• 한국도서관정보학회지
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• 제53권3호
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• pp.263-285
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• 2022

본 연구에서는 중학생을 대상으로 중학생의 온라인 학습 행동에 영향을 미치는 새로운 요인을 구성하기 위한 요인분석을 제시하였다. 총 204명의 한국 중학생이 참여했으며, 중학교 3년 학생의 표본을 목적표본으로 선정하여 사용하였다. 요인 분석 결과는 공유 분산의 66.15%를 차지하는 35개 항목에 대한 8개 요인 솔루션을 제시했다. 중학생들의 온라인 학습 행동을 식별하기 위해 다양한 요인이 고려된다. 이때, 중학교 시기 온라인 러닝의 적절한 경험과 활용도는 그들의 미래 교육의 중요한 발판이 되기 때문에 중요하다. 본 연구의 결과는 중학생을 위한 온라인 러닝 시스템의 질을 향상시키고 온라인 학습을 발전시키기 위한 정보를 제공할 것으로 기대한다. 연구 결과는 중학생의 온라인 학습 행동에 영향을 미치는 8가지 중요한 요인을 제시했고, 그것들은 1) 소셜 미디어를 학습 도구로 활용한 커뮤니케이션, 2) ICT를 활용한 정보 공유 의지, 3) 테크놀러지 중독, 4) 테크놀러지 도입, 5) ICT를 활용한 정보 탐색, 6) 소셜 미디어 학습 활용, 7) ICT를 이용한 정보 검색, 그리고 8) 테크놀러지 몰입이다. 본 연구의 결과는 중학생들이 학습도구로 소셜미디어를 활용한 커뮤니케이션을 선호하며, ICT를 활용한 정보 공유 의도를 대부분 중시하고 있음을 확인하였다. 요인 분석을 기반으로 얻은 데이터는 온라인 러닝의 새로운 교육 플랫폼을 적용하기 위해, 소셜 미디어 학습과 ICT의 혼합에 대한 온라인 학습 행동에 중요하게 적용할 수 있을 것이다. 이 연구는 중학생들의 온라인 학습 행동을 더 잘 이해하고 온라인 학습 환경을 설계하는 정보 전문가가 특히 디지털 리터러시가 필요한 중학생에게 더 잘 지원할 수 있도록 유용하게 사용할 것으로 기대한다.

• Educational Technology International
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• 제11권2호
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• pp.43-75
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• 2010

The purpose of this study is to identify patterns of learner behaviors, conflicting and facilitating factors during collaborative work in an online learning community(OLC). This study further seeks to investigate the difference of learner behaviors between high- and low-performing groups, and conflicting and facilitating factors. The online postings from four groups(19 students) in the spring semester(study 1) and six groups(24 students) in the fall semester(study 2) were analyzed. A coding scheme was generated based on constant comparison using the qualitative data analysis tool, NVivo. The analysis identified 7 categories of learner behaviors in both studies. Among the seven categories, information seeking and co-construction were most frequently observed in both studies. One evident difference between the high- and low-performing groups was that the high-performing groups revealed more incidents of learner behaviors in both studies. In addition, six categories of conflicting factors and five categories of facilitating factors were emerged in both studies. The inefficiency of work category was one of the most frequently observed categories in both studies. Interestingly, the high-performing groups showed more incidents of conflicting factors than the low-performing groups. This study revealed two different types of conflicting factors and there is a need for different moderating strategies depending on its type. Based on the results of the study, effective design strategies for an OLC to facilitate active learning were suggested.

• 컴퓨터교육학회논문지
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• 제23권1호
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• pp.77-90
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• 2020

본 연구는 온라인 문제기반학습에서 학습자의 학습행태에 따른 학습유형을 파악하고 각 학습유형의 특징을 조사하여 효과적인 온라인 문제기반학습 설계를 위한 시사점을 도출하기 위해 수행되었다. 본 연구를 위해 6주 동안 K대학에서 운영된 문제기반학습 프로그램에 참여한 1,341명의 초·중학생의 온라인 활동 데이터가 수집되었고, 이를 통하여 학습자들의 학습행태를 나타내는 48개의 변인이 추출되었다. 추출된 변인은 학습자들의 학습유형을 구분하기 위한 계층적 군집분석 기법에 활용되었으며, 구분된 학습유형에 따라 학습행태와 학업성취도 측면에서 어떠한 차이가 있는지 비교·분석하였다. 그 결과, 학습자의 온라인 학습유형은 학습참여 수준에 따라 '고수준 학습참여형(군집 1)', '중수준 학습참여형(군집 2)', '저수준 학습참여형(군집 3)'으로 구분되었다. 또한, 학습참여 수준이 높은 군집이 높은 학업성취도를 얻은 것으로 확인되었다. 이러한 결과를 바탕으로 온라인 문제기반학습을 효과적으로 설계·운영하기 위한 시사점을 제시하였다.

• Educational Technology International
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• 제18권2호
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• pp.193-213
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• 2017

This study aimed to explore online learning profiles of in-service teachers in South Korea, focusing on video lecture and discussion activities. A total of 269 teachers took an online professional development course for 14 days, using an online learning platform from which web log data were collected. The data showed the frequency of participation and the initial participation time, which was closely related to procrastinating behaviors. A cluster analysis revealed three online learning profiles of in-service teachers: procrastinating (n=42), passive interaction (n=136), and active learning (n=91) clusters. The active learning cluster showed high-level participation in both video lecture and discussion activities from the beginning of the online course, whereas the procrastinating cluster was seldom engaged in learning activities for the first half of the learning period. The passive interaction cluster was actively engaged in watching video lectures from the beginning of the online course but passively participated in discussion activities. As a result, the active learning cluster outperformed the passive interaction cluster in learning achievements. The findings were discussed in regard to how to improve online learning environments through considering online learning profiles of in-service teachers.

• Asia pacific journal of information systems
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• 제26권1호
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.

• Educational Technology International
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• 제15권2호
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• 한국융합학회논문지
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• 제11권9호
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• pp.433-441
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• 2020

본 연구는 온라인 수업에 참여한 간호대학생의 딴짓에 영향을 미치는 요인을 파악하기 위해 수행하였다. 자료수집은 3개 대학에 재학 중인 간호대학생 304명을 대상으로 2020년 4월 20일부터 4월 30일까지 구조화된 자가 보고식 설문지 작성을 통해 수집하였다. 자료 분석은 SPSS 26.0 program을 이용하여 T-test, ANOVA, Pearson's correlation coefficient, multiple regression을 이용하였다. 연구결과, 딴짓은 흥미도(r=-17, p=.003), 이해도(r=-19, p=.001), 필요도(r=-12, p=.031), 학습동기(r=-12, p=.046), 자기조절효능감(r=-11, p=.040), 학습자신감(r=-14, p=.017), 강의만족도(r=-22, p<.001), 강의몰입(r=-24, p<.001)과 유의한 상관관계를 보였다. 또한 학습자신감(β= -.20), 토론 및 발표선호정도(β= .19), 강의몰입(β= -.15), 강의만족도(β= -.15)이 딴짓에 유의한 영향을 미치는 것으로 확인되었으며(F=9.95, p<.001), 모형의 설명력은 10.6%였다. 본 연구를 통해 온라인 수업에 있어 간호대학생의 딴짓을 감소시키기 위한 교수학습방법 및 프로그램의 개발을 제언한다.

• Educational Technology International
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• 제16권2호
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• pp.201-229
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• 2015

Culture is a crucial concept that forms the thinking and behaviors of a group of people, and it influences interactions in learning. Thus, it is also essential to consider cultural sensitivity in online learning technologies and instructional design as education is a set of learning actions based on values and perceptions. MOOCs, the latest online learning platform, are global online learning platforms that provide global learners with free and various learning resources including courses from different world-class institutions. Despite globalization having brought learners closer to sharing similar learning resources, the actual experiences with the resource are expected to vary according to cultures, mainly because learning behavior is a set of outcomes based on cultural differences. Taking this into consideration, this study aims to examine MOOCs from a cultural perspective in order to facilitate global learners, especially Korean learners, to utilize MOOCs with user-friendly services and contents. To achieve this objective, the study first identified and developed an evaluation criteria to examine the cultural sensitivity of MOOCs and conducted case studies on courses from major MOOC providers including edX and Coursera. From the findings, design recommendations of contents and courses on MOOCs were suggested to provide Korean learners with optimal learning experiences.

• 의학교육논단
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• 제23권1호
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• pp.46-55
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• 2021

Since the emergence of coronavirus disease 2019 (COVID-19), medical schools have experienced a sudden, full-scale transition to online classes. As the COVID-19 pandemic continues, it is important to evaluate current educational programs and to assess their implications. This study explored perceptions of online classes and learning behavior among medical students. Twenty preclinical medical students were interviewed in focus groups for 2 months. They generally expressed positive perceptions about online classes, and in particular, positively assessed the ability to lead their individual lifestyles and study in comfortable environments with fewer time and space constraints. Students thought that the online environment provided a fair chance of facilitating positive interactions with the professor and considered communication with the professor to be an important factor only when it was related to the class content or directly helped with their grades and careers. Students also had negative views, such as feeling uncertain when they could not see their peers' learning progress and assess themselves in comparison and feeling social isolation. Learning behaviors have also changed, as students explored their learning styles and adapted to the changed learning environment. Students expanded their learning by using online functions. However, students sometimes abused the online class format by "just playing" the lecture while not paying attention and relying on other students' lecture transcripts to study. The results of this study are hoped to provide a useful foundation for future research on online class-based teaching and learning.