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(single-department vs. interdisciplinary), and network centrality metrics. Funding success
was structured as a categorical target variable based on the funding amount allocated to
each project. To model collaboration as a predictor of funding success, correlation
matrices and predictive visualization modeling were employed. This approach allowed for
the identification of statistical relationships between the complexity of a research team's
interdisciplinary network and their corresponding funding impact classification, thereby
establishing a data-driven link between collaborative practices and resource acquisition.
4. Results and discussions
This section presents findings across eight analytical dimensions, integrating
quantitative results with interpretive discussion. Each subsection corresponds to a
specific visualization technique and addresses distinct aspects of the university research
ecosystem.

4.1. Research themes and keyword patterns

Figure 1. Research keywords wordcloud
Source: Author
The word cloud analysis reveals dominant research themes across the institutional
portfolio. “Mining” emerges as the most prominent term, indicating substantial focus on
data mining, text mining, and knowledge extraction methodologies. “Optimization”
appears with nearly equal frequency, suggesting widespread application of algorithmic
improvement techniques across departments. “AI” and “NLP” (Natural Language
Processing) feature prominently, reflecting institutional strengths in artificial intelligence
and computational linguistics. “Prediction” and “Trends” appear frequently, highlighting
emphasis on forecasting models and pattern recognition. These keyword patterns align
closely with Fourth Industrial Revolution priorities (De Boer et al., 2002), demonstrating
strategic positioning within contemporary technology trends. The strong presence of both
“AI” and “Education” suggests interdisciplinary approaches combining artificial
intelligence with pedagogical applications, consistent with global movements toward
educational data mining and learning analytics (Mueller et al., 2019). This thematic
alignment positions the institution favorably within competitive academic landscapes
where technology-driven research attracts substantial funding and attention.

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