Skip to main content
SpringerLink
Log in

Sociograms: An Effective Tool For Decision Making in Social Learning

  • Original research
  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

Abstract

In the last few years, social learning, i.e., learning based on the analysis and discussion of topics by means of social collaborative systems, mainly social network services such as Facebook or Twitter, has acquired a great importance and led many instructors and institutions to deploy courses that include activities to be performed in them. For effective learning, both teachers and learners are required to gain insight into how the interaction takes place and how the learning process evolves over the time. Given that the nature of this kind of learning is inherently social, the Social Network Analysis (SNA) theory is perfectly suitable for this purpose. Therefore, this paper proposes the use of sociograms, SNA representations, to answer many of the questions that both learners and teachers need to know to make the best decisions and act accordingly. Furthermore, several network settings are suggested and the interpretation of the most relevant centrality measures when applied to online social learning is provided. Finally, the usefulness of sociograms is shown by means of the analysis of the activity performed in a MOOC course hosted in OpenMOOC platform.

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. http://ecolearning.eu/

References

  • Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Parada, H., & Munoz-Organero, M. (2014). Delving into participants’ profiles and use of social tools in moocs. IEEE Transactions on Learning Technologies, 7(3), 260–266. https://doi.org/10.1109/TLT.2014.2311807.

    Article  Google Scholar 

  • Arnold, K. E., & Pistilli, M. D. (2012). Course signals at purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, LAK ’12 (pp. 267–270). New York, NY: ACM. https://doi.org/10.1145/2330601.2330666.

  • Bakharia, A., & Dawson, S. (2011). Snapp: A bird’s-eye view of temporal participant interaction. In Proceedings of the 1st international conference on learning analytics and knowledge, LAK ’11 (pp. 168–173). New York, NY: ACM. https://doi.org/10.1145/2090116.2090144.

  • Barr, J., & Gunawardena, A. (2012). Classroom salon: A tool for social collaboration. In Proceedings of the 43rd ACM technical symposium on computer science education, SIGCSE ’12 (pp. 197–202). New York, NY: ACM. https://doi.org/10.1145/2157136.2157196.

  • Bayer, J., Bydzovská, H., Géryk, J., Obšıvac, T., & Popelınskỳ, L. (2012). Predicting drop-out from social behaviour of students. In Proceedings of the 5th international conference on educational data mining (pp. 103–109).

  • Beck, F., Burch, M., Diehl, S., & Weiskopf, D. (2014). The state of the art in visualizing dynamic graphs. In R. Borgo, R. Maciejewski, & I. Viola (Eds.), EuroVis - STARs. The Eurographics Association. https://doi.org/10.2312/eurovisstar.20141174.

  • Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology, 2(1), 113–120. https://doi.org/10.1080/0022250X.1972.9989806.

    Article  Google Scholar 

  • Brewe, E., Kramer, L., & Sawtelle, V. (2012). Investigating student communities with network analysis of interactions in a physics learning center. Physical Review Special Topics-Physics Education Research, 8(1), 010101.

    Article  Google Scholar 

  • Brinton, C. G., Chiang, M., Jain, S., Lam, H., Liu, Z., & Wong, F. M. F. (2014). Learning about social learning in moocs: From statistical analysis to generative model. IEEE Transactions on Learning Technologies, 7(4), 346–359. https://doi.org/10.1109/TLT.2014.2337900.

    Article  Google Scholar 

  • Brown, J. S., & Adler, R. P. (2008). Minds on fire: Open education, the long tail, and learning 2.0. Educause Review, 43(1), 16–32.

    Google Scholar 

  • Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology & Society, 15(3), 3–26.

    Google Scholar 

  • Carley, K. M. (2014). ORA: A toolkit for dynamic network analysis and visualization (pp. 1219–1228). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-6170-8_309.

    Book  Google Scholar 

  • Clow, D. (2014). Data wranglers: Human interpreters to help close the feedback loop. In Proceedings of the fourth international conference on learning analytics and knowledge, LAK ’14 (pp. 49–53). New York, NY: ACM. https://doi.org/10.1145/2567574.2567603.

  • Crespo, P., & Antunes, C. (2012). Social networks analysis for quantifying students’ performance in teamwork. In Proceedings of the 5th international conference on educational data mining (pp. 234–235).

  • Cuéllar, M. P., Delgado, M., & Pegalajar, M. C. (2011). Improving learning management through semantic web and social networks in e-learning environments. Expert Systems with Applications, 38(4), 4181–4189. https://doi.org/10.1016/j.eswa.2010.09.080.

    Article  Google Scholar 

  • Dawson, S. (2010). Seeing the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736–752. https://doi.org/10.1111/j.1467-8535.2009.00970.x.

    Article  Google Scholar 

  • Dawson, S., Tan, J. P. L., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australasian Journal of Educational Technology, 27(6), 924–942.

    Article  Google Scholar 

  • de Lima, M., & Zorrilla, M. (2017). Social networks and the building of learning communities: An experimental study of a social MOOC. The International Review of Research in Open and Distributed Learning 18(1). http://www.irrodl.org/index.php/irrodl/article/view/2630

  • Dingyloudi, F., & Strijbos, J. W. (2018). Just plain peers across social networks: Peer-feedback networks nested in personal and academic networks in higher education. Learning, Culture and Social Interaction, 18, 86–112. https://doi.org/10.1016/j.lcsi.2018.02.002.

    Article  Google Scholar 

  • Dowson, M., & McInerney, D. M. (2003). What do students say about their motivational goals?: Towards a more complex and dynamic perspective on student motivation. Contemporary Educational Psychology, 28(1), 91–113. https://doi.org/10.1016/S0361-476X(02)00010-3.

    Article  Google Scholar 

  • Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Educational Technology & Society, 15, 58–76.

    Google Scholar 

  • Federico, P., Aigner, W., Miksch, S., Windhager, F., & Zenk, L. (2011). A visual analytics approach to dynamic social networks. In Proceedings of the 11th international conference on knowledge management and knowledge technologies, i-KNOW ’11 (pp. 47:1–47:8). New York, NY: ACM. https://doi.org/10.1145/2024288.2024344.

  • Ferguson, R., & Shum, S. B. (2012). Social learning analytics: five approaches. In S. Dawson, C. Haythornthwaite, S. B. Shum, D. Gasevic, & R. Ferguson (Eds.), Second international conference on learning analytics and knowledge, LAK 2012, Vancouver, BC, Canada, 29 April–02 May 2012. ACM (pp. 23–33). https://doi.org/10.1145/2330601.2330616.

  • Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3–5), 75–174.

    Article  Google Scholar 

  • Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.

    Article  Google Scholar 

  • García-Saiz, D., Palazuelos, C., & Zorrilla, M. (2014). Data mining and social network analysis in the educational field: An application for non-expert users. In A. Peña Ayala (Ed.), Educational data mining (Vol. 524, pp. 411–439)., Studies in computational intelligence Berlin: Springer. https://doi.org/10.1007/978-3-319-02738-8-15.

    Chapter  Google Scholar 

  • Garrido, C. M. C., Olazabalaga, I. M., & Ruiz, U. G. (2015). Redes sociales y aprendizaje cooperativo en un mooc. Revista complutense de educación, 26(1), 119–139.

    Google Scholar 

  • Gerstein, G. (2014). Experiences in self-determined learning, chap. In L-M. Blaschke, C. Kenyon, & S. Hase (Eds.), Moving from education 1.0 through education 2.0 towards education 3.0. (pp. 83–99). Scotts Valley: Create Space Independent Publishing Platform.

    Google Scholar 

  • Gómez Aguilar, D., García-Peñalvo, F., & Theron, R. (2014). Visual analytical model for educational data. In: 2014 9th Iberian conference on information systems and technologies (CISTI) (pp. 1–6). https://doi.org/10.1109/CISTI.2014.6877098

  • Hernández-García, A., González-González, I., Jiménez-Zarco, A. I., & Chaparro-Peláez, J. (2015). Applying social learning analytics to message boards in online distance learning: A case study. Computers in Human Behavior, 47, 68–80. https://doi.org/10.1016/j.chb.2014.10.038.

    Article  Google Scholar 

  • Jan, S., & Vlachopoulos, P. (2018). Social network analysis: A framework for identifying communities in higher education online learning. Technology, Knowledge and Learning,. https://doi.org/10.1007/s10758-018-9375-y.

    Article  Google Scholar 

  • Joubert, M., & Wishart, J. (2012). Participatory practices: Lessons learnt from two initiatives using online digital technologies to build knowledge. Computers & Education, 59(1), 110–119.

    Article  Google Scholar 

  • Keim, D., Kohlhammer, J., Ellis, G., & Mansmann, F. (Eds.). (2010). Mastering the information age solving problems with visual analytics. Germany: Eurographics Association.

    Google Scholar 

  • Kitto, K., Lupton, M., Davis, K., & Waters, Z. (2017). Designing for student-facing learning analytics. Australasian Journal of Educational Technology, 33(5), 152–168. 10.14742/ajet.3607.

    Article  Google Scholar 

  • Klovdahl, A., Potterat, J., Woodhouse, D., Muth, J., Muth, S., & Darrow, W. (1994). Social networks and infectious disease: The Colorado springs study. Social Science & Medicine, 38(1), 79–88.

    Article  Google Scholar 

  • Krebs, V. (2002). Mapping networks of terrorist cells. Connections, 24(3), 43–52.

    Google Scholar 

  • Leskovec, J. (2008). Dynamics of large networks. Ph.D. thesis, School of Computer Science, Pittsburgh, PA, USA. AAI3340652

  • Longhi, M. T., Ribeiro Ribeiro, A. C., Rosas, F. W., Machado, L. R., & Behar, P. A. (2018). Social interactions in a virtual learning environment: Development and validation of the social map tool. In V. L. Uskov, R. J. Howlett, & L. C. Jain (Eds.), Smart education and e-learning 2017 (pp. 273–281). Cham: Springer.

    Chapter  Google Scholar 

  • Magogwe, J. M., Ntereke, B., & Phetlhe, K. R. (2014). Facebook and classroom group work: A trial study involving university of Botswana advanced oral presentation students. British Journal of Educational Technology,. https://doi.org/10.1111/bjet.12204.

    Article  Google Scholar 

  • Martínez Maldonado, R., Kay, J., Yacef, K., & Schwendimann, B. (2012). An interactive teacher’s dashboard for monitoring groups in a multi-tabletop learning environment. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Intelligent tutoring systems (Vol. 7315, pp. 482–492)., Lecture notes in computer science Berlin: Springer. https://doi.org/10.1007/978-3-642-30950-2_62.

    Chapter  Google Scholar 

  • McClelland, D. (2009). Human motivation (2nd ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • McNely, B.J., Gestwicki, P.V., Hill, J.H., Parli-Horne, P., & Johnson, E. (2012). Learning analytics for collaborative writing: a prototype and case study. In S. Dawson, C. Haythornthwaite, S. B. Shum, D. Gasevic, & R. Ferguson (Eds.), Second international conference on learning analytics and knowledge, LAK 2012, Vancouver, BC, Canada, 29 April–02 May 2012. ACM (pp. 222–225). https://doi.org/10.1145/2330601.2330616.

  • Moreno, J. (1934). Who shall survive?. Boston: Beacon House.

    Google Scholar 

  • Palazuelos, C., & Zorrilla, M. (2011). FRINGE: A new approach to the detection of overlapping communities in graphs. In: B. Murgante, O. Gervasi, A. Iglesias, D. Taniar, & B. Apduhan (Eds.), Computational science and its applications—ICCSA 2011, lecture notes in computer science (Vol. 6784, pp. 638–653). Springer.

  • Palazuelos, C., García-Saiz, D., & Zorrilla, M. (2013). Social network analysis and data mining: An application to the e-learning context. In International conference on computational collective intelligence technologies and applications.

  • Palazuelos, C., García-Saiz, D., & Zorrilla, M. (2013). Social network analysis and data mining: An application to the e-learning context. In C. Badica, N. Nguyen, & M. Brezovan (Eds.), Computational collective intelligence. Technologies and applications (Vol. 8083, pp. 651–660)., Lecture notes in computer science Berlin: Springer. https://doi.org/10.1007/978-3-642-40495-5_65.

    Chapter  Google Scholar 

  • Pandey, A. (2016). Why you should adopt social learning?. Bengaluru: EI Desigh.

    Google Scholar 

  • Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450.

    Article  Google Scholar 

  • Rabbany, R., Takaffoli, M., & Zaïane, O. (2011). Analyzing participation of students in online courses using social network analysis techniques. In Proceedings of the 4th international conference on educational data mining (pp. 21–30).

  • Rabbany, R., Elatia, S., Takaffoli, M., & Zaïane, O. (2014). Collaborative learning of students in online discussion forums: A social network analysis perspective. In A. Peña Ayala (Ed.), Educational data mining (Vol. 524, pp. 441–466)., Studies in computational intelligence Berlin: Springer. https://doi.org/10.1007/978-3-319-02738-8_16.

    Chapter  Google Scholar 

  • Romero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers & Education, 68, 458–472. https://doi.org/10.1016/j.compedu.2013.06.009.

    Article  Google Scholar 

  • Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 30–32.

    Google Scholar 

  • Silius, K., Miilumäki, T., Huhtamäki, J., Tebest, T., & Meriläinen, J. (2010). Students’ motivations for social media enhanced studying and learning. Knowledge Management & E-Learning: An International Journal, 2, 51–67.

    Google Scholar 

  • Sobieski, C., & Dell’Angelo, T. (2016). Sociograms as a tool for teaching and learning: Discoveries from a teacher research study. The Educational Forum, 80(4), 417–429. https://doi.org/10.1080/00131725.2016.1207734.

    Article  Google Scholar 

  • Sol, J., Beers, P. J., & Wals, A. E. (2013). Social learning in regional innovation networks: trust, commitment and reframing as emergent properties of interaction. Journal of Cleaner Production, 49, 35–43. https://doi.org/10.1016/j.jclepro.2012.07.041.

    Article  Google Scholar 

  • Sundararajan, B. (2010). Emergence of the most knowledgeable other (mko): Social network analysis of chat and bulletin board conversations in a CSCL system. Electronic Journal of E-Learning, 8(2), 191–208.

    Google Scholar 

  • Teasley, S.D., & Whitmer, J. (2017). The impact of student-facing LMS dashboards. Technical report, School of Information, University of Michigan and Director of Analytics - Research Blackboard, Inc.

  • Tobarra, L., Robles-Gómez, A., Ros, S., Hernández, R., & Caminero, A. C. (2014). Analyzing the students’ behavior and relevant topics in virtual learning communities. Computers in Human Behavior, 31, 659–669. https://doi.org/10.1016/j.chb.2013.10.001.

    Article  Google Scholar 

  • Tobarra, L., Robles-Gómez, A., Ros, S., Hernández, R., & Caminero, A. C. (2014). Analyzing the students’ behavior and relevant topics in virtual learning communities. Computers in Human Behavior, 31, 659–669. https://doi.org/10.1016/j.chb.2013.10.001.

    Article  Google Scholar 

  • Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Assche, F., Parra, G., et al. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499–1514. https://doi.org/10.1007/s00779-013-0751-2.

    Article  Google Scholar 

  • Wagner, C. J., & Gonzalez-Howard, M. (2018). Studying discourse as social interaction: The potential of social network analysis for discourse studies. Educational Researcher, 47(6), 375–383. https://doi.org/10.3102/0013189X18777741.

    Article  Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social Network analysis: Methods and applications., Structural analysis in the social sciences Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Watts, D., & Strogatz, S. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  • Weber, Z. A., & Vincent, A. H. (2014). Facebook as a method to promote a mindset of continual learning in an ambulatory care pharmacy elective course. Currents in Pharmacy Teaching and Learning, 6(4), 478–482. https://doi.org/10.1177/1745691612442904.

    Article  Google Scholar 

  • Wenger, E. (2010). Communities of practice and social learning systems: The career of a concept (pp. 179–198). London: Springer. https://doi.org/10.1007/978-1-84996-133-2_11.

    Book  Google Scholar 

  • Wilson, R. E., Gosling, S. D., & Graham, L. T. (2012). A review of facebook research in the social sciences. Perspectives on Psychological Science, 7(3), 203–220. https://doi.org/10.1177/1745691612442904.

    Article  Google Scholar 

  • Zhu, B., Watts, S., & Chen, H. (2010). Visualizing social network concepts. Decision Support Systems, 49(2), 151–161. https://doi.org/10.1016/j.dss.2010.02.001.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Taner Engin, a Turkish Erasmus Student, for his collaboration in the developing of the prototype and the software modules for extracting data from social network services. The research leading to these results has received partial funding from the European Community’s CIP CIP-ICT-PSP-2013-7-621127 - Programme under grant agreement No. 21127 and from Spanish Government under grant TIN2017-86520-C3-3-R B).

Author information

Authors and Affiliations

  1. Dpto. Ingeniería Informática y Electrónica, Universidad de Cantabria, Avda. Los Castros s/n, Santander, Spain

    Marta Zorrilla

  2. Dpto. Educación, Universidad de Cantabria, Avda. Los Castros s/n, Santander, Spain

    Mariana de Lima Silva

Authors
  1. Marta Zorrilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariana de Lima Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marta Zorrilla.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zorrilla, M., de Lima Silva, M. Sociograms: An Effective Tool For Decision Making in Social Learning. Tech Know Learn 24, 659–681 (2019). https://doi.org/10.1007/s10758-019-09416-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-019-09416-7

Keywords

Search

Navigation