Implementation of Stable Pairing Algorithms for Optimizing Educational Games: A Computational and Pedagogical Perspective

Authors

Keywords:

Gale-Shapley algorithm, stable matching, digital game-based learning, compatibility measures, competitive learning

Abstract

The Gale-Shapley algorithm solves the problem of stable pair formation across various fields including economics, labor markets, biology, computer science, and physics. This study modifies the algorithm to use a single list of participants and calculates compatibility scores using Jaccard similarity coefficients from students' proficiency tests and academic performance. We compared the effectiveness of this modified algorithm by evaluating two groups of students engaged in digital educational games: an experimental group matched by the modified algorithm and a randomly matched control group. The results show that the modified algorithm forms pairs with superior compatibility, consistent performance, and balanced competition. These findings suggest integrating the Gale-Shapley algorithm into educational technologies can enhance learning environments. The results significantly impact educational practices indicating that systematic peer training can improve collaboration, competition, and student engagement.

Downloads

Download data is not yet available.

Author Biographies

Luiz Carlos Pinheiro Junior, University of Campinas (UNICAMP), Campinas, SP, Brazil / Federal Institute of Paraná (IFPR)

Luiz Carlos Pinheiro Junior, a professor and researcher in digital games, artificial intelligence, and education. With a degree in Digital Games Technology from the FATEC Americana and a master's degree in electrical engineering and computing from the University of Campinas (UNICAMP), he is currently pursuing his PhD in Electrical Engineering at the same university. With over 20 years of experience in the Information Technology sector, he transitioned to higher and technical education in 2019. He has taught a diverse range of subjects at institutions such as the Federal Institute of São Paulo and the Federal Institute of Paraná. His research interests revolve around the innovative use of digital games and artificial intelligence as pedagogical tools.

Everton Gomede, University of Campinas (UNICAMP), Campinas, SP, Brazil / University of British Columbia (UBC), Vancouver, BC, Canada

Everton Gomede is a computer scientist and data analyst with over 20 years of expertise developing data-driven decision-making tools. He holds a Postdoctoral and Ph.D. in Electrical Engineering/Computing from UNICAMP, a master’s in computer science from the State University of Londrina, and two MBAs from the Getulio Vargas Foundation. Currently, he is engaged in research at the University of British Columbia and the University of Guelph, focusing on data analysis for agriculture. His professional journey includes roles at prestigious institutions such as FEEC/Unicamp, USP and companies like Vanhack and Sicoob. Everton is passionate about innovation and specializes in leveraging machine learning and data science to transform complex data into actionable insights. He is also a prolific educator and author, contributing extensively to scholarly literature in data science and machine learning.

Leonardo de Souza Mendes, University of Campinas (UNICAMP), Campinas, SP, Brazil

Leonardo de Souza Mendes holds a degree in Electrical Engineering from Gama Filho University (1984), a master’s degree in electrical engineering from the Pontifical Catholic University of Rio de Janeiro (1987), and a doctorate in Electrical Engineering from Syracuse University (1991). He is currently a Full Professor (MS6) at the State University of Campinas and a project reviewer for the Fundação de Amparo à Pesquisa do Estado de São Paulo. His main areas of expertise are smart cities, smart enterprises, and developing cognitive systems for smart grids. He is the Communications Networks Laboratory (LARCOM) Scientific Director at UNICAMP's School of Electrical and Computer Engineering. LARCOM has been working since 2001 on the proposal and development of Municipal Infovias (or open access metropolitan networks - OAMN), digital cities, and the current smart cities. Its main research areas are developing and applying intelligent systems for cities, with applications in education, health, and public management.

References

D. Gale and L. S. Shapley, “College admissions and the stability of marriage,” The American Mathematical Monthly, vol. 69, no. 1, pp. 9–15, Jan. 1962, doi: 10.1080/00029890.1962.11989827.

E. M. Fenoaltea, I. B. Baybusinov, J. Zhao, L. Zhou, and Y.-C. Zhang, “The stable marriage problem: An interdisciplinary review from the physicist’s perspective,” Physics Reports, vol. 917, pp. 1–79, Jun. 2021, doi: 10.1016/j.physrep.2021.03.001.

E. Girard, R. Yusri, A. Abusitta, and E. Aïmeur, “An automated stable personalized partner selection for collaborative privacy education,” International Journal of Integrating Technology in Education, vol. 10, no. 2, pp. 9–22, Jun. 2021, doi: 10.5121/ijite.2021.10202.

R. Yusri, A. Abusitta, and E. Aïmeur, “Teens-online: A game theory-based collaborative platform for privacy education,” International Journal of Artificial Intelligence in Education, vol. 31, no. 4, pp. 726–768, Nov. 2020, doi: 10.1007/s40593-020-00224-0.

J. P. Gee, What video games have to teach us about learning and literacy, 2nd ed. New York, NY, USA: Palgrave Macmillan, 2014.

M. Prensky, Digital game-based learning. New York, NY, USA: McGraw-Hill, 2001.

T. Darvenkumar and W. C. Rajasekaran, “Unlocking the power of online gaming: Exploring its potential as a language and communication tool in the English classroom - a survey,” Studies in Media and Communication, vol. 11, no. 6, p. 197, Jul. 2023, doi: 10.11114/smc.v11i6.6053.

J. L. Plass, B. D. Homer, and C. K. Kinzer, “Foundations of game-based learning,” Educational Psychologist, vol. 50, no. 4, pp. 258–283, Oct. 2015, doi: 10.1080/00461520.2015.1122533.

D. W. Johnson and R. T. Johnson, Learning together and alone: Cooperative, competitive, and individualistic learning. Boston, MA, USA: Pearson, 1999.

R. E. Slavin, “Research on cooperative learning and achievement: What we know, what we need to know,” Contemporary Educational Psychology, vol. 21, no. 1, pp. 43–69, Jan. 1996, doi: 10.1006/ceps.1996.0004.

T. H. Laine and R. S. N. Lindberg, “Designing engaging games for education: A systematic literature review on game motivators and design principles,” IEEE Transactions on Learning Technologies, vol. 13, no. 4, pp. 804–821, Oct. 2020, doi: 10.1109/tlt.2020.3018503.

T. Dorji, “The effect of games simulation in improving secondary students’ academic performance,” International Journal of Social Learning, vol. 3, no. 1, pp. 48–64, Dec. 2022, doi: 10.47134/ijsl.v3i1.147.

R. Damaševičius and T. Sidekerskienė, “Virtual worlds for learning in metaverse: A narrative review,” Sustainability, vol. 16, no. 5, p. 2032, Feb. 2024, doi: 10.3390/su16052032.

J. S. Davis, “Game framework analysis and cognitive learning theory providing a theoretical foundation for efficacy in learning in educational gaming,” International Journal of Learning, Teaching and Educational Research, vol. 19, no. 7, pp. 159–175, Jul. 2020, doi: 10.26803/ijlter.19.7.9.

N. L. A. B. H. Ningsih, “The importance of game-based learning in english learning for young learners in the 21st century,” The Art of Teaching English as a Foreign Language (TATEFL)/the Art of Teaching English as a Foreign Language (TATEFL), vol. 4, no. 1, pp. 25–30, May 2023, doi: 10.36663/tatefl.v4i1.492.

D. B. Clark, E. E. Tanner-Smith, and S. S. Killingsworth, "Digital games, design, and learning," Review of Educational Research, vol. 86, no. 1, pp. 79–122, Mar. 2016, doi: 10.3102/0034654315582065.

J. Krath, L. Schürmann, and H. F. O. Von Korflesch, “Revealing the theoretical basis of gamification: A systematic review and analysis of theory in research on gamification, serious games and game-based learning,” Computers in Human Behavior, vol. 125, p. 106963, Dec. 2021, doi: 10.1016/j.chb.2021.106963.

A. Asadzadeh, H. Shahrokhi, B. Shalchi, Z. Khamnian, and P. Rezaei-Hachesu, “Serious educational games for children: A comprehensive framework,” Heliyon, p. e28108, Mar. 2024, doi: 10.1016/j.heliyon.2024.e28108.

D. Zhao, C. H. Muntean, A. E. Chis, G. Rozinaj, and G.-M. Muntean, “Game-based learning: Enhancing student experience, knowledge gain, and usability in higher education programming courses,” IEEE Transactions on Education, vol. 65, no. 4, pp. 502–513, Nov. 2022, doi: 10.1109/te.2021.3136914.

D. Vlachopoulos and A. Makri, “The effect of games and simulations on higher education: A systematic literature review,” International Journal of Educational Technology in Higher Education, vol. 14, no. 1, Jul. 2017, doi: 10.1186/s41239-017-0062-1.

C. Conati and S. Kardan, “Student modeling: Supporting personalized instruction, from problem solving to exploratory open‐ended activities,” Ai Magazine, vol. 34, no. 3, pp. 13–26, Sep. 2013, doi: 10.1609/aimag.v34i3.2483.

V. I. Danilov, “Review of the theory of stable matchings and contract systems,” Computational Mathematics and Mathematical Physics, vol. 63, no. 3, pp. 466–490, Mar. 2023, doi: 10.1134/s0965542523030065.

M. Csikszentmihalyi, Flow: The psychology of optimal experience. New York, NY, USA: HarperCollins, 2009.

E. L. Deci, Intrinsic Motivation. New York, NY, USA: Springer Science & Business Media, 2012.

Downloads

Published

2024-11-14

How to Cite

Pinheiro Junior, L. C., Gomede, E., & de Souza Mendes, L. (2024). Implementation of Stable Pairing Algorithms for Optimizing Educational Games: A Computational and Pedagogical Perspective. IEEE Latin America Transactions, 22(12), 991–999. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9196

Issue

Vol. 22 No. 12 (2024): Ordinary Issue

Section

Computing