully identified naturally occurring foot posture phenotypes with distinct dynamic balance characteristics. Midfoot collapse-dominant pronation demonstrated greater balance impairments than rearfoot valgus patterns, supporting the implementation of phenotype-specific interventions for workplace injury prevention in logistics workers.

This paper investigates the Contention Window (CW) optimization problem in multi-agent scenarios, where the fully cooperative among mobile stations is considered. A partially observable environment is employed to model and analyze the CW optimization problem, and Smart Exponential-Threshold-Linear with Deep Q-learning Network (SETL-DQN) Multi-Agent (MA) algorithm is proposed to obtain the optimal system throughput through the CW Threshold optimization. In the determined scenarios, SETL-DQN(MA) can effectively cope with the mutual interaction among mobile stations. The simulation results show that our proposed method is superior from both static and dynamic scenarios and has the highest optimum packet transmission efficiency.

Many organizations have had deep interests in studies concerning leadership and in academic areas, in not only management but also psychology. Until now, leadership has been accentuated by managers or team leaders especially. Recently, however, the concept of self-leadership directing one"s own activities through self-control or self-management is being focused on practices and in academia. This study is to investigate the influence between self-leadership strategies and learning performance in IT classes mediated by attitude of attendance focused on the social science students in a university. Research results can give us direction of task-taking attitudes in firms or learning attitudes in teaching organizations and implications to human resource managers who are in charge of improving learning performance or productivity.

In the recent e-learning environment, avatars are often used to help learners get familiar with the contents, which is ultimately to motivate them to study more. Therefore, it is important to investigate whether avatars have actually the desirable effect on users of e-learning materials. Surprisingly, however, no extensive study has been conducted on this crucial issue Accordingly, main objectives this study are summarized as follows. First, we need to gain better understanding of how much learners’ trust towards avatars (termed as “avatar trust”) is transferred to learners’ trust towards e-learning contents (termed as “contents trust”). Second, we need to investigate how much learners’ personal relationships with avatars as well as learning behaviors change depending on avatar types (attractive vs. professional) and contents complexity (easy vs. difficult). As described in the study objectives, in order for us to analyze empirical data more systematically, we classified avatar types into two: “attractive” and “professional;” the contents are categorized as either “easy” or “difficult.” Therefore, it is essential for this study to build a prototype e-learning website on which our research purpose can be realized and tested effectively with proper avatar types and e-learning contents. For this purpose, we built a prototype e-learning website, in which avatars are invited from currently working avatar instructors used in real-world e-learning websites, and e-learning contents are adapted from real-world contents about Java programming topic, which have been proved to have shown high quality and reliability. Our research method includes questionnaire survey by inviting a number of valid respondents comprised of office workers who are believed to have high demands for the e-learning contents as well as those who have previous experience with avatar instructors. Respondents were given one of the four e-learning experiment conditions (2 avatar types x 2 contents types) on a random basis. Each experimental e-learning condition is framed to have the same quality but different avatar type and content complexity. Then the respondents are asked to fill out the survey form which has questions about avatar trust, contents trust, personal relationships with avatar, and learning behavior, among others. Regarding the constructs used in research model, we based them rigorously on previous studies. For example, we used six constructs such as behavior to give information (BGI), behavior to obtain information (BOI), need for inclusion wanted, need for control wanted, contents trust, and avatar trust. To measure them, 7-Likert scales were used in the questionnaire. E-learning performance was measured indirectly through two constructs such as BGI and BOI. Six constructs used in the research model were adopted and revised from the FIRO-B model suggested by Schutz. Empirical results are as follows: First, professional avatars are more effective for difficult contents, while attractive avatars were not as effective for easy contents. Second, our study results ascertained that avatar trust transfers to contents trust regardless of avatar types and contents complexity.

According to a lot of people, home literacy practices are closely related to language learners’ school performance. Therefore, for more successful language learning the relationships between both of them are required to investigate. In this study, particularly the relationships between home literacy practices and school performance in bilingual and multilingual contexts have demonstrated. A variety of literacy practices involving family literacy practices, community cultural literacy, and social background were discussed. A number of studies showed that family literacy practices in first and second languages influence language learners’ literacy learning and school performance positively or negatively. Thus, the role of families and parents at home for language-minority learners’ literacy development and academic success in school might be very important.

This study explores the effect of web-based collaborative writing activities through interactive peer-to-peer corrective feedback conducted by students. 40 university students participated in a writing module and were encouraged to participate in both individual and group writing activities based on online interactions with other peers. 40 writing samples from individual writing tasks and 40 writing samples from the web-based writing were collected and scored by both a native teacher and a Korean teacher. Cohen’s analytic scoring rubric (1994) was used for the analysis of learners’ corrective behaviors. This study illustrates three main research questions on student writing behaviors through web-based collaborative Business writing activities. This study shows that experiencing online collaborative Business writing activities may have positive effects on both students’ writing and their writing strategies.

인터넷 서비스의 확산과 함께 정교해지는 피싱(Phishing) 공격은 개인 정보 탈취 및 금융 피해를 유발하는 심각한 보안 위 협으로 대두되고 있다. 기존의 피싱 탐지 체계는 주로 구글 세이프 브라우징(Google Safe Browsing)이나 피쉬탱크 (PhishTank)와 같은 블랙리스트(Blacklist) 방식에 의존해 왔다. 이 방식은 알려진 위협에 대해서는 신속하고 정확한 차단이 가능하나, 제로데이(Zero-day) 공격을 탐지하지 못하는 치명적인 한계를 가진다. 본 연구에서는 이러한 한계를 극복하기 위해 URL의 어휘적 특징을 기반으로 하는 다양한 인공지능 모델의 탐지 성능을 비교 분석하였다. 실험 대상 모델로는 전통적인 휴 리스틱 알고리즘과 머신러닝 모델인 로지스틱 회귀(Logistic Regression), 서포트 벡터 머신(SVM), 랜덤 포레스트(Random Forest), 그리고 딥러닝 모델인 CNN(1D)과 LSTM을 선정하였다. 실험 결과, 휴리스틱 방식은 44.5%의 저조한 정확도를 보인 반면, SVM(RBF 커널) 모델은 97.0%의 정확도와 0.970의 F1-Score를 기록하며 가장 우수한 성능을 나타냈다. 특히 딥러닝 모 델인 CNN(94.5%)과 LSTM(76.1%) 대비 SVM은 0.165초라는 빠른 추론 속도를 보여 실시간 탐지 환경에서 성능과 효율성의 최적 균형을 갖춘 모델임을 입증하였다.

As internet services proliferate, phishing attacks are becoming increasingly sophisticated and are emerging as a serious security threat that causes the theft of personal information and financial damage. Existing phishing detection systems have primarily relied on blacklist methods such as Google Safe Browsing or PhishTank. While this approach enables the rapid and accurate blocking of known threats, it has a critical limitation in its inability to detect zero-day attacks. To overcome these limitations, this study comparatively analyzed the detection performance of various artificial intelligence models based on the lexical features of URLs. The models selected for the experiment included traditional heuristic algorithms, machine learning models such as Logistic Regression, Support Vector Machine(SVM), and Random Forest, as well as deep learning models like CNN(1D) and LSTM. The experimental results showed that while the heuristic method yielded a poor accuracy of 44.5%, the SVM(RBF kernel) model demonstrated the superior performance, recording an accuracy of 97.0% and an F1-Score of 0.970. In particular, compared to the deep learning models CNN(94.5%) and LSTM(76.4%), SVM demonstrated a fast inference speed of 0.165 seconds, proving it to be the model with the optimal balance between performance and efficiency in a real-time detection environment.

The purpose of this study is to investigate the effects of mobile-learning (m-learning) on EFL student satisfaction and performance. For this empirical study, a research model was proposed with hypotheses. For m-learning attributes, four variables were suggested: Mobility, Content Personalization, Interactivity, and Accessibility. In addition, Learning Supports mechanisms used as potential moderating factor was proposed in order to test whether this variable enhances the relationship between Satisfaction and Performance in English m-learning. the data were collected from actual m-learning EFL students. A total of 279 responses were analyzed to test the proposed hypotheses. Results show that all hypotheses were significantly supported, suggesting that m-learning attributes are crucial factors for increasing EFL learners’ satisfaction and performance. Furthermore, the relationship between Satisfaction and Performance is enhanced by the inclusion of various types of Learning Supports. Finally, academic and practical implications are discussed.

This study empirically verified the factors affecting the learning outcomes of English as a Foreign Language (EFL) learners in a smart learning (s-learning) environment. Based on social cognitive theory (SCT), six independent constructs (facilitating conditions, social influence, positive attitude toward s-learning, self-efficacy, social interaction, and knowledge) were proposed in the research model. The data collected from 455 s-learning EFL learners were analyzed in order to test the causal relationships between these six constructs and s-learning performance. For the analysis, the structural equation model (SEM) with AMOS 22.0 was used, and all six constructs were found to have a significant effect on s-learning performance. Important implications are discussed based on the study results.

This paper discusses the performance of linear regression, regression tree, support vector regression, and ensemble learners in modelling airflow between two spaces based on accuracy and training time. To obtain training data, different scenarios from an existing computational fluid dynamics (CFD) model are simulated via transient analysis using Cradle scSTREAM. The raw dataset is transformed to having time step sizes of 2.5s, 5.0s, and 50.0s. Feature scaling is also employed on the each data set using both min-max scaling and z-score methods for a total of 9 datasets. Hyperparameters according to machine learning (ML) algorithms are varied such that 15 ML models across the four algorithms are developed. The results show that the regression trees perform the best over all other algorithms, with all models maintaining R2 values above 0.95 at the different datasets. On the other hand, as expected, all linear models demonstrated poor performance compared to nonlinear models. Data resolution affects model accuracy and training time, with accuracy declining slightly as time step size increased. It is also found that there is no significant effect of feature scaling. Lastly, ML models yield substantially cheaper simulation costs than CFD to simulate airflow.

실패 학습 행동은 창업 벤처의 성과를 향상시키는 중요한 방법이며, 본 연구는 실패 학습 행동이 창업 성과에 미치는 영향의 근본적인 메커니 즘을 탐구하는 데 전념합니다. 관련 이론을 참고하여 본 연구에서는 비즈니스 모델 혁신을 매개변수로, 조직 관성을 조절변수로 선택했습니다. 본 연구는 설문지를 통해 307개 스타트업 표본으로부터 데이터를 수집하고 층화 회귀분석을 통해 가설검증을 실시했습니다. 그 결과 실패 학습 행동은 재무적 및 비재무적 기업 성과에 유의미한 정(+)의 영향을 미치며, 비즈니스 모델 혁신은 이 효과를 부분적으로 매개하는 역할을 하는 것 으로 나타났습니다. 조직의 관성은 비즈니스 모델 혁신이 재무적 기업가적 성과에 미치는 영향을 약화시킵니다. 본 연구는 실패 학습 행동과 기 업가적 성과에 대한 이론적 결과를 확장하여 기업 경영 실무에 시사점을 제공합니다.

Failure learning behavior is an important way to promote the performance of entrepreneurial firms, and this study is dedicated to exploring the underlying mechanisms of the impact of failure learning behavior on entrepreneurial performance. Referring to related theories, this study selects business model innovation as a mediating variable and organizational inertia as a moderating variable. The study adopts a questionnaire method to collect data from a sample of 307 start-ups and conducts hypothesis testing through stratified regression. The results show that failure learning behavior has a significant positive effect on financial and non-financial entrepreneurial performance, and business model innovation assumes a partial mediating role in the effect. Organizational inertia negatively moderates the effect of business model innovation on financial entrepreneurial performance. This study extends the theoretical results on failure learning behavior and entrepreneurial performance and has implications for business management practice.

Background/Objective. The COVID-19 has accelerated the conduct of online classes around the world. To administer distant learning, schools are now relying on learning management systems. With the problems of the abrupt shift to an online modality, there must be measures in place to cluster students’ characteristics in order to assist their learning requirements, struggles, and academic performances. Methods/Statistical Analysis. Based on Delone and McLean’s IS Success model, this study used quantitative approaches and exploratory techniques to explain the higher education learners’ clusters. From March to April 2021, 303 samples were randomly selected from students participating in online programs at a higher education school on Taft Avenue in Manila. Sex, school, frequency of use, time duration, and experience with the LMS were all factors in the study. Both Hierarchical Cluster Analysis and K-Means Cluster Analysis were used to classify the samples. Findings. The results revealed that there are four clusters formed which are labeled as: service-oriented, system quality-oriented, holistic-oriented, and LMS-averse. Based on the results, information quality has the greatest influence. Since the clustering analysis is non-inferential and it is used as an exploratory technique, the researchers do not guarantee a unique solution as this depends on the elements of the subjects and the variables used. Improvements/Applications. The academic stakeholders can use the cluster analysis to make the appropriate interventions to improve the online course content and enhance the students’ academic performance. Some of these appropriate interventions include creating course policies, planning the necessary training of teachers and students, and developing the online delivery of the lessons.