A College Buddy System with Matching Algorithm

Chi Ling Ko

doi:10.32535/jicp.v8i1.4001

A College Buddy System with Matching Algorithm

Chi Ling Ko

Abstract

This study introduces BuddyIN, a mobile application developed to strengthen peer mentorship and collaborative learning among students at INTI International College Penang. The primary objective is to provide an intelligent platform that efficiently matches students with suitable study partners while offering academic support features. The system was developed using the Waterfall methodology, encompassing requirement gathering, system design, implementation, testing, deployment, and maintenance. Key components include the buddy matching algorithm, AI chatbot integration, intuitive user interface design, and extensive system testing to ensure functionality and reliability. The matching algorithm effectively pairs senior students with freshmen based on user-input data, fostering meaningful academic connections. The project successfully produced a fully functional mobile application that promotes productive study partnerships, delivers real-time academic assistance, and enhances students’ learning experiences, time management, and overall academic performance.

Full Text:

PDF

References

Abhis. (2024, May 30). Python | Decision tree implementation. GeeksforGeeks. https://www.geeksforgeeks.org/decision-tree-implementation-python/

Andries, F., & Lengkoan, F. (2023). The importance of students’ perception of online learning during pandemic. International Journal of Applied Business and International Management, 8(2), 294–305. https://doi.org/10.32535/ijabim.v8i2.2475

Aswathisasidharan. (2023, May 30). Support vector machine (SVM) algorithm. GeeksforGeeks. https://www.geeksforgeeks.org/support-vector-machine-algorithm/

Balan, H. R. (2015). Using peer mentoring-buddy system as an intervention strategy to enhance science research skills. Journal of Multidisciplinary Studies, 4(1), 168-185. http://dx.doi.org/10.7828/jmds.v4i1.850

D’Orazio, M. (2021). Distances with mixed type variables, some modified Gower’s coefficients. arXiv. arXiv preprint arXiv:2101.02481.

Eduopinions. (2022, June 2). Why is a buddy system so important at university? Eduopinions. https://www.eduopinions.com/blog/student-life/why-is-a-buddy-system-so-important-at-university/

GeeksforGeeks. (2024a, May 30). K-nearest neighbor (KNN) algorithm. GeeksforGeeks. https://www.geeksforgeeks.org/k-nearest-neighbours/

GeeksforGeeks. (2024b, May 30). Naive Bayes classifiers. GeeksforGeeks. https://www.geeksforgeeks.org/naive-bayes-classifiers/

Global E. (2014). Navigate student [Mobile app]. Google Play. https://play.google.com/store/apps/details?id=com.eab.se&hl=en&gl=US

Han, J., Kamber, M., & Pei, J. (2012). Getting to know your data. In Data mining (pp. 39-82). Elsevier. https://doi.org/10.1016/B978-0-12-381479-1.00002-2

Hero, C. (2015). Course Hero: AI homework help [Mobile app]. Google Play. https://play.google.com/store/apps/details?id=com.coursehero.coursehero&hl=en&gl=US

Holland, J. C., & Pawlikowska, T. (2019). Learning Cardiac Embryology—Which Resources Do Students Use, and Why?. Medical Science Educator, 29(4), 1051-1060. https://doi.org/10.1007/s40670-019-00803-4

Jair, C., García-López, E., Méndez, M., & Fernández, G. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189–215. https://doi.org/10.1016/j.neucom.2019.10.118

Kabasakal, ?., & Soyuer, H. (2021, March). A Jaccard similarity-based model to match stakeholders for collaboration in an industry-driven portal. In Proceedings (Vol. 74, No. 1, p. 15). MDPI. https://doi.org/10.3390/proceedings2021074015

Lehal, M. S. (2017). Comparison of cosine, Euclidean distance and Jaccard distance. Int J Sci Res Sci, Eng Technol (IJSRSET), 3(8), 1376-1381.

Li, Q. N., & Li, T. H. (2020). Research on the application of Naive Bayes and Support Vector Machine algorithm on exercises Classification. In Journal of Physics: Conference Series (Vol. 1437, No. 1, p. 012071). IOP Publishing. https://doi.org/10.1088/1742-6596/1437/1/012071

Marakani, S. (2019). Employee matching using machine learning. (Master’s thesis, Blekinge Institute of Technology).

Naidoo, A., Byles, H., & Kwenaite, S. (2021). Student support and transition through a buddy programme to foster social integration. Journal of Student Affairs in Africa, 9(2), 47-63.

Nilsson, P. A. (2019). The buddy programme: Integration and social support for international students. Journal of Comparative & International Higher Education, 11(Winter), 38–43. https://doi.org/10.32674/jcihe.v11iWinter.1095

Oh, Z. J., Odebunmi, A. T., Tu, G. Y., Teoh, Y. E., Thoe, W. M., Toh, Y. J., & Kee, D. M. H. (2025). The influence of artificial intelligence on customer retention in the Asian e-commerce market. Asia Pacific Journal of Management and Education, 8(1), 170–186. https://doi.org/10.32535/apjme.v8i1.3865

Peling, I. B. A., Arnawan, I. N., Arthawan, I. P. A., & Janardana, I. G. N. (2017). Implementation of data mining to predict period of students study using Naive Bayes algorithm. International Journal of Engineering and Emerging Technology, 2(1), 53-57. https://doi.org/10.24843/IJEET.2017.v02.i01.p11

Quora Inc. (2012). Quora: The knowledge platform [Mobile app]. Google Play. https://play.google.com/store/apps/details?id=com.quora.android&hl=en&gl=US

Rahman, M., Kamal, N., & Abdullah, N. F. (2024). EDT-STACK: A stacking ensemble-based decision trees algorithm for tire tread depth condition classification. Results in Engineering, 22, 102218. https://doi.org/10.1016/j.rineng.2024.102218

Rosenbaum, P. (2001). Observational studies: Overview. In International encyclopedia of the social & behavioral sciences (pp. 10808–10815). Elsevier. https://doi.org/10.1016/B0-08-043076-7/00483-6

StuDocu. (2019). Studocu: Study notes & sharing [Mobile app]. Google Play. https://play.google.com/store/apps/details?id=com.studocu.app&hl=en&gl=US

Suwanda, R., Syahputra, Z., & Zamzami, E. M. (2020, June). Analysis of euclidean distance and manhattan distance in the K-means algorithm for variations number of centroid K. In Journal of Physics: Conference Series (Vol. 1566, No. 1, p. 012058). IOP Publishing. https://doi.org/10.1088/1742-6596/1566/1/012058

Varun. (2020, September 27). Cosine similarity: How does it measure the similarity, maths behind and usage in Python. Towards Data Science. https://towardsdatascience.com/cosine-similarity-how-does-it-measure-the-similarity-maths-behind-and-usage-in-python-50ad30aad7db

Vieira, R. (2023, May 20). Gower distance. ruivieira.dev. https://ruivieira.dev/gower-distance.html

Wahyuni, P. (2022). Antecedents and consequences of peer support. International Journal of Applied Business and International Management, 7(1), 75–95. https://doi.org/10.32535/ijabim.v7i1.1443

Zhang, J., Li, Y., Shen, F., He, Y., Tan, H., & He, Y. (2024). Hierarchical text classification with multi-label contrastive learning and KNN. Neurocomputing, 577, 127323. https://doi.org/10.1016/j.neucom.2024.127323

DOI: https://doi.org/10.32535/jicp.v8i1.4001

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Published by:

AIBPM Publisher

Editorial Office:

JL. Kahuripan No. 9 Hotel Sahid Montana, Malang, Indonesia
Phone:+62 341 366222
Email: journal.jicp@gmail.com
Website:http://ejournal.aibpmjournals.com/index.php/JICP

Supported by: Association of International Business & Professional Management

If you are interested to get the journal subscription you can contact us at admin@aibpm.org.

ISSN 2622-0989 (Print)
ISSN 2621-993X (Online)

DOI:Prefix 10.32535 by CrossREF

Journal of International Conference Proceedings (JICP) INDEXED: