USING STRUCTURAL EQUATION MODELING METHODS TO ASSESS THE UNIVERSITY'S DIGITAL ECOSYSTEM
DOI:
https://doi.org/10.37943/17CCXJ5272Keywords:
digital transformation, digital ecosystem , mathematical statistics , structural equations, structural equation model , latent exogenous and endogenous variables, path diagram, asymptotically distribution-free estimation using Grammian (ADF)Abstract
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.
References
King, S., & Prasetyo, J. (2023). Assessing generative A.I. through the lens of the 2023 Gartner Hype Cycle for Emerging Technologies: a collaborative autoethnography, https://doi.org/10.3389/feduc.2023.1300391
Vicente-Saez, R., Gustafsson, R., Martinez-Fuentes, C. (2021). Opening up science for a sustainable world: An expansive normative structure of open science in the digital era. Science and Public Policy, 48(6), 799–813. https://doi.org/10.1093/scipol/scab049
Bass R., & Eynon B. (2017). From Unbundling to Rebundling: Design Principles for Transforming Institutions in the New Digital Ecosystem. The Magazine of Higher Learning, 49(2), 8–17. https://doi.org/10.1080/00091383.2017.1286211
Karaboga, T., Gurol, Y. D., Binici, C. M., Sarp, P. (2020). Sustainable Digital Talent Ecosystem in the New Era: Impacts on Businesses, Governments and Universities. Istanbul Business Research, 49(2), 360-379. https://doi.org/10.26650/ibr.2020.49.0009
Biloshchytska, S., Biloshchytskyi, A., Omirbayev, S., Mukhatayev, A., Faizullin, A., & Kassenov, K. (2021). A Conceptual Model And Process Management Method Of The Planning And Monitoring Of The Workload In The Educational Environment. Scientific Journal of Astana IT University, 5(5), 11–32. https://doi.org/10.37943/AITU.2021.22.54.002
Kropachev, P., Imanov, M., Borisevich, Y., & Dhomane, I. (2020). Information technologies and the future of education in the Republic of Kazakhstan. Scientific Journal of Astana IT University, 1, 30–38. https://doi.org/10.37943/AITU.2020.1.63639
Songsom, N., Nilsook, P., Wannapiroon, P., Chun Che Fung, L., & Wong, K.W. (2019). System Architecture of a Student Relationship Management System using Internet of Things to collect Digital Footprint of Higher Education Institutions. International Journal of Emerging Technologies in Learning (iJET), 14(23), 125–140. https://doi.org/10.3991/ijet.v14i23.11066
Prifti, L., Knigge, M., Löffler, A., Hecht, S., & Krcmar, H. (2017). Emerging Business Models in Education Provisioning: A Case Study on Providing Learning Support as Education-as-a-Service. International Journal of Engineering Pedagogy (iJEP), 7(3), 92–108. https://doi.org/10.3991/ijep.v7i3.7337
Zheng, Y. (2023). Evaluation of Online Teaching Effect of Vocational College Teachers Based on TOPSIS Technology and the Hierarchical Chi-Square Model. International Journal of Emerging Technologies in Learning (iJET), 18(15), 161–173. https://doi.org/10.3991/ijet.v18i15.42251
Cai, J. (2023). Evaluation of Blended Teaching in STEAM Education Using Structural Equation Model Questionnaire Technology. International Journal of Emerging Technologies in Learning (iJET), 18(19), 72–83. https://doi.org/10.3991/ijet.v18i19.43873
Gottipati, S., Shankararaman, V., & Gan, S. (2017). A conceptual framework for analyzing students’ feedback. IEEE Frontiers in Education Conference (FIE), 1-8. https://doi.org/10.1109/FIE.2017.8190703
Cunningham-Nelson, S., Baktashmotlagh, M., & Boles, W. Visualizing Student Opinion Through Text Analysis. IEEE Transactions on Education, 62 (4), 305-311. https://doi.org/10.1109/TE.2019.2924385
Kitsios F., Kamariotou M., Grigoroudis E. (2021). Digital Entrepreneurship Services Evolution: Analysis of Quadruple and Quintuple Helix Innovation Models for Open Data Ecosystems, Sustainability. https://doi.org/10.3390/su132112183
Fieuws, S., Verbeke, G., Boen, F., & Delecluse, C. (2006). High Dimensional Multivariate Mixed Models for Binary Questionnaire Data. Journal of the Royal Statistical Society Series C: Applied Statistics, 55 (4), 449–460. https://doi.org/10.1111/j.1467-9876.2006.00546.x
Schriesheim, C.A., & Hill, K.D. (1981). Controlling Acquiescence Response Bias by Item Reversals: The Effect on Questionnaire Validity. Educational and Psychological Measurement, 41(4), 1101-1114. https://doi.org/10.1177/001316448104100420
Kuliman, K., Kemala, S., Permata, D., Almasdi, A., & Fitri, N.H.A. (2024). Analysis of the Influence of the Marketing Mix on Consumer Purchasing Decisions Using the Structural Equation Modeling Method. International Journal of Islamic Economics, 5 (02), 126-142. https://doi.org/10.32332/ijie.v5i02.7865
Wolff C., Reimann C., Mikhaylova E., Aldaghamin A., Pampus S., & Hermann, E. (2021). Digital Education Ecosystem (DEE) for a Virtual Master School. IEEE International Conference on Smart Information Systems and Technologies (SIST), pp. 1-7, https://doi.org/10.1109/SIST50301.2021.9465914
Safiullin, M. R., & Akhmetshin, E. M. (2019). Digital transformation of a university as a factor of ensuring its competitiveness. International Journal of Engineering and Advanced Technology, 9(1), 7387–7390. https://doi.org/10.35940/ijeat.A3097.109119
Hong, A.J., & Kim, H.J. (2018). College Students’ Digital Readiness for Academic Engagement (DRAE) Scale: Scale Development and Validation. Asia-Pacific Edu Res 27, 303–312. https://doi.org/10.1007/s40299-018-0387-0
Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions of ‘useful’ digital technology in university teaching and learning. Studies in Higher Education, 42(8), 1567–1579. https://doi.org/10.1080/03075079.2015.1007946
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