Handbook of Learning Analytics
Chapter 2
Handbook of Learning Analytics
First Edition
Computational Methods for the Analysis
of Learning and Knowledge Building
Communities
H. Ulrich Hoppe
Abstract
Learning analytics (LA) features an inherent interest in algorithms and computational methods of analysis. This makes LA an interesting field of study for computer scientists and mathematically inspired researchers. A differentiated view of the different types of approaches is relevant not only for “technologists” but also for the design and interpretation of analytics applications. The “trinity of methods” includes analytics of 1) network structures including actor–actor (social) networks but also actor–artefact networks, 2) processes using methods of sequence analysis, and 3) content using text mining or other techniques of artefact analysis. A summary picture of these approaches and their roots is given. Two recent studies are presented to exemplify challenges and potential benefits of using advanced computational methods that combine different methodological approaches.
Export Citation: Plain Text (APA) BIBTeX RIS
No Supplementary Material Available
Batagelj, V. (2003). Efficient algorithms for citation network analysis. arXiv:cs/0309023v1.
Blei, D. M. (2012). Introduction to probabilistic topic models. Communications of the ACM, 55(4), 77–84.
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895.
Carley, K. M., Columbus, D., & Landwehr, P. (2013). AutoMap user’s guide 2013. Technical Report CMU-ISR-13-105. Pittsburgh, PA: Carnegie Mellon University, Institute for Software Research. www.casos.cs.cmu.edu/publications/papers/CMU-ISR-13-105.pdf.
Daems, O., Erkens, M., Malzahn, N., & Hoppe, H. U. (2014). Using content analysis and domain ontologies to check learners’ understanding of science concepts. Journal of Computers in Education, 1(2–3), 113–131.
De Laat, M., Lally, V., Lipponen, L., & Simons, R. J. (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.
Elkina, M., Fortenbacher, A., & Merceron, A. (2013). The learning analytics application LeMo: Rationals and first results. International Journal of Computing, 12(3), 226–234.
Farooq, U., Schank, P., Harris, A., Fusco, J., & Schlager, M. (2007). Sustaining a community computing infrastructure for online teacher professional development: A case study of designing Tapped In. Computer Supported Cooperative Work, 16(4–5), 397–429.
Ferguson, R., & Shum, S. B. (2012). Social learning analytics: Five approaches. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 23–33). New York: ACM.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3), 75–174.
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C. W., & Tseng, V. S. (2014). SPMF: A Java open-source pattern mining library. Journal of Machine Learning Research, 15(1), 3389–3393.
Fox, A. (2013). From MOOCs to SPOCs. Communications of the ACM, 56(12), 38–40.
Halatchliyski, I., Oeberst, A., Bientzle, M., Bokhorst, F., & van Aalst, J. (2012). Unraveling idea development in discourse trajectories. Proceedings of the 10th International Conference of the Learning Sciences (ICLS ʼ12), 2–6 July 2012, Sydney, Australia (vol. 2, pp. 162–166). International Society of the Learning Sciences.
Halatchliyski, I., Hecking, T., Göhnert, T., & Hoppe, H. U. (2014). Analyzing the path of ideas and activity of contributors in an open learning community. Journal of Learning Analytics, 1(2), 72–93.
Harrer, A., Malzahn, N., Zeini, S., & Hoppe, H. U. (2007). Combining social network analysis with semantic relations to support the evolution of a scientific community. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Proceedings of the 7th International Conference on Computer-Supported Collaborative Learning (CSCL 2007), 16–21 July 2007, New Brunswick, NJ, USA (pp. 270–279). International Society of the Learning Sciences.
Harrer, A., Martínez-Monés, A., & Dimitracopoulou, A. (2009). Users’ data: Collaborative and social analysis. In N. Balacheff, S. Ludvigsen, T. de Jong, A. Lazonder, & S. Barnes (eds.), Technology-enhanced learning: Principles and products (pp. 175–193). Springer Netherlands.
Haythornthwaite, C. (2001). Exploring multiplexity: Social network structures in a computer-supported distance learning class. The Information Society, 17(3), 211–226.
He, W. (2013). Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(1), 90–102.
Hecking, T., Ziebarth, S., & Hoppe, H. U. (2014). Analysis of dynamic resource access patterns in online courses. Journal of Learning Analytics, 1(3), 34–60.
Hoppe, H. U., Erkens, M., Clough, G., Daems, O. & Adams. A. (2013). Using Network Text Analysis to characterise teachers’ and students’ conceptualisations in science domains. In R. K. Vatrapu, P. Reimann, W. Halb, & S. Bull (Eds.), Proceedings of the 2nd International Workshop on Teaching Analytics (IWTA 2013), 8 April 2013, Leuven, Belgium. http://ceur-ws.org/Vol-985/paper7.pdf
Hoppe, H. U., Göhnert, T., Steinert, L., & Charles, C. (2014). A web-based tool for communication flow analysis of online chats. Proceedings of the Workshops at the LAK 2014 Conference co-located with the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA. http://ceur-ws.org/Vol-1137.
Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63.
Hung, J. (2012). Trends of e-learning research from 2000 to 2008: Use of text mining and bibliometrics. British Journal of Educational Technology, 43(1), 5–16.
Krempel, L. (2005). Visualisierung komplexer Strukturen — Grundlagen der Darstellung mehr-dimensionaler Netzwerke. Frankfurt, Germany: Campus-Verlag.
Lehmann, S., Schwartz, M., & Hansen, L. K. (2008). Biclique communities. Physical Review E, 78(1). doi:10.1103/PhysRevE.78.016108
Mühlenbrock, M., & Hoppe, H. U. (1999). Computer supported interaction analysis of group problem solving. Proceedings of the 1999 Conference on Computer Support for Collaborative Learning (CSCL ’99), 12–15 December 1999, Palo Alto, California (pp. 398–405). International Society of the Learning Sciences.
Palla, G., Barabasi, A. L., & Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136), 664–667.
Palla, G., Derenyi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), 814–818.
Poquet, O., & Dawson, S. (2016). Untangling MOOC learner networks. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 208–212). New York: ACM.
Reffay, C., & Chanier, T. (2003). How social network analysis can help to measure cohesion in collaborative distance-learning. In B. Wasson, S. Ludvigsen, & H. U. Hoppe (Eds.), Proceedings of the Conference on Computer Support for Collaborative Learning: Designing for Change in Networked Environments (CSCL 2003), 14–18 June 2003, Bergen, Norway (pp. 343–352). International Society of the Learning Sciences.
Rosé, C., Wang, Y. C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., & Fischer, F. (2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 3(3), 237–271.
Sherin, B. (2013). A computational study of commonsense science: An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences, 22(4), 600–638.
Strijbos, J. W., Martens, R. L., Prins, F. J., & Jochems, W. M. (2006). Content analysis: What are they talking about? Computers & Education, 46(1), 29–48.
Suthers, D. D., Dwyer, N., Medina, R., & Vatraou, R. (2010). A framework for conceptualizing, representing, and analyzing distributed interaction. International Journal of Computer-Supported Collaborative Learning, 5(1), 5–42.
Suthers, D. D., & Desiato, C. (2012). Exposing chat features through analysis of uptake between contributions. Proceedings of the 45th Hawaii International Conference on System Sciences (HICSS-45), 4–7 January 2012, Maui, HI, USA (pp. 3368–3377). IEEE Computer Society.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.
Wise, A. F., Cui, Y., & Vytasek, J. (2016). Bringing order to chaos in MOOC discussion forums with content-related thread identification. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 188–197). New York: ACM.
Wu, T., Khan, F. M., Fisher, T. A., Shuler, L. A., & Pottenger, W. M. (2005). Posting act tagging using transformation-based learning. In T. Y. Lin, S. Ohsuga, C.-J. Liau, X. Hu, & S. Tsumoto (Eds.), Foundations of data mining and knowledge discovery (pp. 321–331). Springer Berlin Heidelberg.
Zeini, S., Göhnert, T., Hecking, T., Krempel, L., & Hoppe, H. U. (2014). The impact of measurement time on subgroup detection in online communities. In F. Can, T. Özyer, & F. Polat (Eds.), State of the art applications of social network analysis (pp. 249–268). Springer International Publishing.
Ziebarth, S., Neubaum G., Kyewski E., Krämer N., Hoppe H. U., & Hecking T. (2015). Resource usage in online courses: Analyzing learner’s active and passive participation patterns. Proceedings of the 11th International Conference on Computer Supported Collaborative Learning (CSCL 2015), 7–11 June 2015, Gothenburg, Sweden (pp. 395–402). International Society of the Learning Sciences.
Zumbach, J., Mühlenbrock, M., Jansen, M., Reimann, P., & Hoppe, H. U. (2002). Multi-dimensional tracking in virtual learning teams: An exploratory study. Proceedings of the Conference on Computer Support for Collaborative Learning: Foundations for a CSCL Community (CSCL 2002), 7–11 January 2002, Boulder, CO, USA (pp. 650–651). International Society of the Learning Sciences.
Title
Computational Methods for the Analysis of Learning and Knowledge Building Communities
Book Title
Handbook of Learning Analytics
Pages
pp. 23-33
Copyright
2017
DOI
10.18608/hla17.002
ISBN
978-0-9952408-0-3
Publisher
Society for Learning Analytics Research
Authors
H. Ulrich Hoppe
Author Affiliations
Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Germany
Editors
Charles Lang1
George Siemens2
Alyssa Wise3
Dragan Gašević4
Editor Affiliations
1. Teachers College, Columbia University, USA
2. LINK Research Lab, University of Texas at Arlington, USA
3. Learning Analytics Research Network, New York University, USA
4. Schools of Education and Informatics, University of Edinburgh, UK