skip to main content
research-article

Grouping Peers Based on Complementary Degree and Social Relationship using Genetic Algorithm

Published:16 October 2018Publication History
Skip Abstract Section

Abstract

The aim of this article is to propose a new innovative grouping approach using the genetic algorithm (GA) to enhance the interaction and collaboration among peers by considering the complementary degree of students’ learning status and their social relationships. In order to validate our approach, experiments were designed with a group of students and the outcomes were tested with an e-Learning system. The auto-grouping mechanism is developed using GA for better learning results, which is justified based on the performance of students tested on the e-Learning system. The outcomes clearly indicate that the proposed approach can generate a high degree of heterogeneous grouping and encourage students to learn better. The technical contribution of this article can be implemented in any massive open online course platforms with thousands of students, with regard to identifying peers for collaborative works.

References

  1. A. F. AbuSeileek. 2012. The effect of computer-assisted cooperative learning methods and group size on the EFL learners’ achievement in communication skills. Computers and Education 58, 1 (2012), 231--239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Abedi and A. Beikverdi. 2012. Rise of massive open online courses. In 4th International Congress Engineering Education (ICEED’12), 1--4.Google ScholarGoogle Scholar
  3. D. Adamson, G. Dyke, H. J. Jang, and C. P. Rosé. 2014. Towards an agile approach to adapting dynamic collaboration support to student needs. In International Journal of AI in Education 24, 1 (2014), 91--121.Google ScholarGoogle Scholar
  4. G. K. Awal and K. K. Bharadwaj. 2014. Team formation in social networks based on collective intelligence—an evolutionary approach. Applied Intelligence 41, 2 (2014), 627--648. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Ballera, I. A. Lukandu, and A. Radwan. 2014. Personalizing e-learning curriculum using: Reversed roulette wheel selection algorithm. In International Conference Education Technologies and Computers (ICETC’14), 91--97.Google ScholarGoogle Scholar
  6. R. C. Chen, S. Y. Chen, J. Y. Fan, and Y. T. Chen. 2012. Grouping partners for cooperative learning using genetic algorithm and social network analysis. In Proceedings of the International Workshop on Information and Electronics Engineering (IWIEE’12), 3888--3893.Google ScholarGoogle Scholar
  7. P. J. Chuang, M. C. Chiang, C. S. Yang, and C. W. Tsai. 2012. Social networks-based adaptive pairing strategy for cooperative learning. Journal of Educational Technology and Society 15, 3 (2012), 226--239.Google ScholarGoogle Scholar
  8. J. Dinardo. 2008. Natural experiments and quasi-natural experiments. The New Palgrave Dictionary of Economics, 856--859.Google ScholarGoogle Scholar
  9. G. Dyke, A. Adamson, I. Howley, and C. P. Rosé. 2013. Enhancing scientific reasoning and discussion with conversational agents. IEEE Transactions on Learning Technologies 6, 3 (2013), 240--247. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. E. Fieldsend. 2017. University staff teaching allocation: Formulating and optimizing a many-objective problem. In ACM Proceedings of the Genetic and Evolutionary Computation Conference, 1097--1104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. T. R. Guskey. 1985. Implementing Mastery Learning. Wadsworth, Belmont, CA.Google ScholarGoogle Scholar
  12. C. E. Hmelo-Silver, C. A. Chinn, A. M. O'Donnell, and C. Chan. 2013. The International Handbook of Collaborative Learning (1st. ed.). Routledge, New York. ISBN: 978-0-415-80573-5(hbk)Google ScholarGoogle Scholar
  13. G. J. Hwang, P. Y. Yin, C. W. Hwang, and C. C. Tsai. 2008. An enhanced genetic approach to composing cooperative learning groups for multiple grouping criteria. Journal of Educational Technology and Society 11, 1 (2008), 148--167.Google ScholarGoogle Scholar
  14. B. Jong, Y. Wu, and T. Chan. 2006. Dynamic grouping strategies based on a conceptual graph for cooperative learning. IEEE Transactions on Knowledge and Data Engineering 18, 6 (2006), 738--747. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. H. Kim and S. Lee. 2013. A peer to peer social networking service exploiting triangle relationship among friends. In 5th International Conference on Ubiquitous and Future Networks (ICUFN’13), 816--821.Google ScholarGoogle Scholar
  16. J. Konert, D. Burlak, and R. Steinmetz. 2014. The group formation problem: An algorithmic approach to learning group formation. In Open Learning and Teaching in Educational Communities, Springer International Publishing, 221--234. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. W. Li, Y. T. Wang, and Y. C. Chang. 2014. The differences between self-organized group and designated group for co-operative learning. In The 7th International Conference on Ubi-Media Computing and Workshops (UMEDIA’14), 254--256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. K. S. Martinez, M. A. Hearst, and A. Fox. 2014. Monitoring MOOCs: Which information sources do instructors value? In The 1st ACM Conference on Learning@ Scale Conference, 79--88. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. I. Matin, S. Jahan, and M. R. Huq. 2014. Community recommendation in social network using strong friends and quasi-clique approach. In International Conference on Electrical and Computer Engineering (ICECE’14), 453--456.Google ScholarGoogle Scholar
  20. R. Malhotra, N. Singh, and Y. Singh. 2011. Genetic algorithms: Concepts, design for optimization of process controllers. Computer and Information Science 4, 2 (2011), 39--54.Google ScholarGoogle ScholarCross RefCross Ref
  21. J. Moreno, D. A. Ovalle, and R. M. Vicari. 2012. A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Computers and Education 58, 1 (2012), 560--569. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. O. N. Pratiwi, I. G. B. B. Nugraha, S. H. Supangkat, and B. Rahardjo. 2013. Web application for Jigsaw-based cooperative learning. In International Conference ICT for Smart Society (ICISS’13), 1--4.Google ScholarGoogle Scholar
  23. L. Qing, T. Yongqin, H. Yongguo, and, W. Yadong. 2013. The research of the knowledge structure optimization for the virtual reality teaching based on genetic algorithm. In 4th International Conference on Digital Manufacturing and Automation (ICDMA’13), 1510--1513.Google ScholarGoogle ScholarCross RefCross Ref
  24. Y. Ren, S. Li, and H. Wen. 2011. Exploring the impact of diversity in heterogeneous groups on teachers’ performance. In International Conference on Electrical and Control Engineering (ICECE’11), 3927--3930.Google ScholarGoogle Scholar
  25. C. P. Rose, P. Goldman, J. Z. Sherer, and L. Resnick. 2015. Supportive technologies for group discussion in MOOCs. Current Issues in Emerging eLearning 2, 1 (2015).Google ScholarGoogle Scholar
  26. H. Sadeghi and A. A. Kardan. 2015. A novel justice-based linear model for optimal learner group formation in computer-supported collaborative learning environments. Computers in Human Behavior 48 (2015), 436--447. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. H. M. Su, T. K. Shih, and Y. H. Chen. 2014. Grouping teammates based on complementary degree and social network analysis using genetic algorithm. In 7th International Conference on Ubi-Media Computing and Workshops (UMEDIA’14), 59--64. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. T. Sinha. 2014. Together we stand, together we fall, together we win: Dynamic team formation in massive open online courses. In 5th International Conference Applications of Digital Information and Web Technologies (ICADIWT’14), 107--112.Google ScholarGoogle ScholarCross RefCross Ref
  29. D. Smullen, J. Gillett, J. Heron, and S. Rahnamayan. 2014. Genetic algorithm with self-adaptive mutation controlled by chromosome similarity. In IEEE Congress Evolutionary Computation (CEC’14), 504--511.Google ScholarGoogle Scholar
  30. I. Srba and M. Bielikova. 2015. Dynamic group formation as an approach to collaborative learning support. IEEE Transactions on Learning Technologies 8, 2 (2015), 173--186.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. K. Tanbeer, F. Jiang, C. S. Leung, R. K. MacKinnon, and I. J. Medina, 2013. Finding groups of friends who are significant across multiple domains in social networks. In 5th International Conference on Computational Aspects of Social Networks (CASoN’13), 21--26.Google ScholarGoogle Scholar
  32. M. R. Ullmann, D. J. Ferreira, C. G. Camilo, S. S. Caetano, and L. de Assis. 2015. Formation of learning groups in cMOOCs using particle swarm optimization. In IEEE Congress on Evolutionary Computation (CEC’15), 3296--3304.Google ScholarGoogle ScholarCross RefCross Ref
  33. Y. C. Yang, C. Y. Wang, C. Y. Huang, and Y. C. Chen. 2013. Pattern generation for mutation analysis using genetic algorithms. In IEEE International Symposium Circuits and Systems (ISCAS’13), 2545--2548.Google ScholarGoogle Scholar
  34. D. Yang, M. Wen, and C. Rose. 2014. Peer influence on attrition in massively open online courses. Educational Data Mining, 405--406.Google ScholarGoogle Scholar
  35. Z. Zheng and N. Pinkwart. 2014a. A discrete particle swarm optimization approach to compose heterogeneous learning groups. In 14th International Conference on Advanced Learning Technologies (ICALT’14), 49--51. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Z. Zheng and N. Pinkwart. 2014b. Dynamic re-composition of learning groups using PSO-based algorithms. Educational Data Mining, 357--358.Google ScholarGoogle Scholar
  37. E. Zamudio, L. S. Berdún, and A. A. Amandi. 2016. Social networks and genetic algorithms to choose committees with independent members. Expert Systems with Applications 43 (2016), 261--270. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Grouping Peers Based on Complementary Degree and Social Relationship using Genetic Algorithm

                Recommendations

                Comments

                Login options

                Check if you have access through your login credentials or your institution to get full access on this article.

                Sign in

                Full Access

                • Published in

                  cover image ACM Transactions on Internet Technology
                  ACM Transactions on Internet Technology  Volume 19, Issue 1
                  Regular Papers, Special Issue on Service Management for IOT and Special Issue on Knowledge-Driven BPM
                  February 2019
                  321 pages
                  ISSN:1533-5399
                  EISSN:1557-6051
                  DOI:10.1145/3283809
                  • Editor:
                  • Ling Liu
                  Issue’s Table of Contents

                  Copyright © 2018 ACM

                  Publisher

                  Association for Computing Machinery

                  New York, NY, United States

                  Publication History

                  • Published: 16 October 2018
                  • Revised: 1 February 2018
                  • Accepted: 1 February 2018
                  • Received: 1 January 2016
                  Published in toit Volume 19, Issue 1

                  Permissions

                  Request permissions about this article.

                  Request Permissions

                  Check for updates

                  Qualifiers

                  • research-article
                  • Research
                  • Refereed

                PDF Format

                View or Download as a PDF file.

                PDF

                eReader

                View online with eReader.

                eReader
                About Cookies On This Site

                We use cookies to ensure that we give you the best experience on our website.

                Learn more

                Got it!