Skip to main content
SpringerLink
Log in

Expanding students’ social networks via optimized team assignments

  • Original - OR Modeling/Case Study
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

The class social network is a momentous factor when it comes to educational, personal and professional student success as well as achieving course learning outcomes. Students and teachers benefit from expanded network connectivity via augmented engagement, more inclusivity, and efficient diffusion of information. We present a novel method for positively influencing the class social network. We develop an in-class grouping strategy based on optimization and sociocentric network analysis that pragmatically expands the students’ social networks. In contrast to existing routines, our technique focuses on maximizing individual student opportunities to establish new ties. Based on the knowledge of existing connections, our procedure systematically optimizes the overall number of new ties that can be established during a team project. Our data-driven approach is designed for practical use in class. We show that the underlying combinatorial problem of maximizing unrelated intra-team students can be modeled as a bin packing variant. Using an integer programming formulation, we demonstrate the efficient spreadsheet implementation. We discuss model extensions to account for high-density networks, team balancing, and teammate forcing and forbidding, allowing for hybridization using existing grouping techniques. In an empirical study, we provide evidence for the efficacy of our approach using data from 10 industrial engineering classes with 253 students and 77 project teams - in both face-to-face and virtual modes. We demonstrate the impact of our grouping method compared to random-assignment, self-selection, and maximizing existing intra-team ties. We report an impressive 62% increase of ties compared to only 17% when self-assigning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. The template spreadsheet is available from the authors upon request.

  2. www.opensolver.org.

  3. www.gurobi.com.

  4. Computations are performed using the MAPLE Graph Theory package (www.maplesoft.com).

References

  • Albert, F., Koltai, J., & David, B. (2021). The increasing importance of friends: Changes in core discussion network composition in post-communist Hungary between 1997–2015. Social Networks, 66, 139–145.

    Google Scholar 

  • Amara, S., Macedo, J., Bendella, F., & Santos, A. (2016). Group formation in mobile computer supported collaborative learning contexts: A systematic literature review. Educational Technology and Society, 19(2), 258–273.

    Google Scholar 

  • Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., & Leonardi, S. (2012). Online team formation in social networks. In Proceedings of the 21st international conference on world wide web. WWW ’12 (pp. 839–848). Association for Computing Machinery, New York, NY, USA.

  • Bacon, D. R., Stewart, K. A., & Silver, W. S. (1999). Lessons from the best and worst student team experiences: How a teacher can make the difference. Journal of Management Education, 23(5), 467–488.

    Google Scholar 

  • Banchero, M., Robledo, F., Romero, P., Sartor, P., & Servetti, C. (2021). Max-diversity orthogonal regrouping of MBA students using a GRASP/VND heuristic. In Variable neighborhood search: 8th international conference, ICVNS 2021, Abu Dhabi, United Arab Emirates, March 21–25, 2021, Proceedings (pp. 58–70). Berlin, Heidelberg: Springer.

  • Bicard, D. F., Ervin, A., Bicard, S. C., & Baylot-Casey, L. (2012). Differential effects of seating arrangements on disruptive behavior of fifth grade students during independent seatwork. Journal of Applied Behavior Analysis, 45(2), 407–411.

    PubMed  PubMed Central  Google Scholar 

  • Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing Social Networks. London: Sage.

    Google Scholar 

  • Borgnat, P., Fleury, E., Guillaume, J., Magnien, C., Robardet, C., & Scherrer, A. (2007). Evolving networks. In F. Fogelman-Soulié, D. Perrotta, J. Piskorski, & R. Steinberger (Eds.), Mining massive data sets for security - advances in data mining, search, social networks and text mining, and their applications to security, proceedings of the NATO advanced study institute on mining massive data sets for security, Gazzada (Varese), Italy, 10-21 September 2007. NATO Science for Peace and Security Series - D: Information and Communication Security, vol. 19, pp. 198–203. Amsterdam, The Netherlands: IOS Press.

  • Burkard, R., Dell’Amico, M., & Martello, S. (2012). Assignment Problems. Philadelphia: Society for Industrial and Applied Mathematics.

    Google Scholar 

  • Burke, A. (2011). Group work: How to use groups effectively. Journal of Effective Teaching, 11(2), 87–95.

    ADS  Google Scholar 

  • Celik, B. G., & Brunnhoeffer, G. C. (2013). A stepwise multi-assignment spreadsheet model to select student teams. International Journal of Engineering & Technology, 2(3), 247–258.

    Google Scholar 

  • Chapman, K. J., Meuter, M., Toy, D., & Wright, L. (2006). Can’t we pick our own groups? The influence of group selection method on group dynamics and outcomes. Journal of Management Education, 30(4), 557–569.

    Google Scholar 

  • Chen, R.-C. (2012). Grouping optimization based on social relationships. Mathematical Problems in Engineering 2012.

  • Conforti, M., Cornuéjols, G., Zambelli, G., et al. (2014). Integer Programming. Graduate Texts in Mathematics, vol. 271. Springer, Cham, Switzerland.

  • Feichtner, S. B., & Davis, E. A. (1984). Why some groups fail: A survey of students’ experiences with learning groups. Organizational Behavior Teaching Review, 9(4), 58–73.

    Google Scholar 

  • Felder, R. M., & Brent, R. (1994). Cooperative learning in technical courses: Procedures, pitfalls, and payoffs. Technical report, North Carolina State University.

  • Felder, R., Woods, D., Stice, J., & Rugarcia, A. (2000). The future of engineering education: Part 2. Teaching methods that work. Chemical Engineering Education, 34(1), 26–39.

    Google Scholar 

  • Forslund Frykedal, K., & Hammar Chiriac, E. (2018). Student collaboration in group work: Inclusion as participation. International Journal of Disability, Development and Education, 65(2), 183–198.

    Google Scholar 

  • Gallego, M., Laguna, M., Martí, R., & Duarte, A. (2013). Tabu search with strategic oscillation for the maximally diverse grouping problem. Journal of the Operational Research Society, 64(5), 724–734.

    Google Scholar 

  • Gutiérrez, J. H., Astudillo, C. A., Ballesteros-Pérez, P., Mora-Melià, D., & Candia-Véjar, A. (2016). The multiple team formation problem using sociometry. Computers & Operations Research, 75, 150–162.

    MathSciNet  Google Scholar 

  • Holmberg, K. (2019). Formation of student groups with the help of optimisation. Journal of the Operational Research Society, 70(9), 1538–1553.

    Google Scholar 

  • Hübscher, R. (2010). Assigning students to groups using general and context-specific criteria. IEEE Transactions on Learning Technologies, 3(3), 178–189.

    Google Scholar 

  • Johnson, R. T., & Johnson, D. W. (2008). Active learning: Cooperation in the classroom. The Annual Report of Educational Psychology in Japan, 47, 29–30.

    Google Scholar 

  • Johnson, S. D., Suriya, C., Won Yoon, S., Berrett, J. V., & La Fleur, J. (2002). Team development and group processes of virtual learning teams. Computers & Education, 39(4), 379–393.

    Google Scholar 

  • Kargar, M., & An, A. (2011). Discovering top-k teams of experts with/without a leader in social networks. In Proceedings of the 20th ACM international conference on information and knowledge management. CIKM ’11 (pp. 985–994). Association for Computing Machinery, New York, NY, USA.

  • Kitts, J. A., & Leal, D. F. (2021). What is(n’t) a friend? Dimensions of the friendship concept among adolescents. Social Networks, 66, 161–170.

    PubMed  PubMed Central  Google Scholar 

  • Krause, R. W., Huisman, M., Steglich, C., & Snijders, T. (2020). Missing data in cross-sectional networks - an extensive comparison of missing data treatment methods. Social Networks, 62, 99–112.

    Google Scholar 

  • Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1–2), 83–97.

    MathSciNet  Google Scholar 

  • Layton, R. A., Loughry, M. L., Ohland, M. W., & Ricco, G. D. (2010). Design and validation of a web-based system for assigning members to teams using instructor-specified criteria. Advances in Engineering Education, 2(1), 1–28.

    Google Scholar 

  • Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 2–42.

    Google Scholar 

  • Levi, D., Rinzel, L., Cadiz, D., & Cacapit, M. (1998). Effects of education and team projects on student attitudes toward team work. Technical report, California Polytechnic State University, San Luis Obispo.

  • Liccardi, I., Ounnas, A., Pau, R., Massey, E., Kinnunen, P., Lewthwaite, S., Midy, M.-A., & Sarkar, C. (2007). The role of social networks in students’ learning experiences. ACM Sigcse Bulletin, 39(4), 224–237.

    Google Scholar 

  • Marx, A., Fuhrer, U., & Hartig, T. (1999). Effects of classroom seating arrangements on children’s question-asking. Learning Environments Research, 2(3), 249–263.

    Google Scholar 

  • Myers, S. A. (2012). Students’ perceptions of classroom group work as a function of group member selection. Communication Teacher, 26(1), 50–64.

    Google Scholar 

  • Oakley, B. A., Felder, R. M., Brent, R., & Elhajj, I. (2004). Turning student groups into effective teams. Journal of Student Centered Learning, 2(1), 9–34.

    Google Scholar 

  • Oakley, B. A., Hanna, D. M., Kuzmyn, Z., & Felder, R. M. (2007). Best practices involving teamwork in the classroom: Results from a survey of 6435 engineering student respondents. IEEE Transactions on Education, 50(3), 266–272.

    ADS  Google Scholar 

  • Ohland, M. W., Loughry, M. L., Woehr, D. J., Bullard, L. G., Felder, R. M., Finelli, C. J., Layton, R. A., Pomeranz, H. R., & Schmucker, D. G. (2012). The comprehensive assessment of team member effectiveness: Development of a behaviorally anchored rating scale for self-and peer evaluation. Academy of Management Learning & Education, 11(4), 609–630.

    Google Scholar 

  • Palla, G., Barabási, A.-L., & Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136), 664–667.

    ADS  CAS  PubMed  Google Scholar 

  • Palubeckis, G., Ostreika, A., & Rubliauskas, D. (2015). Maximally diverse grouping: An iterated tabu search approach. Journal of the Operational Research Society, 66(4), 579–592.

    Google Scholar 

  • Prince, M. J., & Felder, R. M. (2006). Inductive teaching and learning methods: Definitions, comparisons, and research bases. Journal of Engineering Education, 95(2), 123–138.

    Google Scholar 

  • Proll, L. (1972). A simple method of assigning projects to students. Journal of the Operational Research Society, 23(2), 195–201.

    Google Scholar 

  • Rienties, B., Alcott, P., & Jindal-Snape, D. (2014). To let students self-select or not: That is the question for teachers of culturally diverse groups. Journal of Studies in International Education, 18(1), 64–83.

    Google Scholar 

  • Roeder, T. M., & Saltzman, R. M. (2014). Schedule-based group assignment using constraint programming. INFORMS Transactions on Education, 14(2), 63–72.

    Google Scholar 

  • Romero, C. C. (2009). Cooperative learning instruction and science achievement for secondary and early post-secondary students: A systematic review. PhD thesis, Colorado State University, Fort Collins.

  • Rusticus, S. A., & Justus, B. J. (2019). Comparing student- and teacher-formed teams on group dynamics, satisfaction, and performance. Small Group Research, 50(4), 443–457.

    Google Scholar 

  • Sadykov, R., & Vanderbeck, F. (2013). Bin packing with conflicts: a generic branch-and-price algorithm. INFORMS Journal on Computing, 25(2), 244–255.

    MathSciNet  Google Scholar 

  • Schulz, A. (2021). The balanced maximally diverse grouping problem with block constraints. European Journal of Operational Research, 294, 42–53.

    MathSciNet  Google Scholar 

  • Shepherd, W. G. (1965). Operations research in education. Management Science, 11(4), 13–19.

    Google Scholar 

  • Springer, L., Stanne, M. E., & Donovan, S. S. (1999). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A meta-analysis. Review of Educational Research, 69(1), 21–51.

    Google Scholar 

  • Thanh, T. N., Morgan, M., Butler, M., & Marriott, K. (2019). Perfect match: Facilitating study partner matching. In SIGCSE ’19: Proceedings of the 50th ACM technical symposium on computer science education (pp. 1102–1108).

  • Volker, B., De Cuyper, R., Mollenhorst, G., Dirkzwager, A., van der Laan, P., & Nieuwbeerta, P. (2016). Changes in the social networks of prisoners: A comparison of their networks before and after imprisonment. Social Networks, 47, 47–58.

    Google Scholar 

  • Vörös, A., Boda, Z., Elmer, T., Hoffman, M., Mepham, K., Raabe, I. J., & Stadtfeld, C. (2021). The Swiss StudentLife Study: Investigating the emergence of an undergraduate community through dynamic, multidimensional social network data. Social Networks, 65, 71–84.

    Google Scholar 

  • Webb, N. M. (1997). Assessing students in small collaborative groups. Theory Into Practice, 36(4), 205–213.

    MathSciNet  Google Scholar 

  • Weitz, R., & Lakshminarayanan, S. (1998). An empirical comparison of heuristic methods for creating maximally diverse groups. Journal of the Operational Research Society, 49(6), 635–646.

    Google Scholar 

  • Yamada, T., Kataoka, S., & Watanabe, K. (2002). Heuristic and exact algorithms for the disjunctively constrained knapsack problem. Information Processing Society of Japan Journal, 43(9), 2864–2870.

    MathSciNet  Google Scholar 

  • Zervoudakis, K., Mastrothanasis, K., & Tsafarakis, S. (2019). Forming automatic groups of learners using particle swarm optimization for applications of differentiated instruction. Computer Applications in Engineering Education, 28, 282–292.

    Google Scholar 

Download references

Acknowledgements

We are indebted to Nian Cheng and Jose Macedo for supporting the collection of class social network data and letting us generate project teams in their classes. Furthermore, we thank John Pan for his valuable comments. We are thankful for the reviewers help in improving this paper.

Funding

No funds, grants, or other support was received.

Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Engineering, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA, 93407, USA

    Alessandro Hill

  2. Department of Mechanical Engineering, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA, 93407, USA

    Steffen Peuker

Authors
  1. Alessandro Hill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steffen Peuker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessandro Hill.

Ethics declarations

Financial interests

The authors have no financial or proprietary interests in any material discussed in this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A Example for mathematical formulation

The IP formulation (F) for computing the 3 optimized teams in the example class from Sect. 4.1 is given in an LP file format below. The formulation contains 39 binary variables and 48 constraints. Note that Inequality (3) is split into two inequalities to comply with standard LP notation for solvers.

figure a
Fig. 10
figure 10

The anonymized social network and the optimized grouping of 30 students in class J before (left) and after the term (right), indicated by 7 different group colors

Full size image
Fig. 11
figure 11

The 32 new intra-group ties (left) and the 30 generic new inter-group ties (right) after the term, indicated by 7 different group colors

Full size image

Appendix B Example for social network and grouping effect

An example class social network snapshots from our case study—before and after the team project—with optimized team assignments (Class J, IME 305, Operations Research II) is presented below. The number of ties among the 30 students increased from 57 to 108, as illustrated in Fig. 10. In Fig. 11, the 32 newly created intra-group ties (left) which presumably stem from project interaction, and the 30 new ties that were established outside of the projects (right) are depicted.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hill, A., Peuker, S. Expanding students’ social networks via optimized team assignments. Ann Oper Res 332, 1107–1131 (2024). https://doi.org/10.1007/s10479-023-05492-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-023-05492-2

Keywords

Search

Navigation