Alumni-Connect: Digital Platform for Centralized Alumni Data Management and Engagement

References

[1] Weerts, D. J., & Ronca, J. M. (2008). Characteristics of engaged alumni in the United States. Research in Higher Education, 49(3), 274-305.

[2] Gaier, S. E. (2005). Alumni satisfaction with their undergraduate academic experience and the impact on alumni giving and participation. International Journal of Educational Advancement, 5(4), 279 -288.

[3] McAlexander, J. H., Koenig, H. F., & Schouten, J. W. (2006). Building relationships of brand community in higher education: A strategic framework for university advancement. International Journal of Educational Advancement, 6(2), 107-118.

[4] Ricci, F., Rokach, L., & Shapira, B. (Eds.). (2015). Recommender Systems Handbook (2nd ed.). Springer.

[5] Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370.

[6] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.

[7] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70.

[8] Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 40(6), 601-618.

[9] Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177-194.

[10] Molnar, C. (2022). Interpretable Machine Learning (2nd ed.). Lulu.com.

[11] Salton, G., & Buckley, C. (1988). Term- weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.

[12] Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.

[13] Burke, R. (2007). Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web (pp. 377-408). Springer.

[14] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734- 749.

[15] Sandhu, R., Coyne, E. J., Feinstein, H. L., & Youman, C. E. (1996). Role-based access control models. IEEE Computer, 29(2), 38-47.

[16] Ferraiolo, D. F., Sandhu, R., Gavrila, S., Kuhn, D. R., & Chandramouli, R. (2001). Proposed NIST standard for role-based access control. ACM Transactions on Information and System Security, 4(3), 224-274.

[17] Rahm, E., & Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Data Engineering Bulletin, 23(4), 3-13.

[18] Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1-52.

[19] Few, S. (2013). Information Dashboard Design: Displaying Data for At-a-Glance Monitoring (2nd ed.). Analytics Press.

[20] Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Proceedings 1996 IEEE Symposium on Visual Languages, 336-343.

@article{1716161,
      author = {N. Nalini Krupa, Y. Venkata Guru Mahesh, S K. Nouman, V. Nikhil Satya, T. Manish Reddy},
      title = {Alumni-Connect: Digital Platform for Centralized Alumni Data Management and Engagement},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {485-494},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716161.pdf},
      abstract = {Alumni engagement and career guidance for students remain critical challenges in technical education institutions. Traditional alumni networks lack structured mechanisms for meaningful student-alumni connections, systematic career mentorship, and data-driven placement prediction. This paper presents Alumni-Connect, an AI-powered engagement platform that integrates machine learning-based mentor matching, career path prediction, and intelligent recommendation systems to bridge the gap between students and alumni professionals. The proposed system employs an XGBoost-based mentor matching algorithm to identify optimal student-alumni pairs with 91% accuracy based on skill overlap, career interests, and expertise domains. A transparent, weighted-feature career prediction model estimates individual placement probability and salary ranges with domain-specific calibration. The platform utilizes TF-IDF vectorization and cosine similarity for intelligent job and mentor recommendations, achieving 89% recommendation relevance in evaluation studies. The system supports role-based access for students, alumni, counsellors, HODs, principals, and administrators, with comprehensive analytics dashboards for institutional performance monitoring. Multi-modal profile management enables rich professional representations including certifications, internships, project portfolios, and social links validated through roll number standardization. Evaluation results demonstrate significant improvements in mentor matching accuracy, recommendation quality, and overall engagement compared to traditional directory-based networking systems.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716161}
  }