Using Gephi to visualize online course participation: a Social Learning Analytics approach
Article Sidebar
Published
Dec 31, 2014
Main Article Content
Abstract
Online education poses big challenges, especially in distance education, where students must adapt to self-directed learning, teachers need to adopt a different way of delivering educational contents and developing knowledge, and course coordinators have to cope with high student numbers and strict time limits for processing all the information originated in the learning space.
Social learning analytics provides tools and methods for extracting information that is useful for improving the learning process. This case study shows how instructors and course coordinators can use the tool Gephi to generate relevant information that would otherwise be difficult to gain. Analysis of empirical data from a cross-curricular course with 656 students proves the usefulness of Gephi for social learning analytics studies and demonstrates how the tool can provide relevant indicators of student activity and engagement. The study also discusses the potential of social learning analytics for improving online instruction via learning data visualization.
Social learning analytics provides tools and methods for extracting information that is useful for improving the learning process. This case study shows how instructors and course coordinators can use the tool Gephi to generate relevant information that would otherwise be difficult to gain. Analysis of empirical data from a cross-curricular course with 656 students proves the usefulness of Gephi for social learning analytics studies and demonstrates how the tool can provide relevant indicators of student activity and engagement. The study also discusses the potential of social learning analytics for improving online instruction via learning data visualization.
Article Details
Section
Articles - Special Issue
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access)
References
Agudo-Peregrina, Á.F., Iglesias-Pradas, S., Conde-González, M.Á., & Hernández-García, Á. (2013). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.
Alstete, J., & Beutell, N. (2004). Performance indicators in online distance learning courses: a study of management education. Quality Assurance in Education, 12(1), 6-14.
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. Proceedings of the Third International ICWSM Conference, USA, 361-362.
Beaudoin, M. F. (2002). Learning or lurking? Tracking the “invisible” online student. The Internet and Higher Education, 5(2), 147-155.
Buckingham-Shum, S., & Ferguson, R. (2012). Social Learning Analytics. Journal Of Educational Technology & Society, 15(3), 3-26.
Cambridge, D., & Perez-Lopez, K. (2012, April). First steps towards a social learning analytics for online communities of practice for educators. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 69-72). ACM.
Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224-238.
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.
Davies, J., & Graff, M.G. (2005). Performance in e-learning: online participation and student grades. British Journal of Educational Technology, 36(4), 657-663.
Fruchterman, T.M.J., & Reingold, E.M. (1991), Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129-1164.
García-Saiz, D., Palazuelos, C., & Zorrilla, M. (2013). Data Mining and Social Network Analysis in the Educational Field: An Application for Non-expert Users. In Educational Data Mining: Applications and Trends. Berlin, Heidelberg, DE: Springer.
Gómez-Aguilar, D.A., Therón, R., & García-Peñalvo, F. J. (2009). Semantic Spiral Timelines Used as Support for e-Learning. Journal of Universal Computer Science, 15(7), 1526-1545.
Jacomy, M., Heymann, S., Venturini, T., & Bastian, M. (2012). ForceAtlas2, a graph layout algorithm for handy network visualization (forthcoming).
Hrastinski, S. (2008). What is online learner participation? A literature review. Computers & Education, 51(4), 1755-1765.
Long, P., & Siemens, G. Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 31-40.
Macfadyen, L.P., Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599.
Macfadyen, L., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Educational Technology & Society, 15(3), 149-163.
Nurmela, K., Lehtinen, E., & Palonen, T. (1999). Evaluating CSCL Log Files by Social Network Analysis. In C. M. Hoadley and J. Roschelle (Eds.), Proceedings of the Computer Support for Collaborative Learning (CSCL) 1999 Conference (pp. 434-444). Palo Alto, CA: Stanford University.
Rabbany, R., ElAtia, S., Takaffoli, M., & Zaïane, O. R. (2013). Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective. In A. Peña-Ayala (Ed.), Educational Data Mining: Applications and Trends (pp. 1-30). Berlin, Heidelberg, DE: Springer.
Reffay, C., & Chanier, T. (2003). How Social Network Analysis can help to Measure Cohesion in Collaborative Distance-Learning. Designing for Change in Networked Learning Environments: Computer-Supported Collaborative Learning, 2, 343-352.
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.
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.
Wise, A.F., & Hausknecht, S. N. (2013). Learning Analytics for Online Discussions: A Pedagogical Model for Intervention with Embedded and Extracted Analytics. Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 48-56), 8-12 April 2013. Leuven, BE/ New York, NY: ACM.
Alstete, J., & Beutell, N. (2004). Performance indicators in online distance learning courses: a study of management education. Quality Assurance in Education, 12(1), 6-14.
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. Proceedings of the Third International ICWSM Conference, USA, 361-362.
Beaudoin, M. F. (2002). Learning or lurking? Tracking the “invisible” online student. The Internet and Higher Education, 5(2), 147-155.
Buckingham-Shum, S., & Ferguson, R. (2012). Social Learning Analytics. Journal Of Educational Technology & Society, 15(3), 3-26.
Cambridge, D., & Perez-Lopez, K. (2012, April). First steps towards a social learning analytics for online communities of practice for educators. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 69-72). ACM.
Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224-238.
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.
Davies, J., & Graff, M.G. (2005). Performance in e-learning: online participation and student grades. British Journal of Educational Technology, 36(4), 657-663.
Fruchterman, T.M.J., & Reingold, E.M. (1991), Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129-1164.
García-Saiz, D., Palazuelos, C., & Zorrilla, M. (2013). Data Mining and Social Network Analysis in the Educational Field: An Application for Non-expert Users. In Educational Data Mining: Applications and Trends. Berlin, Heidelberg, DE: Springer.
Gómez-Aguilar, D.A., Therón, R., & García-Peñalvo, F. J. (2009). Semantic Spiral Timelines Used as Support for e-Learning. Journal of Universal Computer Science, 15(7), 1526-1545.
Jacomy, M., Heymann, S., Venturini, T., & Bastian, M. (2012). ForceAtlas2, a graph layout algorithm for handy network visualization (forthcoming).
Hrastinski, S. (2008). What is online learner participation? A literature review. Computers & Education, 51(4), 1755-1765.
Long, P., & Siemens, G. Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 31-40.
Macfadyen, L.P., Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599.
Macfadyen, L., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Educational Technology & Society, 15(3), 149-163.
Nurmela, K., Lehtinen, E., & Palonen, T. (1999). Evaluating CSCL Log Files by Social Network Analysis. In C. M. Hoadley and J. Roschelle (Eds.), Proceedings of the Computer Support for Collaborative Learning (CSCL) 1999 Conference (pp. 434-444). Palo Alto, CA: Stanford University.
Rabbany, R., ElAtia, S., Takaffoli, M., & Zaïane, O. R. (2013). Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective. In A. Peña-Ayala (Ed.), Educational Data Mining: Applications and Trends (pp. 1-30). Berlin, Heidelberg, DE: Springer.
Reffay, C., & Chanier, T. (2003). How Social Network Analysis can help to Measure Cohesion in Collaborative Distance-Learning. Designing for Change in Networked Learning Environments: Computer-Supported Collaborative Learning, 2, 343-352.
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.
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.
Wise, A.F., & Hausknecht, S. N. (2013). Learning Analytics for Online Discussions: A Pedagogical Model for Intervention with Embedded and Extracted Analytics. Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 48-56), 8-12 April 2013. Leuven, BE/ New York, NY: ACM.