Digital Education Ecosystem (DEE) for a Virtual Master School

Wolff, Carsten; Reimann, Christian; Mikhaylova, Ekaterina; Aldaghamin, Areej; Pampus, Sascha; Hermann, Ekaterina

doi:10.1109/sist50301.2021.9465914

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…Researchers from the Institute of Technology Bisnis Haji Agus Salim Bukittinggi, Indonesia in [16] use structural equation modeling (SEM) to determine the impact of the marketing mix and perform data processing in the Smart PLS version 3.0. Thus, to assess the satisfaction of the university's DES, it is advisable to use one of the methods of statistical data processing, in particular the method of structural equations [17].…”

Section: Methodsmentioning

confidence: 99%

Using Structural Equation Modeling Methods to Assess the University's Digital Ecosystem

Muratuly,

Denissova,

Dyomina

et al. 2024

sjaitu

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This paper explores the construction of a model for evaluating the digital ecosystem within a university, with a focus on identifying key factors influencing satisfaction with the implementation of new digital processes in the educational environment. The study employs mathematical methods, specifically factor analysis, to gauge the impact of these digital processes on the overall educational landscape. A questionnaire was designed to collect relevant data, and structural equation modeling, utilizing the asymptotically distribution-free estimation method with Grammian in STATISTICA software, was employed for survey result processing. The proposed model aims to provide insights into the dynamics of a university's digital ecosystem, offering a systematic approach to assess satisfaction levels and comprehend the implications of integrating novel digital processes within the educational framework. Mathematical methods, including factor analysis, add a quantitative dimension to the evaluation process, enabling a comprehensive understanding of the relationships between various factors. The study's methodology ensures a rigorous and systematic analysis of survey data, enhancing the reliability of the findings. The developed model and methodology contribute to advancing our understanding of the digitalization of university environments, providing valuable tools for decision-makers in shaping effective strategies for integrating digital processes in education. The study conducted a survey with 350 participants, including university staff and students. A questionnaire with 17 questions, both open and closed-ended, was developed to collect data. The authors employed structural equation modeling, specifically the asymptotically distribution-free estimation method, for data processing. The study's a posteriori model illustrates the structure of interaction factors influencing satisfaction with the university's digital ecosystem.

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Section: Methodsmentioning

confidence: 99%

Using Structural Equation Modeling Methods to Assess the University's Digital Ecosystem

Muratuly,

Denissova,

Dyomina

et al. 2024

sjaitu

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“…Thus, it is necessary to integrate and design educational resources, courses, programs, learning management system, infrastructure, etc. to improve its quality (Wolff et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Suleimankadieva et al (2021) pointed out that the development of a digital education ecosystem needed to ensure four main principles of interdisciplinarity, multidisciplinarity, and holism of system, as well as improvement of learners' cognition ability to solve complicated problems, work with and analyze amount of information and contents, and skills to design and evaluate other issues. Besides this, the design of a DLE needed to identify the adaptive and predictive ability for teachers and learners in the system to adapt to the new methods in educational activities (Wolff et al, 2021).…”

Section: Components Of a Digital Learning Ecosystemmentioning

confidence: 99%

Digital Learning Ecosystem for Classroom Teaching in Thailand High Schools

et al. 2023

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Digital learning ecosystem plays important roles for transforming teaching classroom into learning community. This article used a mixed method to (1) explore the teaching and learning activities and components of a digital learning ecosystem through eighteen in-depth interviews and six observation classroom teachings of three subjects (English, Mathematics, and Science), and (2) measure the factors influencing the success of the ecosystem from a survey of 350 teachers at high schools in Thailand under the KKU Smart Learning Project. The quantitative data is analyzed by using descriptive statistics, exploratory factor analysis, and confirmatory factor analysis; meanwhile, researchers used manually coding method for qualitative data to categorize data into themes and sub-themes. The findings revealed an ecosystem includes four core components: learning community, learning atmosphere, active learning, and teaching and learning support. However, it is important for junior high schools to clarify and improve seven factors affecting the development of this ecosystem to boost classroom transformation into learning community. This study is useful for policy makers, educators, and teachers to issue educational policies and objectives, invest infrastructure, design curriculum, and organize teaching activities to enhance the effectiveness of learning outcomes.

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“…It requires an elaborated cross-border IT infrastructure which was implemented and tested by Euro-PIM in the past, especially due to COVID. We call this cross-border digital learning and teaching infrastructure a "Digital Education Ecosystems (DEE)" [20].…”

Section: Conceptual Model For Project Selectionmentioning

confidence: 99%

Competence-Based Support for Project-Based Learning in Virtual Settings

Mikhridinova

Ngereja

Hussein

et al. 2023

Lecture Notes in Networks and Systems

Self Cite

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Project-based learning in higher education becomes more relevant and job-field-related if it is done in real, or at least realistic, project settings, e.g. including specific team situations or case studies from companies. Such projects are specifically valuable if they are conducted by internationally mixed student teams, best case in interdisciplinary teams. Online participation can be both more realistic and helpful in case physical mobility is not possible. Therefore, we are conducting virtual projects with cross-border student teams. Selecting the right projects and matching the right students with the relevant company cases is very important for an effective and efficient competence delivery. The project selection based on the desired competence gain, the development of a meaningful sequence of projects in order to implement an individual competence development path, the estimation of the contribution of the projects to the desired competence profile of the student, and the implementation of the projects need to be supported by digital tools and a virtual environment. This allows access to projects and company cases from different countries and an international, cross-border learning experience -best case in realistic interdisciplinary teams. This paper describes the concepts and tools for this approach and their experimental analysis.

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Digital Education Ecosystem (DEE) for a Virtual Master School

Cited by 14 publications

References 13 publications

Using Structural Equation Modeling Methods to Assess the University's Digital Ecosystem

Using Structural Equation Modeling Methods to Assess the University's Digital Ecosystem

Digital Learning Ecosystem for Classroom Teaching in Thailand High Schools

Competence-Based Support for Project-Based Learning in Virtual Settings

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