Enhancing Knowledge Transformation in Digital Education: An Analysis of the SECI Model's Application in Course Design and Execution

Enhancing Knowledge Transformation in Digital Education: An Analysis of the SECI Model's Application in Course Design and Execution | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Enhancing Knowledge Transformation in Digital Education: An Analysis of the SECI Model's Application in Course Design and Execution Dmitrij Żatuchin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3988920/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the application of the SECI model in the design and execution of educational courses in "Innovation and Digitization Management" and "Data-Based Decision Making" micro-degrees developed in 2023 and 2024. Leveraging Natural Language Processing (NLP) and text analysis, we explore the patterns in course content that correlate with positive learner feedback and effective knowledge transformation. The methodology includes data preprocessing, tokenization, vectorization, and clustering to systematically compare and contrast course elements with learner feedback. Preliminary findings indicate that the integration of the SECI model, emphasizing real-time content sharing and example-based learning, significantly enhances the transfer of knowledge from tacit to explicit forms. This research aims to identify a replicable cadence in content preparation that optimizes learning outcomes. Educational Philosophy and Theory Information Theory Information Retrieval and Management Decision Sciences SECI model knowledge transformation digital education course design natural language processing tacit knowledge explicit knowledge learner feedback Figures Figure 1 1. INTRODUCTION In the evolving landscape of digital education, the SECI model, as elucidated by Nonaka et al. ( 2000 ), presents a structured framework for the transformation of tacit knowledge into explicit knowledge, thereby facilitating a dynamic learning environment. The model's integration into course design, particularly through the improvisation of problem-based learning (PBL) approaches, enhances knowledge transfer, engagement, and practical application among learners, as demonstrated by Tee and Lee ( 2011 ). Furthermore, the importance of aligning educational practices with industry demands, highlighted by Khousa et al. ( 2015 ), underscores the model's applicability in preparing students for professional challenges. This study, drawing upon insights from Gerami ( 2010 ) and Guo & Xue ( 2020 ), explores the SECI model's versatility across various educational fields, emphasizing its potential to bridge educational practices with pragmatic professional world demands. By evaluating the SECI model's application in recent course developments, this research aims to uncover effective strategies that resonate with learners and promote deep learning. Despite the well-established theoretical benefits of the SECI model in knowledge management, its empirical examination within the context of digital education, particularly in creating engaging learning environments, remains scant. The facilitation of tacit knowledge sharing is augmented by insights from Tee and Lee ( 2011 ), alongside Guzman and Trivelato ( 2011 ), who discuss the complexities of knowledge codification and transfer in higher education. This gap is addressed by evaluating course designs and feedback to discern patterns indicative of successful knowledge transformation, thus validating the hypothesis that courses rooted in the SECI model's principles will garner positive feedback from participants and demonstrate effective knowledge transfer. 2. METHODOLOGY This study employs Natural Language Processing (NLP) and text analysis techniques to analyze course materials and participant feedback from 'Innovation and Digitization Management' and 'Data-Based Decision Making' programs, to uncover recurring themes and patterns that correlate with positive learning outcomes. The methodology includes text preprocessing, numerical transformation using TF-IDF, and the application of K-means clustering to reveal shared themes within the course content. Inspired by the efficiency of cross-lingual alignment techniques as discussed by Lorenzo et al.(2023), our approach seeks to extend the analytical depth by considering the nuances of language and terminology used across different educational contexts. The use of K-means clustering further organizes the content into clusters based on similarity, allowing for an in-depth analysis of central terms and prevalent topics, thereby shedding light on effective content preparation strategies. This comprehensive analytical framework, supported by the foundational works of Li et al. ( 2018 ) and Pan et al. ( 2021 ), enables a nuanced understanding of how digital innovation and the SECI model can be synergistically employed to enhance educational outcomes. 3. FINDINGS 3.1. Feedback from participants of courses Feedback from participants underscores the practical application of the SECI model in enhancing learning experiences. The integration of interactive elements and real-world applications reflects the Socialization and Externalization phases, facilitating the sharing and articulation of tacit knowledge. This alignment with the SECI model phases is enriched by referencing studies like Tee and Lee ( 2011 ), which explore the model's practical applications in education. Such empirical evidence further validates our findings on the model's impact on learner engagement and feedback, illustrating a comprehensive view of the feedback's alignment with the SECI model phases. Suggestions for clearer documentation and more examples align with the Combination phase, aiming for better structured and systematic knowledge. The call for real-world applications and guest speakers underscores the Internalization phase, where explicit knowledge is transformed into tacit understanding, highlighting the SECI model's comprehensive impact on educational outcomes. Together, these feedback surveys present a comprehensive view of the feedback across the three courses (Data-based decision-making Process, Data-based decision-making Leadership, Innovation and Digitization Management), underscoring the value of practical, interactive learning and the effective use of the SECI model, alongside areas for enhancement in course delivery and content clarity. 3.2.Text analysis The analysis of course materials through K-means clustering yielded four distinct clusters, each representing a concentration of topics, as indicated in Table 1 . This analysis, deriving thematic foci from the clustering of course materials, showcases the curriculum's diversity, covering practical tools, analytical techniques, pedagogical strategies, and case studies (Li et al., 2018 ). Cluster 0 focuses on pedagogical strategies, echoing findings from Tee and Lee ( 2011 ) and Jenkin ( 2013 ), who underscore the importance of innovative teaching strategies and the role of information sources in educational settings. Cluster 1, concentrating on analytical methods, and Cluster 2, dedicated to research methodology and data handling, underscore the curriculum's emphasis on structured educational research. Cluster 3, highlighting real-world analytics applications, aligns with Ibidunni et al. ( 2021 ) exploration of the SECI and LMX theory in enhancing students' preparedness for the workplace. These results illustrate the diverse thematic focuses across the courses, showcasing a comprehensive approach to incorporating data analytics, technology, and pedagogical strategies in the curriculum, aligning with the study's findings on the beneficial impact of such integration on teaching practices and educational outcomes. Table 1 Distinct clusters representing a concentration of topics. Cluster Key Topics 0 Python, conversational AI, teaching methods, career development, lectures, abstract thinking, competence development, familiarity with topics, quizzes 1 Variables, causality, RDD (Regression Discontinuity Design), data points, cutoff points, dashboards, effect analysis, heuristics, hypothesis testing, graph interpretation 2 Datasets, hypothesis formulation, briefing sessions, guiding principles, DBDM (Data-Based Decision Making), data gathering, methodology, research mesh, McKinsey frameworks, data collection 3 Data points, cutoff analysis, RDD, variables in analysis, frequency of usage, Uber case study, cost-effectiveness, creation processes, blockchain technology This study further explores the thematic structure of course materials through advanced text analysis, employing Latent Dirichlet Allocation (LDA) for topic modelling and creating a diagram to map identified themes to the SECI model's phases. After the clustering, we embarked on a comparative document analysis to evaluate the similarities between the course contents, employing three different mathematical approaches: Cosine Similarity, Euclidean Distance Similarity, and Jaccard Similarity. Cosine Similarity calculations revealed notable associations between documents, particularly between those that were thematically coherent. Documents within the same cluster showed higher degrees of similarity, which was expected given their topical alignment. Specifically, the documents grouped in Clusters 0 and 3 demonstrated a higher cosine similarity score, suggesting a closer thematic relationship, perhaps due to shared jargon or overlapping subject matter. Euclidean Distance Similarity, when inverted to form a similarity measure, provided a nuanced understanding of document relatedness. This metric highlighted the differential spacing between documents in a multi-dimensional space, offering a perspective that considered the magnitude of term frequencies. In this analysis, Clusters 1 and 2 showcased the largest distances, alluding to distinctive content that sets them apart from other clusters. Jaccard Similarity, which is sensitive to the size of the document as it measures the proportion of shared terms, provided a more stringent measure of similarity. This binary-based measure underscored the shared vocabulary across documents, revealing an intriguing interplay of commonality and uniqueness within the course material. It was observed that the documents within Clusters 0 and 3 shared a greater proportion of terms, reinforcing the insights gained from the Cosine Similarity analysis. These computational techniques painted a comprehensive picture of the textual landscape of the course materials. While Clusters 0 and 3 shared a significant overlap in terms, indicative of related pedagogical strategies or conceptual frameworks, Clusters 1 and 2 were characterized by their distinctiveness, which could be attributed to specialized content unique to the particular courses they represented. Heatmaps of these similarity measures were visualized, providing a vivid illustration of the inter-document relationships (Fig. 1 ). The heatmaps served as a testament to the thematic richness and diversity within the courses and also flagged potential areas for content integration and inter-course connectivity. The visual analytics further supported the SECI model's phases, underscoring the socialization and externalization in the shared knowledge of Clusters 0 and 3, and the combination and internalization in the more distinct knowledge areas of Clusters 1 and 2. This dual approach aims to elucidate the comprehensive integration of socialization, externalization, combination, and internalization processes within the curriculum. By visually representing these alignments, we can pinpoint both the strengths in facilitating a holistic learning experience and areas ripe for enhancement to deepen the application of the SECI model in educational settings. 4. CONCLUSIONS In line with our ongoing efforts to enhance digital education frameworks, this study builds upon our previous work (Żatuchin, 2024 ) where we explored the integration of the SECI model and digital innovation to advance knowledge transformation in MBA education. Our findings underscore the significance of adopting innovative teaching methodologies and leveraging digital tools to facilitate effective knowledge transfer, thereby enhancing the learning experience and preparing students for professional challenges. The clustering results reveal a balanced integration of theory and application within the course materials, aligning well with the SECI model's phases of socialization, externalization, combination, and internalization. The presence of diverse themes—from pedagogical methods to applied analytics—suggests a comprehensive approach to knowledge transformation, encouraging active engagement and practical application. Our analysis showcases a robust alignment of course content with the SECI model's phases, promoting a dynamic learning environment that encourages practical application. The cultural transmission of tacit knowledge (Miton & DeDeo, 2022 ) and the efficacy of flipped classrooms (Orange et al. 2019 ) highlight the evolving pedagogical strategies essential for modern education. Furthermore, the adaptability and relevance of the SECI model in current educational contexts, as discussed by Wang and Kim ( 2023 ), underscore its potential for continuous evolution to meet the challenges of contemporary education. DECLARATIONS participants consented to participate in course feedback studies. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests: The authors declare that they have no competing interests. Acknowledgements: The author acknowledges students of the MBA faculty of EEUAS and students of the Data-Based Decision Making microdegree at Estonian Business School. REFERENCES Gerami, M. (2010). Knowledge Management.(IJCSIS) International Journal of Computer Science and Information Security , Vol. 7 , No. 2, https://www.arxiv.org/abs/1003.1807 Guo, X., & Xue, Y. (2020). The Professional Education Ecosystem of Industrial Design at Georgia Institute of Technology Based on SECI Model. E3S Web of Conferences , 179 , 02032. https://doi.org/10.1051/e3sconf/202017902032 Guzman, G., & Trivelato, L. F. (2011). Packaging and unpackaging knowledge in mass higher education—A knowledge management perspective. Higher Education , 62 (4), 451–465. https://doi.org/10.1007/s10734-010-9398-3 Ibidunni, A. S., Ibidunni, O. M., Akinbola, O. A., Olokundun, M. A., & Ogunnaike, O. O. (2021). Conceptualizing a teacher–student knowledge exchange perspective: Exploring the tripartite relationships between SECI theory, LMX theory and HEIs’ students’ preparedness for the workplace. Higher Education, Skills and Work-Based Learning , 11 (2), 330–348. https://doi.org/10.1108/HESWBL-02-2020-0029 Jenkin, T. (2013). Extending the 4I Organizational Learning Model: Information Sources, Foraging Processes and Tools. Administrative Sciences , 3 (3), 96–109. https://doi.org/10.3390/admsci3030096 Khousa, E. A., Atif, Y., & Masud, M. M. (2015). A social learning analytics approach to cognitive apprenticeship. Smart Learning Environments , 2 (1), 14. https://doi.org/10.1186/s40561-015-0021-z Li, M., Liu, H., & Zhou, J. (2018). G-SECI model-based knowledge creation for CoPS innovation: The role of grey knowledge. Journal of Knowledge Management , 22 (4), 887–911. https://doi.org/10.1108/JKM-10-2016-0458 Lorenzo, A. C. M., Cabot, P.-L. H., & Navigli, R. (2023). Cross-lingual AMR Aligner: Paying Attention to Cross-Attention (arXiv:2206.07587). arXiv. http://arxiv.org/abs/2206.07587 Miton, H., & DeDeo, S. (2022). The cultural transmission of tacit knowledge. Journal of The Royal Society Interface , 19 (195), 20220238. https://doi.org/10.1098/rsif.2022.0238 Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and Leadership: A Unified Model of Dynamic Knowledge Creation. Long Range Planning , 33 (1), 5–34. https://doi.org/10.1016/S0024-6301(99)00115-6 Orange, E., Quadros-Flores, P., & Ferreira, P. (2019). Who was teaching whom? : Flipping higher education. International Journal of Advanced Engineering Research and Science , 6 (11), 390–398. https://doi.org/10.22161/ijaers.611.61 Pan, I., Mason, L., & Matar, O. (2021). Data-Centric Engineering: Integrating simulation, machine learning and statistics. Challenges and Opportunities (arXiv:2111.06223). arXiv. http://arxiv.org/abs/2111.06223 Songkram, N., & Chootongchai, S. (2020). Effects of pedagogy and information technology utilization on innovation creation by SECI model. Education and Information Technologies , 25 (5), 4297–4315. https://doi.org/10.1007/s10639-020-10150-2 Tee, M. Y., & Lee, S. S. (2011). From socialisation to internalisation: Cultivating technological pedagogical content knowledge through problem-based learning. Australasian Journal of Educational Technology , 27 (1). https://doi.org/10.14742/ajet.984 Wang, J., & Kim, E. (2023). The Development and Validation of an Instrument to Collaborative Teaching Assessment under the Impact of COVID-19 through the SECI Model. Sustainability , 15 (12), 9540. https://doi.org/10.3390/su15129540 Żatuchin, D. (2024). Beyond SECI: Advancing Knowledge Transformation through Digital Innovation in MBA Education, PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-3979942/v1 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3988920","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":275057689,"identity":"adacdf25-c71a-45df-a6bd-357407bce3e4","order_by":0,"name":"Dmitrij Żatuchin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDCCA4wNDAwGbBAOY4MEAz8DAzMhLY0NEC3MEC2SDQS1MICsYYBpAdp4gIAWvmuH2x9XFPAxyLefPybxc4dFnvHx5sMGDBU2OLVI3k5sbDwDdBhjTzKbZO8ZiWKzM8eSExjOpOHUYgDS0gDUwsyQzCbN2CaRuO1GjvEBxrbDhLWw8T+GaNk8//3nA4z//hPWwiMBtWWDBA9zAmPDAbx+mQnUwiMh8djYsheoZcaZNGODhGPJOLXw3U5/8LHhzzE5+f7Ehzd+ttUl9rcffizxocYOpxYoOMaDyk8gpIGBoYawklEwCkbBKBi5AAASpFQDP8oRowAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-1929-9770","institution":"Estonian Entrepreneurship University of Applied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Dmitrij","middleName":"","lastName":"Żatuchin","suffix":""}],"badges":[],"createdAt":"2024-02-25 20:21:30","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3988920/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3988920/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51842421,"identity":"49999925-5736-45f1-a310-73ee20f56803","added_by":"auto","created_at":"2024-03-01 05:33:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92132,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps for clusters - Cosine similarity, Euclidean Distance, Jaccard Similarity.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3988920/v1/fffac2f83034a90307493dc4.png"},{"id":51842422,"identity":"0462c3b5-4ab9-4bdd-a71f-92e1b6d96052","added_by":"auto","created_at":"2024-03-01 05:33:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":204925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3988920/v1/90e39103-ae67-4cc9-bc97-b894966ff2f5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEnhancing Knowledge Transformation in Digital Education: An Analysis of the SECI Model's Application in Course Design and Execution\u003c/p\u003e","fulltext":[{"header":"1.\tINTRODUCTION","content":"\u003cp\u003eIn the evolving landscape of digital education, the SECI model, as elucidated by Nonaka et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), presents a structured framework for the transformation of tacit knowledge into explicit knowledge, thereby facilitating a dynamic learning environment. The model's integration into course design, particularly through the improvisation of problem-based learning (PBL) approaches, enhances knowledge transfer, engagement, and practical application among learners, as demonstrated by Tee and Lee (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, the importance of aligning educational practices with industry demands, highlighted by Khousa et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), underscores the model's applicability in preparing students for professional challenges.\u003c/p\u003e \u003cp\u003eThis study, drawing upon insights from Gerami (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Guo \u0026amp; Xue (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), explores the SECI model's versatility across various educational fields, emphasizing its potential to bridge educational practices with pragmatic professional world demands. By evaluating the SECI model's application in recent course developments, this research aims to uncover effective strategies that resonate with learners and promote deep learning. Despite the well-established theoretical benefits of the SECI model in knowledge management, its empirical examination within the context of digital education, particularly in creating engaging learning environments, remains scant. The facilitation of tacit knowledge sharing is augmented by insights from Tee and Lee (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), alongside Guzman and Trivelato (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), who discuss the complexities of knowledge codification and transfer in higher education.\u003c/p\u003e \u003cp\u003eThis gap is addressed by evaluating course designs and feedback to discern patterns indicative of successful knowledge transformation, thus validating the hypothesis that courses rooted in the SECI model's principles will garner positive feedback from participants and demonstrate effective knowledge transfer.\u003c/p\u003e"},{"header":"2.\tMETHODOLOGY","content":"\u003cp\u003eThis study employs Natural Language Processing (NLP) and text analysis techniques to analyze course materials and participant feedback from 'Innovation and Digitization Management' and 'Data-Based Decision Making' programs, to uncover recurring themes and patterns that correlate with positive learning outcomes. The methodology includes text preprocessing, numerical transformation using TF-IDF, and the application of K-means clustering to reveal shared themes within the course content. Inspired by the efficiency of cross-lingual alignment techniques as discussed by Lorenzo et al.(2023), our approach seeks to extend the analytical depth by considering the nuances of language and terminology used across different educational contexts. The use of K-means clustering further organizes the content into clusters based on similarity, allowing for an in-depth analysis of central terms and prevalent topics, thereby shedding light on effective content preparation strategies. This comprehensive analytical framework, supported by the foundational works of Li et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Pan et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), enables a nuanced understanding of how digital innovation and the SECI model can be synergistically employed to enhance educational outcomes.\u003c/p\u003e"},{"header":"3.\tFINDINGS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3.1. Feedback from participants of courses\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eFeedback from participants underscores the practical application of the SECI model in enhancing learning experiences. The integration of interactive elements and real-world applications reflects the Socialization and Externalization phases, facilitating the sharing and articulation of tacit knowledge. This alignment with the SECI model phases is enriched by referencing studies like Tee and Lee (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which explore the model's practical applications in education. Such empirical evidence further validates our findings on the model's impact on learner engagement and feedback, illustrating a comprehensive view of the feedback's alignment with the SECI model phases.\u003c/p\u003e \u003cp\u003eSuggestions for clearer documentation and more examples align with the Combination phase, aiming for better structured and systematic knowledge. The call for real-world applications and guest speakers underscores the Internalization phase, where explicit knowledge is transformed into tacit understanding, highlighting the SECI model's comprehensive impact on educational outcomes. Together, these feedback surveys present a comprehensive view of the feedback across the three courses (Data-based decision-making Process, Data-based decision-making Leadership, Innovation and Digitization Management), underscoring the value of practical, interactive learning and the effective use of the SECI model, alongside areas for enhancement in course delivery and content clarity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3.2.Text analysis\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe analysis of course materials through K-means clustering yielded four distinct clusters, each representing a concentration of topics, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This analysis, deriving thematic foci from the clustering of course materials, showcases the curriculum's diversity, covering practical tools, analytical techniques, pedagogical strategies, and case studies (Li et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCluster 0 focuses on pedagogical strategies, echoing findings from Tee and Lee (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Jenkin (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), who underscore the importance of innovative teaching strategies and the role of information sources in educational settings. Cluster 1, concentrating on analytical methods, and Cluster 2, dedicated to research methodology and data handling, underscore the curriculum's emphasis on structured educational research. Cluster 3, highlighting real-world analytics applications, aligns with Ibidunni et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) exploration of the SECI and LMX theory in enhancing students' preparedness for the workplace.\u003c/p\u003e \u003cp\u003eThese results illustrate the diverse thematic focuses across the courses, showcasing a comprehensive approach to incorporating data analytics, technology, and pedagogical strategies in the curriculum, aligning with the study's findings on the beneficial impact of such integration on teaching practices and educational outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistinct clusters representing a concentration of topics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Topics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePython, conversational AI, teaching methods, career development, lectures, abstract thinking, competence development, familiarity with topics, quizzes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables, causality, RDD (Regression Discontinuity Design), data points, cutoff points, dashboards, effect analysis, heuristics, hypothesis testing, graph interpretation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDatasets, hypothesis formulation, briefing sessions, guiding principles, DBDM (Data-Based Decision Making), data gathering, methodology, research mesh, McKinsey frameworks, data collection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData points, cutoff analysis, RDD, variables in analysis, frequency of usage, Uber case study, cost-effectiveness, creation processes, blockchain technology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study further explores the thematic structure of course materials through advanced text analysis, employing Latent Dirichlet Allocation (LDA) for topic modelling and creating a diagram to map identified themes to the SECI model's phases. After the clustering, we embarked on a comparative document analysis to evaluate the similarities between the course contents, employing three different mathematical approaches: Cosine Similarity, Euclidean Distance Similarity, and Jaccard Similarity.\u003c/p\u003e \u003cp\u003eCosine Similarity calculations revealed notable associations between documents, particularly between those that were thematically coherent. Documents within the same cluster showed higher degrees of similarity, which was expected given their topical alignment. Specifically, the documents grouped in Clusters 0 and 3 demonstrated a higher cosine similarity score, suggesting a closer thematic relationship, perhaps due to shared jargon or overlapping subject matter.\u003c/p\u003e \u003cp\u003eEuclidean Distance Similarity, when inverted to form a similarity measure, provided a nuanced understanding of document relatedness. This metric highlighted the differential spacing between documents in a multi-dimensional space, offering a perspective that considered the magnitude of term frequencies. In this analysis, Clusters 1 and 2 showcased the largest distances, alluding to distinctive content that sets them apart from other clusters.\u003c/p\u003e \u003cp\u003eJaccard Similarity, which is sensitive to the size of the document as it measures the proportion of shared terms, provided a more stringent measure of similarity. This binary-based measure underscored the shared vocabulary across documents, revealing an intriguing interplay of commonality and uniqueness within the course material. It was observed that the documents within Clusters 0 and 3 shared a greater proportion of terms, reinforcing the insights gained from the Cosine Similarity analysis.\u003c/p\u003e \u003cp\u003eThese computational techniques painted a comprehensive picture of the textual landscape of the course materials. While Clusters 0 and 3 shared a significant overlap in terms, indicative of related pedagogical strategies or conceptual frameworks, Clusters 1 and 2 were characterized by their distinctiveness, which could be attributed to specialized content unique to the particular courses they represented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeatmaps of these similarity measures were visualized, providing a vivid illustration of the inter-document relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The heatmaps served as a testament to the thematic richness and diversity within the courses and also flagged potential areas for content integration and inter-course connectivity. The visual analytics further supported the SECI model's phases, underscoring the socialization and externalization in the shared knowledge of Clusters 0 and 3, and the combination and internalization in the more distinct knowledge areas of Clusters 1 and 2.\u003c/p\u003e \u003cp\u003eThis dual approach aims to elucidate the comprehensive integration of socialization, externalization, combination, and internalization processes within the curriculum. By visually representing these alignments, we can pinpoint both the strengths in facilitating a holistic learning experience and areas ripe for enhancement to deepen the application of the SECI model in educational settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.\tCONCLUSIONS","content":"\u003cp\u003eIn line with our ongoing efforts to enhance digital education frameworks, this study builds upon our previous work (Żatuchin, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) where we explored the integration of the SECI model and digital innovation to advance knowledge transformation in MBA education. Our findings underscore the significance of adopting innovative teaching methodologies and leveraging digital tools to facilitate effective knowledge transfer, thereby enhancing the learning experience and preparing students for professional challenges.\u003c/p\u003e \u003cp\u003eThe clustering results reveal a balanced integration of theory and application within the course materials, aligning well with the SECI model's phases of socialization, externalization, combination, and internalization. The presence of diverse themes\u0026mdash;from pedagogical methods to applied analytics\u0026mdash;suggests a comprehensive approach to knowledge transformation, encouraging active engagement and practical application.\u003c/p\u003e \u003cp\u003eOur analysis showcases a robust alignment of course content with the SECI model's phases, promoting a dynamic learning environment that encourages practical application. The cultural transmission of tacit knowledge (Miton \u0026amp; DeDeo, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the efficacy of flipped classrooms (Orange et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlight the evolving pedagogical strategies essential for modern education. Furthermore, the adaptability and relevance of the SECI model in current educational contexts, as discussed by Wang and Kim (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), underscore its potential for continuous evolution to meet the challenges of contemporary education.\u003c/p\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003eparticipants consented to participate in course feedback studies.\u003c/p\u003e\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eAvailability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCompeting interests: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAcknowledgements: The author acknowledges students of the MBA faculty of EEUAS and students of the Data-Based Decision Making microdegree at Estonian Business School.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"REFERENCES","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eGerami, M. (2010). Knowledge Management.(IJCSIS) \u003cem\u003eInternational Journal of Computer Science and Information Security\u003c/em\u003e, \u003cem\u003eVol. 7\u003c/em\u003e, No. 2, https://www.arxiv.org/abs/1003.1807 \u003c/li\u003e\n\u003cli\u003eGuo, X., \u0026amp; Xue, Y. (2020). The Professional Education Ecosystem of Industrial Design at Georgia Institute of Technology Based on SECI Model. \u003cem\u003eE3S Web of Conferences\u003c/em\u003e, \u003cem\u003e179\u003c/em\u003e, 02032. https://doi.org/10.1051/e3sconf/202017902032\u003c/li\u003e\n\u003cli\u003eGuzman, G., \u0026amp; Trivelato, L. F. (2011). Packaging and unpackaging knowledge in mass higher education\u0026mdash;A knowledge management perspective. \u003cem\u003eHigher Education\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(4), 451\u0026ndash;465. https://doi.org/10.1007/s10734-010-9398-3\u003c/li\u003e\n\u003cli\u003eIbidunni, A. S., Ibidunni, O. M., Akinbola, O. A., Olokundun, M. A., \u0026amp; Ogunnaike, O. O. (2021). Conceptualizing a teacher\u0026ndash;student knowledge exchange perspective: Exploring the tripartite relationships between SECI theory, LMX theory and HEIs\u0026rsquo; students\u0026rsquo; preparedness for the workplace. \u003cem\u003eHigher Education, Skills and Work-Based Learning\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 330\u0026ndash;348. https://doi.org/10.1108/HESWBL-02-2020-0029\u003c/li\u003e\n\u003cli\u003eJenkin, T. (2013). Extending the 4I Organizational Learning Model: Information Sources, Foraging Processes and Tools. \u003cem\u003eAdministrative Sciences\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 96\u0026ndash;109. https://doi.org/10.3390/admsci3030096\u003c/li\u003e\n\u003cli\u003eKhousa, E. A., Atif, Y., \u0026amp; Masud, M. M. (2015). A social learning analytics approach to cognitive apprenticeship. \u003cem\u003eSmart Learning Environments\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 14. https://doi.org/10.1186/s40561-015-0021-z\u003c/li\u003e\n\u003cli\u003eLi, M., Liu, H., \u0026amp; Zhou, J. (2018). G-SECI model-based knowledge creation for CoPS innovation: The role of grey knowledge. \u003cem\u003eJournal of Knowledge Management\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(4), 887\u0026ndash;911. https://doi.org/10.1108/JKM-10-2016-0458\u003c/li\u003e\n\u003cli\u003eLorenzo, A. C. M., Cabot, P.-L. H., \u0026amp; Navigli, R. (2023). \u003cem\u003eCross-lingual AMR Aligner: Paying Attention to Cross-Attention\u003c/em\u003e (arXiv:2206.07587). arXiv. http://arxiv.org/abs/2206.07587\u003c/li\u003e\n\u003cli\u003eMiton, H., \u0026amp; DeDeo, S. (2022). The cultural transmission of tacit knowledge. \u003cem\u003eJournal of The Royal Society Interface\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(195), 20220238. https://doi.org/10.1098/rsif.2022.0238\u003c/li\u003e\n\u003cli\u003eNonaka, I., Toyama, R., \u0026amp; Konno, N. (2000). SECI, Ba and Leadership: A Unified Model of Dynamic Knowledge Creation. \u003cem\u003eLong Range Planning\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 5\u0026ndash;34. https://doi.org/10.1016/S0024-6301(99)00115-6\u003c/li\u003e\n\u003cli\u003eOrange, E., Quadros-Flores, P., \u0026amp; Ferreira, P. (2019). Who was teaching whom? : Flipping higher education. \u003cem\u003eInternational Journal of Advanced Engineering Research and Science\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(11), 390\u0026ndash;398. https://doi.org/10.22161/ijaers.611.61\u003c/li\u003e\n\u003cli\u003ePan, I., Mason, L., \u0026amp; Matar, O. (2021). \u003cem\u003eData-Centric Engineering: Integrating simulation, machine learning and statistics. Challenges and Opportunities\u003c/em\u003e (arXiv:2111.06223). arXiv. http://arxiv.org/abs/2111.06223\u003c/li\u003e\n\u003cli\u003eSongkram, N., \u0026amp; Chootongchai, S. (2020). Effects of pedagogy and information technology utilization on innovation creation by SECI model. \u003cem\u003eEducation and Information Technologies\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(5), 4297\u0026ndash;4315. https://doi.org/10.1007/s10639-020-10150-2\u003c/li\u003e\n\u003cli\u003eTee, M. Y., \u0026amp; Lee, S. S. (2011). From socialisation to internalisation: Cultivating technological pedagogical content knowledge through problem-based learning. \u003cem\u003eAustralasian Journal of Educational Technology\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(1). https://doi.org/10.14742/ajet.984\u003c/li\u003e\n\u003cli\u003eWang, J., \u0026amp; Kim, E. (2023). The Development and Validation of an Instrument to Collaborative Teaching Assessment under the Impact of COVID-19 through the SECI Model. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(12), 9540. https://doi.org/10.3390/su15129540\u003c/li\u003e\n\u003cli\u003eŻatuchin, D. (2024). Beyond SECI: Advancing Knowledge Transformation through Digital Innovation in MBA Education, PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-3979942/v1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SECI model, knowledge transformation, digital education, course design, natural language processing, tacit knowledge, explicit knowledge, learner feedback","lastPublishedDoi":"10.21203/rs.3.rs-3988920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3988920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the application of the SECI model in the design and execution of educational courses in \"Innovation and Digitization Management\" and \"Data-Based Decision Making\" micro-degrees developed in 2023 and 2024. Leveraging Natural Language Processing (NLP) and text analysis, we explore the patterns in course content that correlate with positive learner feedback and effective knowledge transformation. The methodology includes data preprocessing, tokenization, vectorization, and clustering to systematically compare and contrast course elements with learner feedback. Preliminary findings indicate that the integration of the SECI model, emphasizing real-time content sharing and example-based learning, significantly enhances the transfer of knowledge from tacit to explicit forms. This research aims to identify a replicable cadence in content preparation that optimizes learning outcomes.\u003c/p\u003e","manuscriptTitle":"Enhancing Knowledge Transformation in Digital Education: An Analysis of the SECI Model's Application in Course Design and Execution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-01 05:33:31","doi":"10.21203/rs.3.rs-3988920/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd629ee5-1cd4-403a-8ad0-45ba72e849a3","owner":[],"postedDate":"March 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28987626,"name":"Educational Philosophy and Theory"},{"id":28987627,"name":"Information Theory"},{"id":28987628,"name":"Information Retrieval and Management"},{"id":28987629,"name":"Decision Sciences"}],"tags":[],"updatedAt":"2024-03-01T05:33:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-01 05:33:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3988920","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3988920","identity":"rs-3988920","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}