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Contrasting Automatic and Manual Group Formation: A Case Study in a Software Engineering Postgraduate Course

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Artificial Intelligence in Education (AIED 2021)

Abstract

This paper proposes the comparison of a group formation approach based on an evolutionary algorithm with a manual approach performed by an instructor with ten years of experience on this task. The groups were created based on the professional, psychological, and experience profile of each student. The results obtained demonstrated the algorithm’s potential, reaching an average similarity of \(83.46\%\) with the groups formed manually by the instructor.

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References

  1. Alqahtani, M., Gauch, S., Salman, O., Ibrahim, M., Al-Saffar, R.: Diverse group formation based on multiple demographic features. arXiv preprint arXiv:2008.03808 (2020)

  2. Ani, Z.C., Yasin, A., Husin, M.Z., Hamid, Z.A.: A method for group formation using genetic algorithm. Int. J. Comput. Sci. Eng. 2(9), 3060–3064 (2010)

    Google Scholar 

  3. Bellhäuser, H., Konert, J., Müller, A., Röpke, R.: Who is the perfect match? effects of algorithmic learning group formation using personality traits. J. Interact. Med. (i-com) 17(1), 65–77 (2018). https://doi.org/10.1515/icom-2018-0004

  4. Contreras, R., Salcedo, P., et al.: Genetic algorithms as a tool for structuring collaborative groups. Natural Comput. 16(2), 1–9 (2016)

    MathSciNet  Google Scholar 

  5. Dillenbourg, P.: Collaborative Learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series. ERIC (1999)

    Google Scholar 

  6. Graf, S., Bekele, R.: Forming heterogeneous groups for intelligent collaborative learning systems with ant colony optimization. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 217–226. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_22

    Chapter  Google Scholar 

  7. Järvelä, S., et al.: Socially shared regulation of learning in CSCL: understanding and prompting individual-and group-level shared regulatory activities. Int. J. Comput.-Supported Collab. Learn. 11(3), 263–280 (2016)

    Article  Google Scholar 

  8. Lin, Y.T., Huang, Y.M., Cheng, S.C.: An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization. Comput. Educ. 55(4), 1483–1493 (2010)

    Article  Google Scholar 

  9. Liu, C.C., Tsai, C.C.: An analysis of peer interaction patterns as discoursed by on-line small group problem-solving activity. Comput. Educ. 50(3), 627–639 (2008)

    Article  Google Scholar 

  10. Miranda, P.B., Mello, R.F., Nascimento, A.C.: A multi-objective optimization approach for the group formation problem. Exp. Syst. Appl. 162, 113828 (2020)

    Article  Google Scholar 

  11. Moreno, J., Ovalle, D.A., Vicari, R.M.: A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Comput. Educ. 58(1), 560–569 (2012)

    Article  Google Scholar 

  12. Müller, A., Bellhäuser, H., Konert, J., Röpke, R.: Effects of group formation on student satisfaction and performance: a field experiment. Small Group Res. 1046496420988592 (2021). https://doi.org/10.1177/1046496420988592

  13. Odo, C., Masthoff, J., Beacham, N.: Group formation for collaborative learning. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11626, pp. 206–212. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23207-8_39

    Chapter  Google Scholar 

  14. Zheng, B., Niiya, M., Warschauer, M.: Wikis and collaborative learning in higher education. Technol. Pedagogy Educ. 24(3), 357–374 (2015)

    Article  Google Scholar 

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Correspondence to Rafael Ferreira Mello .

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Fiorentino, G. et al. (2021). Contrasting Automatic and Manual Group Formation: A Case Study in a Software Engineering Postgraduate Course. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_30

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  • DOI: https://doi.org/10.1007/978-3-030-78270-2_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78269-6

  • Online ISBN: 978-3-030-78270-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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