Handbook of Learning Analytics
Chapter 9
Handbook of Learning Analytics
First Edition
Discourse Analytics
Carolyn Penstein Rosé
Abstract
This chapter introduces the area of discourse analytics (DA). Discourse analytics has its impact in multiple areas, including offering analytic lenses to support research, enabling formative and summative assessment, enabling of dynamic and context sensitive triggering of interventions to improve the effectiveness of learning activities, and provision of reflection tools such as reports and feedback after learning activities in support of both learning and instruction. The purpose of this chapter is to encourage both an appropriate level of hope and an appropriate level of skepticism for what is possible while also exposing the reader to the breadth of expertise needed to do meaningful work in this area. It is not the goal to impart the needed expertise. Instead, the goal is for the reader to find his or her place within this scope to discern what kinds of collaborators to seek in order to form a team that encompasses sufficient breadth. We begin with a definition of the field, casting a broad net both theoretically and methodologically, explore both representational and algorithmic dimensions, and conclude with suggestions for next steps for readers who are interested in delving deeper.
Export Citation: Plain Text (APA) BIBTeX RIS
No Supplementary Material Available
Adamopoulos, P. (2013). What makes a great MOOC? An interdisciplinary analysis of student retention in online courses. Proceedings of the 34th International Conference on Information Systems: Reshaping Society through Information Systems Design (ICIS 2013), 15–18 December 2013, Milan, Italy. http://aisel.aisnet.org/icis2013/proceedings/BreakthroughIdeas/13/
Allen, L., Snow, E., & McNamera, D. (2015). Are you reading my mind? Modeling students’ reading comprehension skills with natural language processing techniques. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 246–254). New York: ACM.
Biber, D., & Conrad, S. (2011). Register, Genre, and Style. Cambridge, UK: Cambridge University Press.
Blei, D., Ng, A., & Jordan, M. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Buckingham Shum, S. (2013). Proceedings of the 1st International Workshop on Discourse-Centric Learning Analytics (DCLA13), 8 April 2013, Leuven, Belgium.
Buckingham Shum, S., de Laat, M., de Liddo, A., Ferguson, R., & Whitelock, D. (2014). Proceedings of the 2nd International Workshop on Discourse-Centric Learning Analytics (DCLA14), 24 March 2014, Indianapolis, IN, USA.
Chen, B., Chen, X., & Xing, W. (2015). “Twitter archaeology” of Learning Analytics and Knowledge conferences. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 340–349). New York: ACM.
Collins, L., & Lanza, S. T. (2010). Latent class and latent transition analysis with applications in the social, behavioral, and health sciences. Wiley.
Dascalu, M., Dessus, P., & McNamera, D. (2015). Discourse cohesion: A signature of collaboration. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 350–354). New York: ACM.
Erkens, G., & Janssen, J. (2008). Automatic coding of dialogue acts in collaboration protocols. International Journal of Computer-Supported Collaborative Learning, 3, 447–470.
Ezen-Can, A., Boyer, K., Kellog, S., & Booth, S. (2015). Unsupervised modeling for understanding MOOC discussion forums: A learning analytics approach. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 146–150). New York: ACM.
Foltz, P. (1996). Latent semantic analysis for text-based research. Behavior Research Methods, Instruments, & Computers, 28(2), 197–202.
Garson, G. D. (2013). Factor Analysis. Asheboro, NC: Statistical Associates Publishing. http://www.statisticalassociates.com/factoranalysis.htm
Gianfortoni, P., Adamson, D., & Rosé, C. P. (2011). Modeling stylistic variation in social media with stretchy patterns. Proceedings of the 1st Workshop on Algorithms and Resources for Modeling of Dialects and Language Varieties (DIALECTS ’11), 31 July 2011, Edinburgh, Scotland (pp. 49–59). Association for Computational Linguistics.
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101, 5228–5235.
Gweon, G., Jain, M., McDonough, J., Raj, B., & Rosé, C. P. (2013). Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation. International Journal of Computer Supported Collaborative Learning, 8(2), 245–265.
Gweon, G., Agarwal, P., Udani, M., Raj, B., & Rosé, C. P. (2011). The automatic assessment of knowledge integration processes in project teams. Proceedings of the 9th International Conference on Computer-Supported Collaborative Learning, Volume 1: Long Papers (CSCL 2011), 4–8 July 2011, Hong Kong, China (pp. 462–469). International Society of the Learning Sciences.
Hmelo-Silver, C., Chinn, C., Chan, C., & O’Donnell, A. (2013). The International Handbook of Collaborative Learning. Routledge.
Hsiao, I., & Awasthi, P. (2015). Topic facet modeling: Semantic and visual analytics for online discussion forums. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 231–235). New York: ACM.
Jackson, P., & Moulinier, I. (2007). Natural language processing for online applications: Text retrieval, extraction, and categorization. Amsterdam: John Benjamins Publishing Company.
Jo, Y., Loghmanpour, N., & Rosé, C. P. (2015). Time series analysis of nursing notes for mortality prediction via state transition topic models. Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM ’15), 19–23 October 2015, Melbourne, VIC, Australia (pp. 1171–1180). New York: ACM.
Joksimović, S., Kovanović, V., Jovanović, J., Zouaq, A., Gašević, D., & Hatala, M. (2015). What do cMOOC participants talk about in social media? A topic analysis of discourse in a cMOOC. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 156–165). New York: ACM.
Jurafsky, D., & Martin, J. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Pearson.
Knight, S., & Littleton, K. (2015). Developing a multiple-document-processing performance assessment for epistemic literacy. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 241–245). New York: ACM.
Kumar, R., Rosé, C. P., Wang, Y. C., Joshi, M., & Robinson, A. (2007). Tutorial dialogue as adaptive collaborative learning support. Proceedings of the 13th International Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work (AIED 2007), 9–13 July 2007, Los Angeles, CA, USA (pp. 383–390). IOS Press.
Levinson, S. (1983). Conversational structure. Pragmatics (pp. 284–286). Cambridge, UK: Cambridge University Press.
Loehlin, J. C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis. Routledge.
Manning, C., & Schuetze, H. (1999). Foundations of statistical natural language processing. MIT Press.
Martin, J., & Rose, D. (2003). Working with discourse: Meaning beyond the clause. Continuum.
Martin, J., & White, P. (2005). The language of evaluation: Appraisal in English. Palgrave.
Mayfield, E., & Rosé, C. P. (2013). LightSIDE: Open source machine learning for text accessible to non-experts. In M. D. Shermis & J. Burstein (Eds.), Handbook of Automated Essay Grading: Current Applications and New Directions (pp. 124–135). Routledge.
McLaren, B., Scheuer, O., De Laat, M., Hever, R., de Groot, R., & Rosé, C. P. (2007). Using machine learning techniques to analyze and support mediation of student E-discussions. Proceedings of the 13th International Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (AIED 2007), 9–13 July 2007, Los Angeles, CA, USA (pp. 331–338). IOS Press.
McNamara, D. S., & Graesser, A. C. (2012). Coh-Metrix: An automated tool for theoretical and applied natural language processing. In P. M. McCarthy & C. Boonthum (Eds.), Applied Natural Language Processing: Identification, Investigation, and Resolution (pp. 188–205). Hershey, PA: IGI Global.
Milligan, S. (2015). Crowd-sourced learning in MOOCs: Learning analytics meets measurement theory. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 151–155). New York: ACM.
Molenaar, I., & Chiu, M. (2015). Effects of sequences of socially regulated learning on group performance. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 236–240). New York: ACM.
Morrow, R. A., & Brown, D. D. (1994). Deconstructing the conventional discourse of methodology: Quantitative versus qualitative methods. In R. A. Morrow & D. D. Brown (Eds.), Critical theory and methodology: Contemporary social theory, Vol. 3 (pp. 199–225). Thousand Oaks, CA: Sage.
Mu, J., Stegmann, K., Mayfield, E., Rosé, C. P., & Fischer, F. (2012). The ACODEA framework: Developing segmentation and classification schemes for fully automatic analysis of online discussions. International Journal of Computer-Supported Collaborative Learning, 138, 285–305.
Nehm, R., Ha, M., & Mayfeld, E. (2012). Transforming biology assessment with machine learning: Automated scoring of written evolutionary explanations. Journal of Science Education and Technology, 21, 183–196.
Nguyen, D., Dogruöz, A. S., Rosé, C. P., & de Jong, F. (in press). Computational sociolinguistics: A survey. Computational Linguistics.
O’Donnell, A., & King, A. (1999). Cognitive perspectives on peer learning. Routledge.
O’Grady, W., Archibald, J., Aronoff, M., & Rees-Miller, J. (2009). Contemporary linguistics: An introduction. Boston/New York: Bedford/St. Martins.
Page, E. B. (1966). The imminence of grading essays by computer. Phi Delta Kappan, 48, 238–243.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
Ramesh, A., Goldwasser, D., Huang, B., Daumé III, H., & Getoor, L. (2013). Modeling learner engagement in MOOCs using probabilistic soft logic. NIPS Workshop on Data Driven Education: Advances in Neural Information Processing Systems (NIPS-DDE 2013), 9 December 2013, Lake Tahoe, NV, USA. https://www.umiacs.umd.edu/~hal/docs/daume13engagementmooc.pdf
Ribeiro, B. T. (2006). Footing, positioning, voice: Are we talking about the same things? In A. De Fina, D. Schiffrin, & M. Bamberg (Eds.), Discourse and identity (pp. 48–82). New York: Cambridge University Press.
Rosé, C. P., & Tovares, A. (2015). What sociolinguistics and machine learning have to say to one another about interaction analysis. In L. Resnick, C. Asterhan, & S. Clarke (Eds.), Socializing intelligence through academic talk and dialogue. Washington, DC: American Educational Research Association.
Rosé, C. P., & VanLehn, K. (2005). An evaluation of a hybrid language understanding approach for robust selection of tutoring goals. International Journal of Artificial Intelligence in Education, 15, 325–355.
Rosé, C. P., 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.
Sekiya, T., Marsuda, Y., & Yamaguchi, K. (2015). Curriculum analysis of CS departments based on CS2013 by simplified, supervised LDA. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 330–339). New York: ACM.
Shermis, M. D., & Burstein, J. (2013). Handbook of Automated Essay Evaluation: Current Applications and New Directions. New York: Routledge.
Shermis, M., & Hammer, B. (2012). Contrasting state-of-the-art automated scoring of essays: Analysis. Annual National Council on Measurement in Education Meeting, 14–16.
Simsek, D., Sandor, A., & Buckingham Shum, S. (2015). Correlations between automated rhetorical analysis and tutor’s grades on student essays. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 355–359). New York: ACM.
Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multi-level, longitudinal, and structural equation models. Chapman & Hall/CRC.
Snow, E., Allen, L., Jacovina, M., Perret, C., & McNamera, D. (2015). You’ve got style: Writing flexibility across time. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 194–202). New York: ACM.
Soller, A., & Lesgold, A. (2007). Modeling the process of collaborative learning. In H. U. Hoppe, H. Ogata, & A. Soller (Eds.), The role of technology in CSCL: Studies in technology enhanced collaborative learning (pp 63–86). Springer. doi:10.1007/978-0-387-71136-2_5
Wen, M., Yang, D., & Rosé, C. P. (2014a). Sentiment analysis in MOOC discussion forums: What does it tell us? In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (EDM2014), 4–7 July, London, UK. International Educational Data Mining Society. https://www.researchgate.net/publication/264080975_Sentiment_analysis_in_MOOC_discussion_forums_What_does_it_tell_us
Wen, M., Yang, D., & Rosé, C. P. (2014b). Linguistic reflections of student engagement in massive open online courses. Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM ’14), 1–4 June 2014, Ann Arbor, Michigan, USA. Palo Alto, CA: AAAI Press. http://www.cs.cmu.edu/~mwen/papers/icwsm2014-camera-ready.pdf
Witten, I. H., Frank, E., & Hall, M. (2011). Data mining: Practical machine learning tools and techniques, 3rd ed. San Francisco, CA: Elsevier.
Title
Discourse Analytics
Book Title
Handbook of Learning Analytics
Pages
pp. 105-114
Copyright
2017
DOI
10.18608/hla17.009
ISBN
978-0-9952408-0-3
Publisher
Society for Learning Analytics Research
Authors
Carolyn Penstein Rosé
Author Affiliations
Language Technologies Institute and Human–Computer Interaction Institute, Carnegie Mellon University, USA
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