6 research outputs found

    Educaciรณn durante la pandemia COVID-19. Uso de la tecnologรญa en la nube: Jamboard

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    La enseรฑanza a distancia durante la pandemia Covid-19 es una situaciรณn retadora tanto para el docente como para el estudiante, ya que ambos necesitan adaptarse al aprendizaje remoto; sin embargo, a pesar del esfuerzo realizado por el docente, los resultados de participaciรณn del estudiante no siempre son los esperados. Al respecto, esta investigaciรณn aplicรณ la herramienta Jamboard, la cual permite que los estudiantes participen en tiempo real durante el desarrollo de la clase, con la constante observaciรณn del docente. Objetivo. conocer los niveles de satisfacciรณn de los estudiantes ante el uso del Jamboard. Materiales y mรฉtodos. El enfoque de la investigaciรณn fue cuantitativo, descriptivo de corte transversal. Se usรณ la tรฉcnica de la encuesta y el instrumento fue el cuestionario de satisfacciรณn de la herramienta Jamboard que se aplicรณ a 162 estudiantes de una universidad privada de la ciudad de Lima, Perรบ, del aรฑo 2020. Resultados. los estudiantes mejoraron bastante la motivaciรณn y el interรฉs en el curso, ademรกs se encontraron muy satisfechos con la herramienta Jamboard. Conclusiรณn. Se recomienda utilizar Jamboard para lograr una participaciรณn activa en la educaciรณn a distancia.Campus At

    Exploration of An Open Online Learning Platform Based on Google Cloud Computing

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    With the progress of society, traditional online learning platforms have demonstrated the uneven distribution of information resources, and teacherโ€“student communication exhibits a certain delay. At present, cloud computing, which is a new product of information technology, has been favored in many areas because of its superior feedback mechanism and storage space. Therefore, to improve the integration of online learning information resources and facilitate interaction between teachers and students, we designed our own online learning system based on the Google cloud computing platform. We used Googleโ€™s cloud computing platform and the Google App Engine to develop a unified and open online learning platform that is capable of storing large amounts of data, integrating considerable amounts of learning resources, and storing them on cloud. Through a test, we determined that the designed online learning platform for sharing information resources and integrating teacherโ€“student exchanges is highly beneficial. The platform helps the classroom learning atmosphere become active, and has a positive effect on teaching methods. The proposed platform can promote further development of online learning

    Afriican American Students\u27 Experiences of Stress from Discrimination in Online Doctoral Education

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    Abstract There is a lack of current research about the experiences of stress related to discrimination encountered by African American students in online doctoral programs. Such discrimination can negatively impact the academics, educational experiences, and overall health of this student population. In this generic qualitative study, how African American students in online doctoral programs interpreted, perceived, and responded to their experiences of stress regarding discrimination was explored. Using the conceptual framework of Lazarus and Folkmanโ€™s cognitive appraisal theory, the research questions addressed stress related to discrimination encountered in online educational institutions, discriminatory factors perceived as inhibitors towards earning a doctoral degree, and coping strategies utilized. Data were collected from 8 African American online doctoral students, including 3 men and 5 women, in Skype interviews, and NVivo 12 facilitated the thematic analysis of their responses. Findings indicate that African American online doctoral students perceive that they experience discrimination from faculty and university staff and that this perception leads to stress, depression, and self-doubt. Research is recommended on distinguishing student isolation based on online educational delivery from student isolation based on discrimination, objectively measuring discrimination, and including the perceptions of professors and administrators. The results of the study can inform university administrators and policymakers about the importance of addressing issues of discrimination that can negatively influence the academic success and health of African American students

    Military Breaking Boundaries Implementing Third-Party Cloud Computing Practices for Data Storage

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    Senior Information Technology (IT) military leadership cannot currently implement, maintain, and administer cloud data storage without the direct support of third-party vendors. This study explicitly impacts cloud practitioners, engineers, and architects requiring a most sophisticated and streamlined ability to safehouse invaluable data using third-party data storage. Grounded in the theory of planned behavior, the purpose of this qualitative single case study was to investigate strategies military leadership uses to implement third-party cloud computing for data storage. The participants (n = 22) consisted of cloud administrators, engineers, and architects within a sizeable midwestern city with a minimum of 3 years of cloud computing knowledge and 5 years of total IT experience. Data collection included semistructured interviews using Skype, face-to-face, and telephone interviews, and internal and external organizational documents (n = 17). Four themes were identified through thematic analysis: work relationships amongst AWS vendors and military technicians, the strength of newly created security practices, all training/learning curves are considered, and continuous safety and improvement. It is recommended that both AWS and military technicians continue to work together, promoting safety and security. The implications for positive social change include the potential for job creation and enhancing the community economically

    ์ค‘๋“ฑ ์ˆ˜ํ•™ ๊ต์œก์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ต์œกํ•™๊ณผ(๊ต์œก๊ณตํ•™์ „๊ณต), 2023. 2. ์ž„์ฒ ์ผ.In response to the demand for a perspective and strategic approach that fits the digital transformation era, policies that specify the implementation and support of technology-based education have been announced in Korea. Furthermore, due to the COVID-19 pandemic and the shift towards distance learning, the limitations of outdated devices and wireless internet constraints are being overcome. As a result, teachers and learners can more easily utilize cloud-based learning tools by using their own smart devices, and the amount of learning data available to teachers in the classroom has increased. Therefore, the environment to execute personalized classes based on learning data, which were previously difficult to implement in traditional big class settings, has been established through the use of cloud-based learning tools. However, to provide personalized support to individual learners and promote meaningful learning, it is necessary to go beyond simply incorporating technology into the education setting. When adopting new technology, teachers tend to adopt it in a way that maintains their existing teaching practices instead of changing them. Therefore, it is necessary to design a class that is different from the previous method in order to observe changes in instructors and learners and bring out positive changes by using cloud-based learning tools and learning data for personalized education at school sites. Therefore, this study aims to develop a practical and specific instruction model and strategies that guide teachers on how, when and what to do when using cloud-based learning tools to deliver data-based personalized education. To achieve this, the research questions were set to 1) develop a data-based personalized instruction model and strategy using cloud-based learning tools, and 2) examine the responses of instructors and learners to the developed instruction model and strategy. In the case of learner response, changes in mathematics learning achievement were examined in the cognitive aspect, and changes in learning attitude were examined in the affective aspect. This study was conducted according to the design and development research method as follows. Based on the empirical exploration of teachers using cloud-based learning tools at the site and an integrative literature review, the initial instruction model and instructional strategy were derived. In order to confirm the internal validity of the initial model developed, two rounds of internal validation were conducted with a total of six people, including three instructional design experts, two mathematics education experts, and one secondary education expert with experience in using cloud-based learning tools. The third version of instruction model was derived after the internal validation. An external validation was conducted with the revised model and strategy with in a math class of third graders in a middle school located in Seoul. As a method of external validation, quasi-experimental research and interviews were used. In the case of a quasi-experimental research, two class groups were set, one consisting of an experimental group using instruction models, strategies, and cloud-based learning tools, and the other consisting of a group that only used cloud-based learning tools. Before applying the instruction model and strategy, the homogeneity of the group was verified based on the results of the final exam grades in the first semester of the third graders and a pre-test on mathematics learning attitudes. In addition, after completing 8 lessons in the field trial, evaluations were carried out to assess the students' achievement in mathematics and their attitude towards learning mathematics The records of teacher and learner interviews during the field trial and the learners' reflection journals were analyzed. Based on the analysis, the instruction model and strategies were revised and improved to derive the final version. The final model consists of 5 steps: 1) learning data analysis, which affect on the all stages of the model, 2) planning, 3) execution, 4) evaluation, and 5) environment configuration supporting the previous 4 steps. In addition, 13 instructional strategies and 41 detailed guidelines were developed and offered with detailed examples and explanations. The result of the post-test comparison between the control group and the experimental group showed the following. In the case of the cognitive domain, no significant difference was found between the groups in the learning achievement test results. This shows that the instructional model and the strategies developed in this study do not have a significant effect on learning achievement. In the case of the affective domain, the results of a post-test on the attitude towards mathematics learning showed a significant difference in the area of interest and perception of value in mathematics compared to the control group in the experimental group. Also, when examining changes within the experimental group, it was confirmed that the participation during the model application period was statistically significantly higher compared to the participation before the model application period. Additionally, in the case of low-achieving learners, a large increase in post-participation was observed compared to other high-performing groups of learners. Interview results with teachers and learners showed that the developed instruction model was effectively utilized in implementing data-based personalized lessons with learning data and cloud-based learning tools. In particular, learners showed a higher degree of satisfaction compared to previous lessons. Based on the above research results, discussions and implications were derived and the need for follow-up studies was suggested. This study presents concrete steps and activities that should be taken by instructors when conducting data-based personalized learning in a school context with a cloud-based learning environment. It explores a realistic way of realizing personalized learning in school classrooms, which was difficult to implement in crowded classroom, by utilizing learning data and cloud-based learning tools. Additionally, the study highlights the importance of instructional design in utilizing cloud-based learning tools and learning data.๋””์ง€ํ„ธ ํŠธ๋žœ์Šคํฌ๋ฉ”์ด์…˜ ์‹œ๋Œ€์— ๋ถ€ํ•ฉํ•˜๋Š” ์‹œ๊ฐ๊ณผ ์ „๋žต์˜ ์‹ค์ฒœ์ด ์š”๊ตฌ๋จ์— ๋”ฐ๋ผ ๊ตญ๋‚ด์—์„œ๋Š” ํ…Œํฌ๋†€๋กœ์ง€ ํ™œ์šฉํ•œ ์ˆ˜์—…์˜ ์‹ค์ฒœ๊ณผ ์ง€์›์„ ๋ช…์‹œํ•˜๋Š” ์ •์ฑ…๋“ค์ด ๋ฐœํ‘œ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ๊ต์‚ฌ์™€ ํ•™์ƒ์˜ ์œ ์˜๋ฏธํ•œ ์Šค๋งˆํŠธ๊ธฐ๊ธฐ ์‚ฌ์šฉ์„ ์œ„ํ•œ ์ •์ฑ…๊ณผ ์ฝ”๋กœ๋‚˜ 19๋กœ ์ธํ•œ ์›๊ฒฉ ๊ต์œก ์‹œํ–‰์œผ๋กœ ์ธํ•˜์—ฌ ๋ฌผ๋ฆฌ์  ํ•œ๊ณ„์˜€๋˜ ๊ธฐ๊ธฐ์˜ ๋…ธํ›„ํ™”์™€ ๋ฌด์„ ์ธํ„ฐ๋„ท ํ™˜๊ฒฝ ์ œ์•ฝ์ด ๊ทน๋ณต๋˜๊ณ  ์žˆ๋‹ค. ์ด๋กœ ์ธํ•˜์—ฌ ๊ต์‚ฌ์™€ ํ•™์Šต์ž๋“ค์€ ๊ฐœ๋ณ„ ์Šค๋งˆํŠธ๊ธฐ๊ธฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ๋”์šฑ ์›ํ™œํ•˜๊ฒŒ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ์œผ๋ฉฐ ํ•™๊ต ํ˜„์žฅ์—์„œ ๊ต์‚ฌ๊ฐ€ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ•™์Šต ๋ฐ์ดํ„ฐ๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๊ธฐ์กด ๋‹ค์ธ์ˆ˜ ํ•™๊ธ‰ ํ˜•ํƒœ๋กœ ์ด๋ฃจ์–ด์ง€๋Š” ํ•™๊ต ๊ต์œก์—์„œ ์‹คํ˜„ํ•˜๊ธฐ ์–ด๋ ค์› ๋˜ ํ•™์Šต ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜์˜ ๋งž์ถคํ˜• ์ˆ˜์—…์„ ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ํ™˜๊ฒฝ์ด ์กฐ์„ฑ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฐœ๋ณ„ ํ•™์Šต์ž์—๊ฒŒ ๋งž์ถคํ˜• ์ง€์›์„ ์ œ๊ณตํ•˜๊ณ  ์œ ์˜๋ฏธํ•œ ํ•™์Šต์„ ์ด‰์ง„ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋‹จ์ˆœํžˆ ํ…Œํฌ๋†€๋กœ์ง€๋ฅผ ๊ต์œก ํ˜„์žฅ์— ํˆฌ์ž…ํ•˜๋Š” ๊ฒƒ์—์„œ ๋” ๋‚˜์•„๊ฐ€ ๋ณธ์งˆ์— ์žˆ์–ด์„œ ๋ณ€ํ™”๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ƒˆ๋กœ์šด ํ…Œํฌ๋†€๋กœ์ง€๋ฅผ ํ™œ์šฉํ•  ๋•Œ, ๊ต์‚ฌ๋“ค์€ ์ž์‹ ์˜ ๊ต์œก์  ํ–‰์œ„๋ฅผ ๋ฐ”๊พธ๊ธฐ๋ณด๋‹ค๋Š” ๊ธฐ์กด์˜ ํ–‰์œ„๋ฅผ ์œ ์ง€ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ฑ„ํƒํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ์™€ ํ•™์Šต ๋ฐ์ดํ„ฐ๋ฅผ ํ•™๊ต ํ˜„์žฅ์˜ ๋งž์ถคํ˜• ์ˆ˜์—…์„ ์œ„ํ•ด ํ™œ์šฉํ•จ์œผ๋กœ์จ ๊ต์ˆ˜์ž์™€ ํ•™์Šต์ž์˜ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜๊ณ  ๊ธ์ •์ ์ธ ๋ณ€ํ™”๋ฅผ ๋Œ์–ด๋‚ด๊ธฐ ์œ„ํ•ด์„œ ์ด์ „์˜ ๋ฐฉ์‹๊ณผ ๋‹ค๋ฅธ ์ˆ˜์—…์„ ์„ค๊ณ„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ต์‚ฌ๊ฐ€ ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋งž์ถคํ˜• ์ˆ˜์—…์„ ์ง„ํ–‰ํ•  ๋•Œ, ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ†ตํ•ด์„œ ์ˆ˜์ง‘๋˜๋Š” ํ•™์Šต ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋งž์ถคํ˜• ์ˆ˜์—…์ด ํ•™๊ต ํ˜„์žฅ์—์„œ ํšจ๊ณผ์ ์œผ๋กœ ์ด๋ฃจ์–ด์งˆ ์ˆ˜ ์žˆ๋„๋ก ๊ต์‚ฌ๊ฐ€ ์–ธ์ œ, ๋ฌด์—‡์„, ์–ด๋–ป๊ฒŒ ํ•ด์•ผ ํ•˜๋Š”์ง€๋ฅผ ์•ˆ๋‚ดํ•˜๋Š” ์‹ค์ œ์ ์ด๊ณ  ๊ตฌ์ฒด์ ์ธ ์ˆ˜์—… ๋ชจํ˜•๊ณผ ์ „๋žต, ์ง€์นจ์„ ๊ฐœ๋ฐœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•˜์—ฌ 1) ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•œ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋งž์ถคํ˜• ์ˆ˜์—… ๋ชจํ˜•๊ณผ ์ „๋žต์„ ๊ฐœ๋ฐœํ•˜๊ณ  2) ๊ฐœ๋ฐœ๋œ ์ˆ˜์—… ๋ชจํ˜•๊ณผ ์ „๋žต์— ๋Œ€ํ•œ ๊ต์ˆ˜์ž์™€ ํ•™์Šต์ž์˜ ๋ฐ˜์‘์„ ๊ฒ€ํ† ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์—ฐ๊ตฌ ๋ฌธ์ œ๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ํ•™์Šต์ž ๋ฐ˜์‘์˜ ๊ฒฝ์šฐ, ์ธ์ง€์  ์ธก๋ฉด์—์„œ ์ˆ˜ํ•™ ํ•™์Šต ์„ฑ์ทจ๋„์˜ ๋ณ€ํ™”๋ฅผ ์‚ดํŽด๋ณด์•˜๊ณ  ์ •์˜์  ์ธก๋ฉด์—์„œ๋Š” ํ•™์Šต ํƒœ๋„์˜ ๋ณ€ํ™”๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ค๊ณ„ ๊ฐœ๋ฐœ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์— ๋”ฐ๋ผ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ ˆ์ฐจ๋กœ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ํ˜„์žฅ์—์„œ ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•œ ๊ต์‚ฌ๋“ค๊ณผ์˜ ๊ฒฝํ—˜์  ํƒ์ƒ‰ ๋ฐ ํ†ตํ•ฉ์  ๋ฌธํ—Œ ๊ฒ€ํ† ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ดˆ๊ธฐ ์ˆ˜์—… ๋ชจํ˜•๊ณผ ๊ต์ˆ˜์ „๋žต์„ ๋„์ถœํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ์ดˆ๊ธฐ ๋ชจํ˜•์˜ ๋‚ด์  ํƒ€๋‹น์„ฑ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ต์ˆ˜ ์„ค๊ณ„ ์ „๋ฌธ๊ฐ€ 3์ธ, ์ˆ˜ํ•™ ๊ต์œก ์ „๋ฌธ๊ฐ€ 2์ธ, ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ ํ™œ์šฉ ๊ฒฝํ—˜์ด ์žˆ๋Š” ์ค‘๋“ฑ ๊ต์œก ์ „๋ฌธ๊ฐ€ 1์ธ์„ ํฌํ•จํ•œ ์ด 6์ธ์—๊ฒŒ ๋‘ ์ฐจ๋ก€์˜ ๋‚ด์  ํƒ€๋‹นํ™”๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋‚ด์  ํƒ€๋‹นํ™”๋ฅผ ํ†ตํ•ด ๋„์ถœ๋œ 3์ฐจ ์ˆ˜์—… ๋ชจํ˜•๊ณผ ์ „๋žต์— ๋Œ€ํ•œ ์™ธ์  ํƒ€๋‹นํ™”๋ฅผ ์„œ์šธ ์†Œ์žฌ์˜ ํ•œ ์ค‘ํ•™๊ต 3ํ•™๋…„ ์ˆ˜ํ•™ ์ˆ˜์—…์—์„œ ์‹ค์‹œํ•˜์˜€๋‹ค. ์™ธ์  ํƒ€๋‹นํ™”์˜ ๋ฐฉ๋ฒ•์œผ๋กœ ์œ ์‚ฌ์‹คํ—˜๊ณผ ๋ฉด๋‹ด์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์œ ์‚ฌ์‹คํ—˜์˜ ๊ฒฝ์šฐ ์ˆ˜์—… ๋ชจํ˜•๊ณผ ์ „๋žต ๊ทธ๋ฆฌ๊ณ  ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•œ ์‹คํ—˜ ์ง‘๋‹จ๊ณผ ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋งŒ ํ™œ์šฉํ•œ ์ง‘๋‹จ์œผ๋กœ ์ด๋ฃจ์–ด์ง„ 2๊ฐœ ํ•™๊ธ‰ ๋‹จ์œ„์˜ ์ง‘๋‹จ์„ ์„ค์ •ํ•˜์˜€๋‹ค. ์ˆ˜์—… ๋ชจํ˜•๊ณผ ์ „๋žต์˜ ์ ์šฉ ์ „์— 3ํ•™๋…„ 1ํ•™๊ธฐ ๊ธฐ๋ง ๊ณ ์‚ฌ ์„ฑ์ ๊ณผ ์ˆ˜ํ•™ ํ•™์Šต ํƒœ๋„ ๊ด€๋ จ ์‚ฌ์ „ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ์ค€์œผ๋กœ ์ง‘๋‹จ์˜ ๋™์งˆ์„ฑ ์—ฌ๋ถ€๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด 8์ฐจ์‹œ์˜ ํ˜„์žฅ ์ ์šฉ์ด ์ข…๋ฃŒ๋œ ์‹œ์ ์— ์„ฑ์ทจ๋„ ํ‰๊ฐ€์™€ ์ˆ˜ํ•™ ํ•™์Šต์— ๋Œ€ํ•œ ํƒœ๋„์— ๋Œ€ํ•œ ๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ต์ˆ˜์ž ๋ฐ ํ•™์Šต์ž ๋Œ€์ƒ ๋ฉด๋‹ด ์ „์‚ฌ ๊ธฐ๋ก๊ณผ ํ•™์Šต์ž์˜ ์„ฑ์ฐฐ์ผ์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ถ„์„ ๋‚ด์šฉ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ˆ˜์—… ๋ชจํ˜•๊ณผ ๊ต์ˆ˜์ „๋žต์„ ์ˆ˜์ • ๋ณด์™„ํ•˜์—ฌ, ์ตœ์ข… ์ˆ˜์—… ๋ชจํ˜• ๋ฐ ๊ต์ˆ˜์ „๋žต์„ ๋„์ถœํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ๋ชจํ˜• ์ „์ฒด ๋‹จ๊ณ„์— ์˜ํ–ฅ์„ ์ฃผ๋Š” 1) ํ•™์Šต ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‹œํ–‰๋˜๋Š” 2) ๊ณ„ํš, 3) ์‹คํ–‰, 4) ํ‰๊ฐ€ ๊ทธ๋ฆฌ๊ณ  ์ด 4๋‹จ๊ณ„๋ฅผ ์ง€์›ํ•˜๋Š” 5) ํ™˜๊ฒฝ ๊ตฌ์„ฑ์œผ๋กœ ์ด๋ฃจ์–ด์ง„ 5๋‹จ๊ณ„์˜ ์ตœ์ข… ๋ชจํ˜•์ด ๋„์ถœ๋˜์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฐ๊ฐ์˜ ๋‹จ๊ณ„๋ฅผ ์ง€์›ํ•˜๋Š” 13๊ฐœ์˜ ๊ต์ˆ˜์ „๋žต๊ณผ 41๊ฐœ์˜ ์ƒ์„ธ์ง€์นจ ๊ทธ๋ฆฌ๊ณ  ์˜ˆ์‹œ ๋ฐ ํ•ด์„ค์ด ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ํ†ต์ œ์ง‘๋‹จ๊ณผ ์‹คํ—˜ ์ง‘๋‹จ์˜ ์‚ฌํ›„๊ฒ€์‚ฌ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๊ณผ๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์ธ์ง€์  ์˜์—ญ์˜ ๊ฒฝ์šฐ, ํ•™์Šต ์„ฑ์ทจ๋„ ๊ฒ€์‚ฌ ๊ฒฐ๊ณผ์—์„œ ์ง‘๋‹จ ๊ฐ„์˜ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š์•˜๋‹ค. ์ด๋Š” ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœ๋œ ์ˆ˜์—… ๋ชจํ˜•๊ณผ ๊ต์ˆ˜์ „๋žต์ด ํ•™์Šต ์„ฑ์ทจ๋„์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ •์˜์  ์˜์—ญ์˜ ๊ฒฝ์šฐ, ์ˆ˜ํ•™ ํ•™์Šต์— ๋Œ€ํ•œ ํƒœ๋„์— ๋Œ€ํ•œ ์‚ฌํ›„๊ฒ€์‚ฌ ๊ฒฐ์—์„œ ์‹คํ—˜ ์ง‘๋‹จ์ด ํ†ต์ œ ์ง‘๋‹จ๊ณผ ๋น„๊ตํ•˜์—ฌ ์ˆ˜ํ•™ ๊ต๊ณผ์— ๋Œ€ํ•œ ํฅ๋ฏธ์™€ ๊ฐ€์น˜ ์ธ์‹ ์˜์—ญ์—์„œ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ์‹คํ—˜ ์ง‘๋‹จ ๋‚ด์˜ ๋ณ€ํ™”๋ฅผ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ๋ชจํ˜• ์ ์šฉ ๊ธฐ๊ฐ„ ์ค‘์˜ ์ฐธ์—ฌ๋„๊ฐ€ ๋ชจํ˜• ์ ์šฉ ๊ธฐ๊ฐ„ ์ „์˜ ์ฐธ์—ฌ๋„์— ๋น„ํ•ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋†’์€ ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์ €์„ฑ์ทจ ํ•™์Šต์ž์˜ ๊ฒฝ์šฐ, ๋‹ค๋ฅธ ์ƒ์œ„ ์ง‘๋‹จ์˜ ํ•™์Šต์ž๋“ค์— ๋น„ํ•ด ์‚ฌํ›„ ์ฐธ์—ฌ๋„ ์ฆ๊ฐ€ ํญ์ด ํฌ๊ฒŒ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ํ•™์Šต ์„ฑ์ทจ ๋ณ€ํ™”์—์„œ๋„ ์ €์„ฑ์ทจ ํ•™์Šต์ž๋“ค์˜ ์„ฑ์ทจ๋„ ์ƒ์Šน ํญ์ด ๋‹ค๋ฅธ ์ƒ์œ„ ์„ฑ์ทจ๋ฅผ ๋ณด์ด๋Š” ํ•™์Šต์ž ์ง‘๋‹จ์— ๋น„ํ•ด ๋งค์šฐ ํฌ๊ฒŒ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ๊ต์ˆ˜์ž์™€ ํ•™์Šต์ž ๋Œ€์ƒ์œผ๋กœ ์‹ค์‹œํ•œ ๋ฉด๋‹ด ๊ฒฐ๊ณผ, ๊ฐœ๋ฐœ๋œ ์ˆ˜์—… ๋ชจํ˜•์ด ํ•™์Šต ๋ฐ์ดํ„ฐ์™€ ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋งž์ถคํ˜• ์ˆ˜์—…์„ ์‹œํ–‰ํ•  ๋•Œ ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉ๋˜์—ˆ์Œ์ด ๊ด€์ฐฐ๋˜์—ˆ์œผ๋ฉฐ, ํŠนํžˆ ํ•™์Šต์ž๋“ค์˜ ๊ฒฝ์šฐ ๊ธฐ์กด ์ˆ˜์—…๊ณผ ๋น„๊ตํ•˜์˜€์„ ๋•Œ ๋” ๋†’์€ ๋งŒ์กฑ์Šค๋Ÿฝ๋‹ค๋Š” ๋ฐ˜์‘์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด์ƒ์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๊ธฐ์ดˆํ•˜์—ฌ ์ตœ์ข… ์ˆ˜์—… ๋ชจํ˜•๊ณผ ์ˆ˜์—… ์ „๋žต๊ณผ ์ด์— ๋Œ€ํ•œ ๋…ผ์˜ ๋ฐ ์‹œ์‚ฌ์ ์„ ๋„์ถœํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ฐ–๋Š” ํ•œ๊ณ„๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ›„์† ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์„ ์ œ์–ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต ํ™˜๊ฒฝ์ด ์กฐ์„ฑ๋œ ํ•™๊ต ๋งฅ๋ฝ์—์„œ ๊ต์ˆ˜์ž๊ฐ€ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋งž์ถคํ˜• ์ˆ˜์—…์„ ์ง„ํ–‰ํ•  ๋•Œ, ์ˆ˜ํ–‰ํ•ด์•ผ ํ•˜๋Š” ๋‹จ๊ณ„์™€ ํ™œ๋™์„ ๊ตฌ์ฒด์ ์œผ๋กœ ์ œ์‹œํ•˜์˜€๋‹ค๋Š” ์ , ๋‹ค์ธ์ˆ˜ ํ•™๊ธ‰์—์„œ ์‹ค์ฒœํ•˜๊ธฐ ์–ด๋ ค์› ๋˜ ๋งž์ถคํ˜• ์ˆ˜์—…์„ ํ•™์Šต ๋ฐ์ดํ„ฐ์™€ ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํ˜„์‹ค์ ์œผ๋กœ ํ•™๊ต ์ˆ˜์—…์—์„œ ์‹คํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ์„ ํƒ์ƒ‰ํ–ˆ๋‹ค๋Š” ์ , ๊ทธ๋ฆฌ๊ณ  ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ์™€ ํ•™์Šต ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜๋Š” ๋ฐ ์žˆ์–ด ๊ต์ˆ˜ ์„ค๊ณ„๊ฐ€ ๊ฐ–๋Š” ์ค‘์š”์„ฑ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ๊ฐ–๋Š”๋‹ค.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ๊ณผ ๋ชฉ์  1 2. ์—ฐ๊ตฌ ๋ฌธ์ œ 7 3. ์šฉ์–ด์˜ ์ •์˜ 8 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 10 1. ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ 10 ๊ฐ€. ํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ…์˜ ์ •์˜์™€ ํŠน์ง• 10 ๋‚˜. ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ 15 ๋‹ค. ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•œ ์ˆ˜์—… ๋ชจํ˜• 19 2. ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋งž์ถคํ˜• ์ˆ˜์—… 25 ๊ฐ€. ๋งž์ถคํ˜• ์ˆ˜์—… 25 ๋‚˜. ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋งž์ถคํ˜• ์ˆ˜์—… ๋ชจํ˜• 29 3. ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•˜๋Š” ์ˆ˜ํ•™ ์ˆ˜์—… ๋ชจํ˜• 36 ๊ฐ€. ICT ํ™œ์šฉ ์ˆ˜ํ•™ ์ˆ˜์—… ๋ชจํ˜• 36 ๋‚˜. ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•œ ์ˆ˜ํ•™ ์ˆ˜์—… ๋ชจํ˜•์˜ ๊ฐ€๋Šฅ์„ฑ 40 III. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 42 1. ์—ฐ๊ตฌ ์ ˆ์ฐจ 43 2. ์ดˆ๊ธฐ ์ˆ˜์—… ๋ชจํ˜• ๋ฐ ์ „๋žต ๊ฐœ๋ฐœ 46 ๊ฐ€. ๊ฒฝํ—˜์  ํƒ์ƒ‰ 46 ๋‚˜. ์„ ํ–‰๋ฌธํ—Œ ๊ฒ€ํ†  46 3. ๋‚ด์  ํƒ€๋‹นํ™” 49 ๊ฐ€. ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž 49 ๋‚˜. ์—ฐ๊ตฌ ๋„๊ตฌ ๋ฐ ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ• 50 4. ์™ธ์  ํƒ€๋‹นํ™” 53 ๊ฐ€. ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž 54 ๋‚˜. ํ˜„์žฅ ์ ์šฉ ์ ˆ์ฐจ 57 ๋‹ค. ์—ฐ๊ตฌ ๋„๊ตฌ ๋ฐ ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ• 83 โ…ฃ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 88 1. ์ตœ์ข… ๋ชจํ˜• ๊ฐœ๋ฐœ 89 ๊ฐ€. ๋ชจํ˜•์˜ ๊ฐ€์ • ๋ฐ ํŠน์ง• 89 ๋‚˜. ์ตœ์ข… ์ˆ˜์—… ๋ชจํ˜•๊ณผ ๊ต์ˆ˜์ „๋žต 91 2. ์ดˆ๊ธฐ ์ˆ˜์—… ๋ชจํ˜• ๋ฐ ๊ต์ˆ˜์ „๋žต 118 ๊ฐ€. ๊ฒฝํ—˜์  ํƒ์ƒ‰๊ณผ ์„ ํ–‰๋ฌธํ—Œ ๊ฒ€ํ† ๋ฅผ ํ†ตํ•œ ์ˆ˜์—… ๋ชจํ˜• ๋ฐ ๊ต์ˆ˜์ „๋žต ๋„์ถœ 118 ๋‚˜. ์ดˆ๊ธฐ ์ˆ˜์—… ๋ชจํ˜• ๋ฐ ๊ต์ˆ˜์ „๋žต ๊ฐœ๋ฐœ 129 3. ๋‚ด์  ํƒ€๋‹นํ™” ๊ฒฐ๊ณผ 139 ๊ฐ€. 1์ฐจ ์ „๋ฌธ๊ฐ€ ํƒ€๋‹นํ™” ๊ฒฐ๊ณผ 139 ๋‚˜. 2์ฐจ ์ „๋ฌธ๊ฐ€ ํƒ€๋‹นํ™” ๊ฒฐ๊ณผ 167 4. ์™ธ์  ํƒ€๋‹นํ™” ๊ฒฐ๊ณผ 197 ๊ฐ€. ์‚ฌํ›„๊ฒ€์‚ฌ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 197 ๋‚˜. ์‹คํ—˜ ์ง‘๋‹จ ๋‚ด์˜ ๋ณ€ํ™” ๋ถ„์„ 199 ๋‹ค. ์ˆ˜์—… ๋ชจํ˜•์— ๋Œ€ํ•œ ๊ต์ˆ˜์ž ๋ฐ ํ•™์Šต์ž ๋ฐ˜์‘ 206 โ…ค. ๋…ผ์˜ ๋ฐ ๊ฒฐ๋ก  211 1. ๋…ผ์˜ 211 ๊ฐ€. ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•œ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋งž์ถคํ˜• ์ˆ˜์—… ๋ชจํ˜•๊ณผ ๊ต์ˆ˜์ „๋žต 211 ๋‚˜. ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜ ํ•™์Šต๋„๊ตฌ๋ฅผ ํ™œ์šฉํ•œ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋งž์ถคํ˜• ์ˆ˜์—… ๋ชจํ˜•๊ณผ ๊ต์ˆ˜์ „๋žต์— ๋Œ€ํ•œ ๊ต์ˆ˜์ž ๋ฐ ํ•™์Šต์ž ๋ฐ˜์‘ 215 2. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 219 ๊ฐ€. ๊ฒฐ๋ก  219 ๋‚˜. ์ œ์–ธ 221 ์ฐธ๊ณ ๋ฌธํ—Œ 223 ๋ถ€ ๋ก 229 Abstract 262์„

    A Narrative Study of Technology-Oriented Academicsโ€™ Autonomy within the Context of Cloud Computing and Cloud-Based Services in Higher Education

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    Ed. D. ThesisThis study aims to develop an in-depth understanding of the intersections between academicsโ€™ technology-orientation, autonomy, and pedagogical practices with cloud computing and cloudbased services within higher education. Two purposes framed this study. The first is to understand how technology-oriented academics conceptualise and utilise cloud computing platforms and services in their pedagogical practices. The second is to explore how these experiences intersect with academicsโ€™ autonomy within the context of higher education. This studyโ€™s motivation was the current confluence on academicsโ€™ autonomy due to higher education structural changes and cloudbased services emergence. Nine academics from a Gulf Cooperation Council higher education institution were recruited using โ€˜criterion-based purposeful selectionโ€™ (Schensul & LeCompte, 2012). The selection process considered their orientations towards using technology in their pedagogical practices. Using qualitative narrative methodology (Moen, 2006; Willis, 2008; McAlpine, 2016), data sources included a series of individual, paired depth and group interviews, participantsโ€™ reflections, researcherโ€™s notes, and relevant material. Triangulation of methods, ongoing iterative dialogue with the participants, and thematic analysis (Clarke & Braun, 2018) contributed to this studyโ€™s rigour. The findings show that academicsโ€™ technology-orientations positively influence their critical perspectives and decision-making towards utilising cloud-based services in their professional development and pedagogical practices. Their orientations, backgrounds, capacities, roles, and objectives influenced their autonomy to variable degrees. The participantsโ€™ technology orientation aligned with their autonomous pedagogical practices with cloud-based services. CC and CBSโ€™s design and features within the participantsโ€™ work conditions seem to afford and equally constrain their cloud-based pedagogic experiences. This paradox yielded three modes of academicsโ€™ autonomy, Constrained, Guided, and Self-Directed, intersecting four modes of cloud-based pedagogies, Expanding the Curriculum, Redefining Pedagogy, Cautious Pedagogy, and Visionary Pedagogy. These findings indicate bounded academicsโ€™ autonomy in the context of cloud-based pedagogy. This thesis extends the field of intersectional studies between technology and higher education. It contributes to understanding academicsโ€™ pedagogic experiences at a time of change in higher education. It also raises important questions concerning the implications of academicsโ€™ autonomy and institutional autonomy impacts upon the ethical cloud-based practice
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