Technology Support for Discussion Based Learning: From Computer Supported Collaborative Learning to the Future of Massive Open Online Courses

Article

Abstract

This article offers a vision for technology supported collaborative and discussion-based learning at scale. It begins with historical work in the area of tutorial dialogue systems. It traces the history of that area of the field of Artificial Intelligence in Education as it has made an impact on the field of Computer-Supported Collaborative Learning through the creation of forms of dynamic support for collaborative learning, and makes an argument for the importance of advances in the field of Language Technologies for this work. In particular, this support has been enabled by an integration of text mining and conversational agents to form a novel type of micro-script support for productive discussion processes. This research from the early part of the century has paved the way for emerging technologies that support discussion-based learning at scale in Massive Open Online Courses (MOOCs). In the next 25 years, we expect to see this early, emerging work in MOOC contexts grow into ubiquitously available social learning approaches in free online learning environments like MOOCs, or what comes next in the online learning space. These ambitious social learning approaches include Problem Based Learning, Team Project Based Learning, and Collaborative Reflection. We expect to see the capability of drawing in and effectively supporting learners of all walks of life, especially impacting currently under-served learners. To that end, we describe the current exploratory efforts to deploy technology supported collaborative and discussion-based learning in MOOCs and offer a vision for work going forward into the next decade, where we envision learning communities and open collaborative work communities coming together as persistent technology supported and enhanced communities of practice.

Keywords

Conversational agents Computer-supported collaborative learning Massive open online courses 

References

  1. Adamson, D., Dyke, G., Jang, H. J., & Rosé, C. P. (2014). Towards an agile approach to adapting dynamic collaboration support to student needs. International Journal of AI in Education, 24(1), 91–121.Google Scholar
  2. Aleven, V., Koedinger, K. R., & Popescu, O. (2003). A Tutorial Dialogue System to Support Self- Explanation: Evaluation and Open Questions. In U. Hoppe, F. Verdejo, & J. Kay (Eds.), Proceedings of the 11th International Conference on Artificial Intelligence in Education, AI-ED 2003 (pp. 39–46). Amsterdam: IOS.Google Scholar
  3. Allen, K., Carenini, G., Ng, R. (2014). Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1169–1180 Doha, Qatar.Google Scholar
  4. Backofen, R. & Smolka, G. (1993). A complete and recursive feature theory, Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pp 193–200.Google Scholar
  5. Bak, J., Lin, C., & Oh, A. (2014). Self-disclosure topic model for classifying and analyzing Twitter conversations. In Proceedings of the International Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), Doha, Qatar (p. 2014).Google Scholar
  6. Berkowitz, M. W., & Gibbs, J. C. (1983). Measuring the developmental features of moral discussion. Merrill-Palmer Quarterly, 29(4), 399–410.Google Scholar
  7. Bhatia, S., Biyani, P, & Mitra, P. (2014). Summarizing online forum discussions – can dialog acts of individual messages help? Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2127–2131.Google Scholar
  8. Bloom, B. S. (1984). The 2 Sigma Problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13, 4–16.CrossRefGoogle Scholar
  9. Bracewell, D. B., Tomlinson, M., Wang, H. (2012). A motif approach for identifying pursuits of power in social discourse. In Proceedings of the Sixth International Conference on Semantic Computing (ICSC) (pp. 1–8).Google Scholar
  10. Bramsen, P., Escobar-Molano, M., Patel, A. & Alonso, R. (2011). Extracting social power relationships from natural language. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 773–782.Google Scholar
  11. Breslow, L., Pritchard, D., de Boer, J., Stump, G., Ho, A., & Seaton, D. (2013). Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research & Practice in Assessment, 8, 13–25.Google Scholar
  12. Brown, P., & Levinson, S. (1987). Politeness: some universals in language usage. Cambridge: Cambridge University Press.Google Scholar
  13. Cadilhac, A., Asher, N., Benamara, F., & Lascarides, A. (2013). Grounding strategic conversation: using negotiation dialogues to predict trades in a win-lose game. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP) (pp. 357–368).Google Scholar
  14. Carbonell, J. (1969). On man-computer interaction: a model and some related issues. IEEE Transactionson Systems Science and Cybernetics, 5(1), 16–26.CrossRefGoogle Scholar
  15. Carbonell, J. (1970). AI in CAI: an artificial intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems, 11(4), 190–202.CrossRefGoogle Scholar
  16. Cohen, P. A., Kulik, J. A., & Kulik, C. C. (1982). Educational Outcomes of Tutoring: A meta-analysis of Findings. American Educational Research Journal, 19, 237–248.CrossRefGoogle Scholar
  17. Dyke, G., Adamson, A., Howley, I., & Rosé, C. P. (2013). Enhancing scientific reasoning and discussion with conversational agents. IEEE Transactions on Learning Technologies, 6(3), 240–247. Special issue on Science Teaching.CrossRefGoogle Scholar
  18. Erkens, G., & Janssen, J. (2006). Automatic coding of communication in collaboration protocols. Proceedings of the 7th International Conference of the Learning Sciences (ICLS).Google Scholar
  19. Evens, M., & Michael, J. (2006). One-on-One tutoring by humans and machines. Lawrence Erlbaum and Associates: Mahwah.Google Scholar
  20. Ferschke, O., Gurevych, I., & Chebotar, Y. (2012). Behind the Article: Recognizing Dialog Acts in Wikipedia Talk Pages, in Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp 777–786Google Scholar
  21. Ferschke, O., Howley, I., Tomar, G., Yang, D., Rosé, C. P. (2015a). Fostering Discussion across Communication Media in Massive Open Online Courses, Proceedings of Computer Supported Collaborative Learning. Pages 459–466.Google Scholar
  22. Ferschke, O., Yang, D., Tomar, G., Rosé, C. P. (2015b). Positive Impact of Collaborative Chat Participation in an edX MOOC, Proceedings of AI in Education. Pages 115–124.Google Scholar
  23. Ferschke, O., Yang, D., Rosé, C. P. (2015c). A Lightly Supervised Approach to Role Identification in Wikipedia Talk Page Discussions. Proceedings of the International AAAI Conference on Weblogs and Social Media. Workshop on Wikipedia, a Social Pedia: Research Challenges and Opportunities. Pages 43–47.Google Scholar
  24. Fischer, F., Kollar, I., Stegmann, K., & Wecker, C. (2013). Toward a script theory of guidance in computer-supported collaborative learning. Educational Psychologist, 48(1), 56–66.CrossRefGoogle Scholar
  25. Freedman, R. K., Rosé, C. P., Ringenberg, M. A., VanLehn, K. (2000). ITS Tools for Natural Language Dialogue: A Domain Independent Parser and Planner, Proceedings of the 5th International Conference on Intelligent Tutoring Systems, pp 433–442.Google Scholar
  26. Gee, J. P. (2011). An Introduction to Discourse Analysis: Theory and Method, Third Edition. New York: Routledge.Google Scholar
  27. Gertner, A., & VanLehn, K. (2000). Andes: A Coached Problem Solving Environment for Physics. In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Intelligent Tutoring Systems: 5th International Conference. Lecture Notes in Computer Science, Vol. 1839 (pp. 133–142). Berlin: Springer.CrossRefGoogle Scholar
  28. Graesser, A. C., Bowers, C. A., Hacker, D. J., & Person, N. K. (1997). An anatomy of naturalistic tutoring. In K. Hogan & M. Pressley (Eds.), Scaffolding student learning: Instructional approaches and issues. Cambridge: Brookline Books.Google Scholar
  29. Graesser, A., Li, H., Forsyth, C. (2014). Learning by Communicating in Natural Language with Conversational Agents. Current Directions in Psychological Science 23(5):374–380CrossRefGoogle Scholar
  30. Greer, J., McCalla, G., Cooke, J., Collins, J., Kumar, V., Bishop, A., & Vassileva, J. (1998). The Intelligent HelpDesk: Supporting Peer Help in a University Course. In Proceedings of the 4th International Conference on Intelligent Tutoring Systems, San Antonio, TX, LNCS No.1452 (pp. 494–503). Berlin: Springer Verlag.Google Scholar
  31. Guinote, A., & Vesvio, T. (2010). The social psychology of power. The Guilford Press: New York.Google Scholar
  32. Gweon, G., Rosé, C. P., Zaiss, Z., & Carey, R. (2006). Providing Support for Adaptive Scripting in an On-Line Collaborative Learning Environment. In Proceedings of CHI 06: ACM conference on human factors in computer systems (pp. 251–260). New York: ACM Press.CrossRefGoogle Scholar
  33. Gweon, G., Jain, M., Mc Donough, 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.CrossRefGoogle Scholar
  34. Hasan, K. & Ng, V. (2014). Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 751–762.Google Scholar
  35. Hmelo-Silver, C. E. (2003). Analyzing collaborative knowledge construction: multiple methods for integrated understanding. Computers & Education, 41(4), 397–420.CrossRefGoogle Scholar
  36. Hmelo-Silver, C., Chinn, C., Chan, C., & O’Donnell, A. (2013). The international handbook of collaborative learning. Routledge: New York.Google Scholar
  37. Howley, I., Tomar, G., Yang, D., Ferschke, O., Rosé, C.P. (2015). Alleviating the Negative Effect of Up and Downvoting on Help Seeking in MOOC Discussion Forums. Proceedings of the 17th International Conference on Artificial Intelligence in Education. pp. 629–632.Google Scholar
  38. Jordan, P., Rosé, C. P., & Vanlehn, K. (2001). Tools for Authoring Tutorial Dialogue Knowledge, Proceedings of the 10th International Conference on AI in Education. Texas: San Antonio.Google Scholar
  39. Klavans, J., & Resnick, P. (1994). The balancing act: combining symbolic and statistical approaches to language. The MIT Press: Cambridge.Google Scholar
  40. Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43.Google Scholar
  41. Kulkarni, C., Cambre, J., Kotturi, Y., Bernstein, M.S., & Klemmer, S.R. (2015). Talkabout: Making Distance Matter with Small Groups in Massive Classes. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ‘15). ACM: New York (pp. 1116–1128).Google Scholar
  42. Kumar, R., & Rosé, C. P. (2011). Architecture for building conversational agents that support collaborative learning. IEEE Transactions on Learning Technologies, 4(1), 21–34.CrossRefGoogle Scholar
  43. Kumar, R., Rosé, C. P., Aleven, V., Iglesias, A., & Robinson, A. (2006). Evaluating the Effectiveness of Tutorial Dialogue Instruction in an Exploratory Learning Context, ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems, pp 666–674. Berlin, Heidelberg: Springer-Verlag.Google Scholar
  44. Kumar, R., Rosé, C. P., Wang, Y. C., Joshi, M., Robinson, A. (2007). Tutorial Dialogue as Adaptive Collaborative Learning Support, Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work, pp 383–390Google Scholar
  45. Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  46. Levinson, S. C. (1983). Pragmatics. Cambridge Textbook in Linguistics. Cambridge: Cambridge University Press.Google Scholar
  47. Martin, J. R., & Rose, D. (2007). Working with discourse: Meaning beyond the clause. London: Continuum.Google Scholar
  48. Martin, J. R., & White, P. R. R. (2005). The language of evaluation: Appraisal in English. Basingstoke: Palgrave Macmillan.Google Scholar
  49. Mayfield, E. & Rosé, C. P. (2011). Recognizing Authority in Dialogue with an Integer Linear Programming Constrained Model. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1018–1026Google Scholar
  50. Mayfield, E., Adamson, D., & Rosé, C. P. (2013). Recognizing Rare Social Phenomena in Conversation: Empowerment Detection in Support Group Chatrooms. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (pp. 104–113).Google Scholar
  51. 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 EDiscussions. Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (pp. 331–338).Google Scholar
  52. 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 7(2), pp285-305.Google Scholar
  53. Mukherjee, A., Venkataraman, V., Liu, B., & Meraz, S. (2013). Public Dialogue: Analysis of Tolerance in Online Discussions. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 1680–1690.Google Scholar
  54. Myers-Scotton, C. (1993). Social motivations for codeswitching: Evidence from Africa. Oxford studies in language contact. Oxford: Clarendon Press.Google Scholar
  55. Nguyen, V., Boyd-Graber, J., & Resnick, P. (2012). SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pages 78–87.Google Scholar
  56. Paul, M. (2012). Mixed Membership Markov Models for Unsupervised Conversation Modeling. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 94–104, Jeju Island, Korea, 12–14 July 2012.Google Scholar
  57. Postemes, T., Spears, R., & Lea, M. (2000). The Formation of Group Norms in Computer-Mediated Communication. Human Communication Research, 26(3), 341–371.CrossRefGoogle Scholar
  58. Prabhakaran, V., Rambow, O., Diab, M. (2012). Predicting Overt Display of Power in Written Dialogs. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 518–522.Google Scholar
  59. Resnick, L., Asterhan, C., & Clarke, S. (2015). Socializing Intelligence through Academic Discourse. Washington, DC: American Educational Research Association.Google Scholar
  60. Ribeiro, B. (2006). Footing, positioning, voice: Are we talking about the same thing? In A. Fina, D. Schiffrin, & M. Bamberg (Eds.), Discourse and Identity (pp. 48–82). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  61. Rosé, C. P. (2000). A Framework for Robust Semantic Interpretation, Proceedings of 1st Meeting of the North American Chapter of the Association for Computational Linguistics, pp 311–318Google Scholar
  62. Rosé, C. P., & Torrey, C. (2005). Interactivity versus Expectation: Eliciting Learning Oriented Behavior with Tutorial Dialogue Systems, Proceedings of 10th IFIP TC13 International Conference on Human-Computer Interaction - Interact 2005, Lecture Notes in Computer Science Volume 3585, pp 323–336.Google Scholar
  63. Rosé, C. P., Moore, J. D., VanLehn, K., & Allbritton, D. (2001). A Comparative Evaluation of Socratic versus Didactic Tutoring, Proceedings of the 23rd Annual Conference of the Cognitive Sciences Society, pp 869–874. Scottland, UK: Edinburgh.Google Scholar
  64. Rosé, C. P., Roque, A., Bhembe, D., & VanLehn, K. (2002). An Efficient Incremental Architecture for Robust Interpretation, HLT ‘02 Proceedings of the 2nd International Conference on Human Languages Technologies, pp 307–312. California: San Diego.Google Scholar
  65. Rosé, C. P., Aleven, V., Carey, R., Robinson, A., Wu, C. (2005). A First Evaluation of the Instructional Value of Negotiatble Problem Solving Goals on the Exploratory Learning Continuum, Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pp 563–570.Google Scholar
  66. Rosé, C. P., Goldman, P., Sherer, J. Z., Resnick, L. (2015). Supportive Technologies for Group Discussion in MOOCs, Current Issues in Emerging eLearning, Special issue on MOOCs, January 2015.Google Scholar
  67. 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, submitted to the International Journal of Computer Supported Collaborative Learning 3(3), pp237-271.Google Scholar
  68. Scadamalia, M., & Bereiter, C. (2006). Knowledge Building: Theory, Pedagogy, and Technology. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (pp. 97–118). New York: Cambridge University Press.Google Scholar
  69. Schneider, G., Dowdall, J., & Rinaldi, F. (2004). A robust and hybrid deep-linguistic theory applied to large-scale parsing, Proceedings of the 3rd Workshop on Robust Methods in Analysis of Natural Language Data, pp 14–23.Google Scholar
  70. Schwartz, D. (1998). The productive agency that drives collaborative learning. In P. Dillenbourg (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 197–218). New York: Elsevier.Google Scholar
  71. Siemens, G. (2005). Connectivism: a learning theory for a digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.Google Scholar
  72. Smith, B. & Eng, M. (2013). MOOCs: A Learning Journey: Two continuing education practitioners investigate and compare cMOOC and xMOOC learning models and experience, in Proceedings of the 6th International Conference on Hybrid Learning and Continuing Education, pp. 244–255.Google Scholar
  73. Soller, A., & Lesgold, A. (2000). Modeling the Process of Collaborative Learning. Proceedings of the International Workshop on New Technologies in Collaborative Learning.Google Scholar
  74. Steele, J. (1990). Meaning-Text Theory: Linguistics, Lexicography, and Implications. Computational Linguistics, 18(1), 108–110.MathSciNetGoogle Scholar
  75. Stevens, A., & Collins, A. (1977). The Goal Structure of a Socratic Tutor. In Proceedings of the National ACM Conference. Association for Computing Machinery, New York, (Also available as BBN Report No. 3518 from Bolt Beranek and Newman Inc., Cambridge, Mass., 02138).Google Scholar
  76. Suthers, D. (2006). Technology affordances for inter-subjective meaning making: a research agenda for CSCL. International Journal of Computer Supported Collaborative Learning, 1, 315–337.CrossRefGoogle Scholar
  77. Teasley, S. D. (1997). Talking about reasoning: How important is the peer in peer collaboration? In L. B. Resnick, R. Säljö, C. Pontecorvo, & B. Burge (Eds.), Discourse, tools and reasoning: Essays on situated cognition (pp. 361–384). Berlin: Springer-Verlag.CrossRefGoogle Scholar
  78. Teplovs, C., Donoahue, Z., Scardamalia, M., & Philip, D. (2007). Tools for concurrent, embedded, and transformative assessment of knowledge building processes and progress. In Proceedings of the 8th iternational conference on Computer supported collaborative learning (CSCL‘07). C. A. Chinn, G. Erkens, S. Puntambekar (Eds.). International Society of the Learning Sciences (pp. 721–723).Google Scholar
  79. Treisman, U. (1992). Studying Students Studying Calculus: A Look at the Lives of Minority Mathematics Students in College. The College Mathematics Journal, 23(5), 362–372.CrossRefGoogle Scholar
  80. VanLehn, K., Jordan, P., Rosé, C. P., & The Natural Language Tutoring Group (2002). The Architecture of Why2-Atlas: a coach for qualitative physics essay writing. In S. A. Cerri, G. Gouarderes, & F. Paraguacu (Eds.), Intelligent Tutoring Systems, 2002: 6th International Conference (pp. 158–167). Berlin: Springer.Google Scholar
  81. VanLehn, K., Graesser, A., Jackson, G. T., Jordan, P., Olney, A., & Rosé, C. P. (2007). Natural Language Tutoring: A comparison of human tutors, computer tutors, and text. Cognitive Science, 31(1), 3–52.CrossRefGoogle Scholar
  82. Vassileva, J., McCalla, G., Greer, J. (2003). Multi-Agent Multi-User Modeling in I-Help. User Modeling and User Adapted Interaction 13(1), pp.179–210. Kluwer Academic Publishers.Google Scholar
  83. Walker, E., Rummel, N., & Koedinger, K. R. (2011). Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity. International Journal of Computer-Supported Collaborative Learning, 6, 279–306.CrossRefGoogle Scholar
  84. Wallace, B.C., Trikalinos, T. A., Laws, M. B., Wilson, I. B., & Charniak, E. (2013). A generative joint, additive, sequential model of topics and speech acts in patient-doctor communication. In EMNLP 2013–2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 1765–1775). Association for Computational Linguistics (ACL).Google Scholar
  85. Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71–95.CrossRefGoogle Scholar
  86. Wintner, S. (2002). Formal language theory for natural language processing, in Proceedings of the Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pp 71–76.Google Scholar
  87. Yang, D., Piergallini, M., Howley, I., Rose, C. (2014). Forum thread recommendation for massive open online courses. Proceedings of 7th Intl Conf. on Educational Data Mining. pp 257–260.Google Scholar
  88. Yang, D., Wen, M., Rosé, C. P. (2015). Weakly Supervised Role Identification in Teamwork Interactions. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Pages 1671–1680.Google Scholar
  89. Zhai, K., & Williams, J. (2014). Discovering latent structure in task-oriented dialogues. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 36–46. Baltimore, Maryland, USA.Google Scholar
  90. Zinn, C., Moore, J. D., & Core, M. G. (2002). A 3-Tier Planning Architecture for Managing Tutorial Dialogue. In S. A. Cerri, G. Gouardères, & F. Paraguaçu (Eds.), Proceedings of the Sixth International Conference on Intelligent Tutoring Systems, ITS 2002 (pp. 574–584). Berlin: Springer Verlag.Google Scholar

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© International Artificial Intelligence in Education Society 2016

Authors and Affiliations

  1. 1.Language Technologies Institute and Human-Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA

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