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Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education

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Abstract

Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment, considering two different dimensions: (1) to organize the education process as a social network-based process; and (2) to analyze the students’ interactions in the context of evaluation of the students learning performance. The objective of this paper is to present a new model for students evaluation based on their behavior during the course and its validation in comparison with the traditional model of students’ evaluation. The validation of the new evaluation model is made through an analysis of the correlation between social network analysis measures (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and average tie strength) and the grades obtained by students (grades for quality of work, grades for volume of work, grades for diversity of work, and final grades) in a social network-based engineering education. The main finding is that the obtained correlation results can be used to make the process of the students’ performance evaluation based on students interactions (behavior) analysis, to make the evaluation partially automatic, increasing the objectivity and productivity of teachers and allowing a more scalable process of evaluation. The results also contribute to the behavioural theory of learning performance evaluation. More specific findings related to the correlation analysis are: (1) the more different interactions a student had (degree centrality) and the more frequently the student was between the interaction paths of other students (betweenness centrality), the better was the quality of the work; (2) all five social network measures had a positive and strong correlation with the grade for volume of work and with the final grades; and (3) a student with high average tie strength had a higher grade for diversity of work than those with low ties.

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References

  • Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607.

    Article  Google Scholar 

  • Abbasi, A., Hossain, L., & Leydesdorff, L. (2012). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6(3), 403–412.

    Article  Google Scholar 

  • Ai, J., Zhao, H., Carley, K., Su, Z., & Li, H. (2013). Neighbor vector centrality of complex networks based on neighbors degree distribution. The European Physical Journal B, 86(4), 1–7.

    Article  Google Scholar 

  • Bales, R. (1950). Interaction process analysis: A method for the study of small groups. Cambridge: Addison-Wesley.

    Google Scholar 

  • Barabási, A. (2002). Linked: The new science of networks. New York: Perseus Books.

    Google Scholar 

  • Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555–564.

    Article  Google Scholar 

  • Borgatti, S. (1995). Centrality and AIDS. Connections, 18(1), 112–114.

    Google Scholar 

  • Borgatti, S. (2006). Identifying sets of key players in a social network. Computational and Mathematical Organization Theory, 12(1), 21–34.

    Article  Google Scholar 

  • Borgatti, S., Everett, M., & Freeman, L. (2002). UCINET for windows: Software for social network analysis (version 6). Harvard: Analytic Technologies.

    Google Scholar 

  • Borgatti, S., & Halgin, D. (2011). On network theory. Organization Science, 22(5), 1168–1181.

    Article  Google Scholar 

  • Borgatti, S., & Li, X. (2009). On social network analysis in a supply chain context. Journal of Supply Chain Management, 45(2), 5–22.

    Article  Google Scholar 

  • Cimenler, O., Reeves, K., & Skvoretz, J. (2014). A regression analysis of researchers’ social network metrics on their citation performance in a college of engineering. Journal of Informetrics, 8(3), 667–682.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Daly, A., Moolenaar, N., Bolivar, J., & Burke, P. (2010). Relationships in reform: The role of teachers’ social networks. Journal of Educational Administration, 48(3), 359–391.

    Article  Google Scholar 

  • Dewick, P., & Miozzo, M. (2004). Networks and innovation: Sustainable technologies in Scottish social housing. R&D Management, 34(3), 323–333.

    Article  Google Scholar 

  • Dowdy, S., Wearden, S., & Chilko, D. (2011). Statistics for research. New Jersey: Wiley.

    Google Scholar 

  • Fernandes, S., Flores, M., & Lima, R. (2007). Project-led education in Engineering: Monitoring and assessing the learning process. In Joining forces in engineering education towards excellence—Proceedings of the SEFI and IGIP joint annual conference 2007. Miskolc, Hungary.

  • Fernandes, S., Mesquita, D., Flores, M., & Lima, R. (2014). Engaging students in learning: Findings from a study of project-led education. European Journal of Engineering Education, 39(1), 55–67.

    Article  Google Scholar 

  • Fidalgo, P. (2012). Learning networks and moodle use in online courses: a social network analysis study. Doctoral thesis, New University of Lisbon, Portugal.

  • Fitzpatrick, B., & Lueck, J. (2010). The case against data lock-in. Queue, 8(10), 20.

    Article  Google Scholar 

  • Freeman, L. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.

    Article  Google Scholar 

  • Freeman, L. (2004). The development of social network analysis: A study in the sociology of science. Vancouver: Empirical Press.

    Google Scholar 

  • Giger, E., Pinzger, M., & Gall, H. (2012). Can we predict types of code changes? An empirical analysis. In Proceedings of the 9th IEEE Working conference on mining software repositories (MSR). Zurich, Switzerland.

  • Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  • Hanneman, R., & Riddle, M. (2005). Introduction to social network methods. California: Riverside.

    Google Scholar 

  • Hansen, M. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82–111.

    Article  Google Scholar 

  • Hassan, I., Talib, N., Riaz, A., & Iqbal, M. (2014). Influence of national and engineering culture on team role selection. International Journal of Technology and Design Education, 24(1), 91–105.

    Article  Google Scholar 

  • Hauke, J., & Kossowski, T. (2011). Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones Geographicae, 30(2), 87–93.

    Article  Google Scholar 

  • Hawe, P., Webster, C., & Shiell, A. (2004). A glossary of terms for navigating the field of social network analysis. Journal of Epidemiology and Community Health, 58(12), 971–975.

    Article  Google Scholar 

  • Helle, L., Tynjälä, P., & Olkinuora, E. (2006). Project-based learning in post-secondary education—Theory, practice and rubber sling shots. Higher Education, 51(2), 287–314.

    Article  Google Scholar 

  • Hong, J., Hwang, M., Wong, W., Lin, H., & Yau, C. (2012). Gender differences in social cognitive learning at a technological project design. International Journal of Technology and Design Education, 22(4), 451–472.

    Article  Google Scholar 

  • Jippes, E., Achterkamp, M., Brand, P., Kiewiet, D., Pols, J., & van Engelen, J. (2010). Disseminating educational innovations in health care practice: Training versus social networks. Social Science and Medicine, 70(10), 1509–1517.

    Article  Google Scholar 

  • Keats, D., & Schmidt, J. P. (2007). The genesis and emergence of Education 3.0 in higher education and its potential for Africa. First Monday, 12(3). doi:10.5210/fm.v12i3.1625.

  • Krackhardt, D. (1992). The strength of strong ties: The importance of philos in organizations. In N. Nohria, & R. G. Eccles (Eds.), Networks and organizations: Structure, form, and action (pp. 216–239). Boston: Harvard Business School Press.

  • Laat, M., Lally, V., Lipponen, L., & Simons, R. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis. International Journal of Computer-Supported Collaborative Learning, 2(1), 87–103.

    Article  Google Scholar 

  • Lengel, J. G. (2013). Education 3.0: Seven steps to better schools. New York: Teachers College Press.

    Google Scholar 

  • Levin, D., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management Science, 50(11), 1477–1490.

    Article  Google Scholar 

  • Liccardi, I., Ounnas, A., Pau, R., Massey, E., Kinnunen, P., Lewthwaite, S., et al. (2007). The role of social networks in students’ learning experiences. ACM SIGCSE Bulletin, 39(4), 224–237.

    Article  Google Scholar 

  • Lima, R., Carvalho, D., Assunção Flores, M., & Van Hattum-Janssen, N. (2007). A case study on project led education in engineering: Students’ and teachers’ perceptions. European Journal of Engineering Education, 32(3), 337–347.

    Article  Google Scholar 

  • Lomi, A., Snijders, T., Steglich, C., & Torló, V. (2011). Why are some more peer than others? Evidence from a longitudinal study of social networks and individual academic performance. Social Science Research, 40(6), 1506–1520.

    Article  Google Scholar 

  • Martınez, A., Dimitriadis, Y., Rubia, B., Gómez, E., & De La Fuente, P. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers and Education, 41(4), 353–368.

    Article  Google Scholar 

  • Obreiter, P., & Gräf, G. (2002). Towards scalability in tuple spaces. In Proceedings of the 2002 ACM symposium on applied computing. Madrid, Spain.

  • Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.

    Article  Google Scholar 

  • Pallant, J. (2010). SPSS survival manual: A step by step guide to data analysis using SPSS. Philadelphia: McGraw-Hill International.

    Google Scholar 

  • Palonen, T., & Hakkarainen, K. (2000). Patterns of interaction in computer supported learning: A social network analysis. In Proceedings of the 4th international conference of the learning sciences. Ann Arbor, Michigan, USA.

  • Powell, P. (2004). Assessment of team-based projects in project-led education. European Journal of Engineering Education, 29(2), 221–230.

    Article  Google Scholar 

  • Powell, P., & Weenk, W. (2003). Project-led engineering education. Utrecht: Lemma.

    Google Scholar 

  • Putnik, G., Costa, E., Alves, C., Manupati, V., & Castro, H. (2013). Social network analysis for social network-based projects in project-led education: A case study from an engineering course. In Proceedings of the 2nd international conference on virtual and networked organizations emergent technologies and tools (ViNOrg). Póvoa de Varzim, Portugal.

  • Rienties, B., & Kinchin, I. (2014). Understanding (in) formal learning in an academic development programme: A social network perspective. Teaching and Teacher Education, 39, 123–135.

    Article  Google Scholar 

  • Russo, T., & Koesten, J. (2005). Prestige, centrality, and learning: A social network analysis of an online class. Communication Education, 54(3), 254–261.

    Article  Google Scholar 

  • Tseng, K., Chang, C., Lou, S., & Chen, W. (2013). Attitudes towards science, technology, engineering and mathematics (STEM) in a project-based learning (PjBL) environment. International Journal of Technology and Design Education, 23(1), 87–102.

    Article  Google Scholar 

  • Van den Bossche, P., & Segers, M. (2013). Transfer of training: Adding insight through social network analysis. Educational Research Review, 8, 37–47.

    Article  Google Scholar 

  • van Hattum-Janssen, N., & Vasconcelos, R. (2008). The tutor in project-led education: Evaluation of tutor performance. In Proceedings of the 36th annual SEFI conference. Aalborg, Denmark.

  • Vercellone-Smith, P., Jablokow, K., & Friedel, C. (2012). Characterizing communication networks in a web based classroom: Cognitive styles and linguistic behavior of self-organizing groups in online discussions. Computers and Education, 59(2), 222–235.

    Article  Google Scholar 

  • Vrabič, R., Husejnagić, D., & Butala, P. (2012). Discovering autonomous structures within complex networks of work systems. CIRP Annals-Manufacturing Technology, 61(1), 423–426.

    Article  Google Scholar 

  • Wasserman, S. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Wellman, B., & Berkowitz, S. (1988). Social structures: A network approach. Cambridge: Cambridge University Press.

    Google Scholar 

  • Wu, T. (2014). Using smart mobile devices in social-network-based health education practice: A learning behavior analysis. Nurse Education Today, 34(6), 958–963.

    Article  Google Scholar 

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Acknowledgments

The authors wish to acknowledge the support of the Fundação para a Ciência e Tecnologia (FCT), Portugal, through the Grants “Projeto Estratégico—UI 252—2011–2012” reference PEst-OE/EME/UI0252/2011, “Ph.D. Scholarship Grant” reference SFRH/BD/85672/2012, and the support of Parallel Planes Lda.

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Authors and Affiliations

  1. Department of Production and Systems, University of Minho, 4800-058, Guimarães, Portugal

    Goran Putnik, Eric Costa, Cátia Alves, Leonilde Varela & Vaibhav Shah

  2. ParallelPlanes, Lda., 4715-288, Braga, Portugal

    Hélio Castro

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  1. Goran Putnik
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Correspondence to Eric Costa.

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Putnik, G., Costa, E., Alves, C. et al. Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education. Int J Technol Des Educ 26, 413–437 (2016). https://doi.org/10.1007/s10798-015-9318-z

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