Application of the Stirling-Darling Technique in an Intelligent Knowledge Extraction Model for Educational Environments

Document Type : Original Research Manuscripts

Author

PhD in Knowledge and Information Science, Knowledge Management, Tarbiat Modares University, Tehran, Iran.

10.22034/lss.2024.495136.1030
Abstract
This study aims to present an intelligent knowledge extraction model for educational environments. The participants were managers of educational centers. Sampling was conducted with 18 experts and specialists. The data collection tools were a review of upstream documents related to education and data extraction in the library section, and semi-structured interviews. The content analysis method based on the Attride-Stirling model was employed to analyze qualitative data. To measure reliability, the Holst coefficient, Scott's p-coefficient, Cohen's kappa index, and Krippendorff's alpha were utilized and confirmed. ATLASTI software was utilized in the content analysis section. In the research employing the Stirling-Darling technique, 75 initial codes were identified across 15 initial themes, which were further categorized into 5 constituent themes. Foundational Concepts and Theoretical Frameworks, Technological Integration and Data Management, Learning Environments and Pedagogical Strategies, Assessment, Evaluation, and Challenges, Future Directions, Professional Development, and Community Engagement. The successful implementation of knowledge extraction in educational centers necessitates a comprehensive approach that considers all these elements in an integrated manner. Advanced technologies should be employed to collect and analyze educational data. Furthermore, the internal and external conditions of educational institutions should be designed and enhanced to facilitate the optimal utilization of this data. This initiative has the potential to transform educational processes and result in significant enhancements in the quality of teaching and learning within educational institutions. To facilitate the flourishing of knowledge extraction within educational models and enhance educational centers, it is crucial to pay attention to infrastructure, internal conditions, and environmental factors.

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Abdi, A., Sedrakyan, G., Veldkamp, B. et al. Students feedback analysis model using deep learning-based method and linguistic knowledge for intelligent educational systems. Soft Comput 27, 14073–14094 (2023). https://doi.org/10.1007/s00500-023-07926-2
Allison, J. T., Cardin, M. A., McComb, C., Ren, M. Y., Selva, D., Tucker, C., ... & Zhao, Y. F. (2022). Artificial Intelligence and Engineering Design. Journal of Mechanical Design, 144(2). https://doi.org/10.1115/1.4053111
AlMulhim, A. (2020). The effect of tacit knowledge and organizational learning on financial performance in service industry. Management Science Letters, 10(10), 2211-2220. https://doi.org/10.5267/j.msl.2020.3.015
Aramoon, E., & Aramoon, V. (2019). Identifying and prioritizing the cultural factors effective on the successful implementation of knowledge management in the industry of electronic insurance services. Romanian Journal of Information Technology and Automatic Control, 29(2), 69-84. https://doi.org/10.33436/v29i2y201906
Butkus, M., Dargenytė-Kacilevičienė, L., Matuzevičiūtė, K., Ruplienė, D., & Šeputienė, J. (2022). Do Gender and Age Matter in Employment–Sectoral Growth Relationship Over the Recession and Expansion. Ekonomika, 101(2), 38-51. https://doi.org/10.15388/Ekon.2022.101.2.3
Grubel, H. G., & Walker, M. A. (2019). Modern service sector growth: Causes and effects. Services in world economic growth, 1-34. https://doi.org/10.4324/9780429305849-1
Guckenbiehl, P. (2022). Investigating Knowledge and Business Model Innovation for Start-up Success (Doctoral dissertation).
Hassanzadeh, M. (2021). Editor’s Note: Transformational Knowledge Management: A New Generation of Knowledge Management to Facilitate Digital Transformation. Information Management Sciences and Technologies, 7(4): 14-7. [in Persian]
Hassanzadeh, M. (2021). The Fifth Data Revolution, the Unparalleled Role of Intelligent Agents and the Necessity of a National Data Organization. Information Management Sciences and Technologies, 7(3): 7-16. [in Persian]
Huang, F., Cheng, L. Distant supervision knowledge extraction and knowledge graph construction method for supply chain management domain. Auton. Intell. Syst. 4, 7 (2024). https://doi.org/10.1007/s43684-024-00064-y
Li, K., Niskanen, J., Kolehmainen, M., & Niskanen, M. (2016). Financial innovation: Credit default hybrid model for SME lending. Expert Systems with Applications, 61, 343-355.
Kang, X. (2021). Combining rough set theory and support vector regression to the sustainable form design of hybrid electric vehicle. Journal of Cleaner Production, 304, 127137. https://doi.org/10.1016/j.jclepro.2021.127137
Magoti, E., & Mtui, J. M. (2020). The relationship between economic growth and service sector in Tanzania: An empirical investigation. African Journal of Economic Review, 8(2), 219-238.
Nordsieck, R., Heider, M., Winschel, A. and Hähner, J. (2021). Knowledge Extraction via Decentralized Knowledge Graph Aggregation. IEEE 15th International Conference on Semantic Computing (ICSC), 2021, pp. 92-99, https://doi.org/10.1109/ICSC50631.2021.00024.
Ogata, H., Liang, C., Toyokawa, Y. et al. Co-designing Data-Driven Educational Technology and Practice: Reflections from the Japanese Context. Tech Know Learn (2024). https://doi.org/10.1007/s10758-024-09759-w
Parriaux, G., Reffay, C., Drot-Delange, B., Khaneboubi, M. (2023). Teachers’ Knowledge in Informatics—Exploring Educational Robotics Resources Through the Lens of Textual Data Analysis. In: Pellet, JP., Parriaux, G. (eds) Informatics in Schools. Beyond Bits and Bytes: Nurturing Informatics Intelligence in Education. ISSEP 2023. Lecture Notes in Computer Science, vol 14296. Springer, Cham. https://doi.org/10.1007/978-3-031-44900-0_10
Peng, C., Xia, F., Naseriparsa, M. et al. Knowledge Graphs: Opportunities and Challenges. Artif Intell Rev 56, 13071–13102 (2023). https://doi.org/10.1007/s10462-023-10465-9
Rojas, M.P., Chiappe, A. Artificial Intelligence and Digital Ecosystems in Education: A Review. Tech Know Learn (2024). https://doi.org/10.1007/s10758-024-09732-7
Sedkaoui, S., Khelfaoui, M., & Kadi, N. (2021). Does Technological Context Support Academic Entrepreneurship Activities in Algeria?. In Digital Literacy and Socio-Cultural Acceptance of ICT in Developing Countries (pp. 79-100). Springer, Cham. https://doi.org/10.1007/978-3-030-61089-0_6
Segooa, M. A., Kalema, B. M., & Zolait, A. H. (2019, September). Predictive analytics to improve outcome-based funding for the public universities in South Africa through big data. In 2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (pp. 1-6). IEEE. https://doi.org/10.1109/3ICT.2019.8910285
Sghir, N., Adadi, A. & Lahmer, M. Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022). Educ Inf Technol 28, 8299–8333 (2023). https://doi.org/10.1007/s10639-022-11536-0
Waghmare, S. (2020). APPLICATION OF KNOWLEDGE MANAGEMENT ADOPTION IN SERVICE INDUSTRY. International Journal of Advanced Research. 8. 538-542. https://doi.org/10.21474/IJAR01/10327 
Zielińska, K. (2016). Financial stability in the eurozone. Comparative Economic Research, 19(1), 157-177. https://doi.org/10.1515/cer-2016-0009
Zimpel-Leal, K., & Lettice, F. (2021). Generative mechanisms for scientific knowledge transfer in the food industry. Sustainability, 13(2), 955. https://doi.org/10.3390/su13020955

  • Receive Date 16 April 2024
  • Revise Date 21 April 2024
  • Accept Date 19 May 2024