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disproportionately large distinction segment, indicating comparable rigor in defining
excellence. The moderate distinction rates combined with solid pass rates and varied
failure rates suggest differentiated student outcomes rather than uniform success or
failure patterns. These outcome patterns provide actionable intelligence for academic
administrators. Departments with elevated failure rates may benefit from enhanced
tutoring services, revised prerequisite structures, or curriculum adjustments. Conversely,
departments with very low failure rates might examine whether standards adequately
challenge high-achieving students. The visualization enables evidence-based discussions
about academic standards, student support, and program quality that transcend
anecdotal impressions (Elena & Lilia, 2018).
5. Conclusions and recommendations
5.1. Theoretical contributions

This study extends bibliometric research traditions by integrating multiple analytical
dimensions within a single institutional context (Ramos-Rincón et al., 2019). While
previous studies have examined collaboration patterns, funding relationships, or
publication trends in isolation, this analysis demonstrates how these elements interact
within university ecosystems. The findings validate theoretical frameworks emphasizing
collaboration as a key driver of research success (Ghani et al., 2022) while revealing
nuanced patterns in how merit-based allocation operates alongside institutional goals for
diversity and stability.
5.2. Practical recommendations
Based on these findings, several recommendations emerge for university research
management:
(1) Enhance collaborative infrastructure: Given the strong collaboration-funding
relationship, institutions should invest in mechanisms facilitating cross-departmental
partnerships. This includes seed funding for exploratory collaborations, shared laboratory
facilities, regular interdisciplinary seminars, and administrative support for multi-
investigator grant applications.

(2) Strengthen patent translation mechanisms: With AI Lab and CS showing strong
patent production, investing in technology transfer infrastructure could enhance
commercialization outcomes. This includes hiring experienced technology transfer
officers, establishing industry partnership programs, and providing faculty training in
intellectual property management.
(3) Leverage thematic strengths strategically: The concentration in AI, data mining,
and optimization represents distinctive institutional competence. Strategic
communication highlighting these strengths can attract aligned faculty, students, and
funding while differentiating the institution in competitive academic markets.

5.3. Concluding remarks
This comprehensive analysis demonstrates the power of combining descriptive
statistics with machine learning visualization to illuminate university research ecosystems.
The findings reveal a well-managed institution balancing disciplinary diversity, merit-
based resource allocation, collaborative emphasis, and stable productivity. The
integration of research and student performance data provides holistic perspective on
institutional functioning, moving beyond traditional siloed analyses that examine teaching
and research independently.

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