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.