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The Civic Mission of MOOCs: Engagement across Political Differences in Online Forums

  • Michael YeomansEmail author
  • Brandon M. Stewart
  • Kimia Mavon
  • Alex Kindel
  • Dustin Tingley
  • Justin Reich
Article

Abstract

Massive open online courses (MOOCs) attract diverse student bodies, and course forums could potentially be an opportunity for students with different political beliefs to engage with one another. We test whether this engagement actually takes place in two politically-themed MOOCs, on education policy and American government. We collect measures of students’ political ideology, and then observe student behavior in the course discussion boards. Contrary to the common expectation that online spaces often become echo chambers or ideological silos, we find that students in these two political courses hold diverse political beliefs, participate equitably in forum discussions, directly engage (through replies and upvotes) with students holding opposing beliefs, and converge on a shared language rather than talking past one another. Research that focuses on the civic mission of MOOCs helps ensure that open online learning engages the same breadth of purposes that higher education aspires to serve.

Keywords

MOOCs Civic education Discourse Text analysis Political ideology Structural topic model 

Notes

Acknowledgements

We gratefully acknowledge grant support from the Spencer Foundations New Civics initiative and the Hewlett Foundation. We also thank the course teams from Saving Schools and American Government, the Harvard VPAL-Research Group for research support, Lisa McKay for edits, and research assistance from Alyssa Napier, Joseph Schuman, Ben Schenck, Elise Lee, Jenny Sanford, Holly Howe, Jazmine Henderson & Nikayah Etienne.

References

  1. Arora, S., Ge, R., Halpern, Y., Mimno, D. M., Moitra, A., Sontag, D., Wu, Y., & Zhu, M. (2013). A practical algorithm for topic modeling with provable guarantees. International Conference on Machine Learning, 2, 280–288.Google Scholar
  2. Athey, S., & Mobius, M. (2012). The impact of news aggregators on internet news consumption: The case of localization. Working Paper. Google Scholar
  3. Baek, J., & Shore, J. (2016). Promoting student engagement in MOOCs. In Proceedings of the third ACM conference on learning@ scale, 293–296. Google Scholar
  4. Benoit, K. (2017). Quanteda: Quantitative analysis of textual data. R package version 0.99.22. https://doi.org/10.5281/zenodo.1004683.
  5. Bischof, J.M. & Airoldi, E.M. (2012). Summarizing topical content with word frequency and exclusivity. In International Conference on Machine Learning (ICML), 201–208.Google Scholar
  6. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.zbMATHGoogle Scholar
  7. Boxell, L., Gentzkow, M., & Shapiro, J. M. (2017). Greater Internet use is not associated with faster growth in political polarization among US demographic groups. Proceedings of the National Academy of Sciences, 201706588. Google Scholar
  8. Brysbaert, M., & New, B. (2009). Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41(4), 977–990.CrossRefGoogle Scholar
  9. Chuang, I. & Ho, A.D. (2016). HarvardX and MITx: Four years of open online courses — Fall 2012-summer 2016. Working Paper. Google Scholar
  10. Della Carpini, M. X. D., Cook, F. L., & Jacobs, L. R. (2004). Public deliberation, discursive participation, and citizen engagement: A review of the empirical literature. Annu. Rev. Polit. Sci., 7, 315–344.CrossRefGoogle Scholar
  11. Doyle, G., & Frank, M. C. (2016). Investigating the sources of linguistic alignment in conversation. In Proceedings of ACL. Google Scholar
  12. Education Next. (2015). Results from the 2015 Education Next Poll. Retrieved October 27, 2015 from http://educationnext.org/2015-ednext-poll-interactive
  13. Faris, R. M., Roberts, H., Etling, B., Bourassa, N., Zuckerman, E., and Benkler, Y. (2017). Partisanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential Election. Berkman Klein Center for Internet & Society Research Paper. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33759251.
  14. Feinerer, I., Hornik, K., & Meyer, D. (2008). Text mining infrastructure in R. Journal of Statistical Software, 25(5), 1–54.CrossRefGoogle Scholar
  15. Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, Echo chambers and online news consumption. Public Opinion Quarterly, 80, 298–320.CrossRefGoogle Scholar
  16. Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22.CrossRefGoogle Scholar
  17. Gardner, H., & Davis, K. (2013). The app generation: How today’s youth navigate identity, intimacy, and imagination in a digital world. New Haven: Yale University Press.Google Scholar
  18. Garrett, R. K. (2009). Echo chambers online?: Politically motivated selective exposure among internet news users1. Journal of Computer-Mediated Communication, 14(2), 265–285.MathSciNetCrossRefGoogle Scholar
  19. Gastil, J. (1992). Undemocratic discourse: A review of theory and research on political discourse. Discourse & Society, 3(4), 469–500.CrossRefGoogle Scholar
  20. Gentzkow, M., & Shapiro, J. M. (2010). What drives media slant? Evidence from US daily newspapers. Econometrica, 78(1), 35–71.MathSciNetCrossRefGoogle Scholar
  21. Gentzkow, M., & Shapiro, J. M. (2011). Ideological segregation online and offline. The Quarterly Journal of Economics, 126(4), 1799–1839.CrossRefGoogle Scholar
  22. Giles, H., Coupland, J., & Coupland, N. (1991). Contexts of accommodation: Developments in applied sociolinguistics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  23. Grimmer, J., & King, G. (2011). General purpose computer-assisted clustering and conceptualization. Proceedings of the National Academy of Sciences, 108(7), 2643–2650.CrossRefGoogle Scholar
  24. Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 267–297.Google Scholar
  25. Groseclose, T., & Milyo, J. (2005). A measure of media bias. The Quarterly Journal of Economics, 120(4), 1191–1237.CrossRefGoogle Scholar
  26. Hübler, A. (1983). Understatements and Hedges in English. Pragmatics and Beyond, 4(6), 1–192.Google Scholar
  27. Ireland, M. E., Slatcher, R. B., Eastwick, P. W., Scissors, L. E., Finkel, E. J., & Pennebaker, J. W. (2011). Language style matching predicts relationship initiation and stability. Psychological Science, 22(1), 39–44.CrossRefGoogle Scholar
  28. Jason, G. (1988). Hedging as a fallacy of language. Informal Logic, 10(3), 169–175.CrossRefGoogle Scholar
  29. Jurafsky, D., & Martin, J. (2009). Speech and natural language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River: Prentice Hall.Google Scholar
  30. Kahne, J., Middaugh, E., Lee, N. J., & Feezell, J. T. (2012). Youth online activity and exposure to diverse perspectives. New Media & Society, 14(3), 492–512.  https://doi.org/10.1177/1461444811420271.CrossRefGoogle Scholar
  31. Kincaid, J. P., Fishburne Jr, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel (No. RBR-8-75). Naval Technical Training Command Millington TN Research Branch.Google Scholar
  32. Kindel, A., Yeomans, M., Reich, J., Stewart, B., & Tingley, D. (2017, April). Discourse: MOOC Discussion Forum Analysis at Scale. In Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale (pp. 141–142). ACM.Google Scholar
  33. Koutropoulos, A., Gallagher, M. S., Abajian, S. C., de Waard, I., Hogue, R. J., Keskin, N. Ö., & Rodriguez, C. O. (2012). Emotive vocabulary in MOOCs: Context & participant retention. European Journal of Open, Distance and E-Learning, 15(1). Google Scholar
  34. Lakoff, G. (2014). The all new Don't think of an elephant!: Know your values and frame the debate. Chelsea Green Publishing. Google Scholar
  35. Loveland, M. T., & Popescu, D. (2011). Democracy on the web: Assessing the deliberative qualities of internet forums. Information, Communication & Society, 14(5), 684–703.CrossRefGoogle Scholar
  36. Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008). Fightin'words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis, 16(4), 372–403.CrossRefGoogle Scholar
  37. Orfield, G., Kucsera, J. & Siegel-Hawley, G. (2012). E pluribus… separation: Deepening double segregation for more students. Working Paper.Google Scholar
  38. Pariser, E. (2012). The filter bubble: How the new personalized web is changing what we read and how we think. New York: Penguin Books.CrossRefGoogle Scholar
  39. Pennebaker, J. W., Booth, R. J., & Francis, M. E. (2007). Linguistic inquiry and word count: LIWC [Computer software]. Austin, TX: liwc. net.Google Scholar
  40. Peterson, P.E. (2010) Let the Charters Bloom. Retrieved August 7, 2015 from http://www.hoover.org/research/let-charters-bloom
  41. Peterson, P. E., Henderson, M., & West, M. R. (2014). What Americans think about schools and how to fix them. Washington, D.C.: Brookings Institution Press.Google Scholar
  42. Quattrociocchi, W., Scala, A., & Sunstein, C. R. (2016). Echo chambers on facebook. Working Paper. Google Scholar
  43. Reich, J., Romer, A., & Barr, D. J. (2014). Dialogue across difference: A case study of Facing History and Ourselves’ Digital Media Innovation Network. In B. Kirshner, E. Middaugh (Eds.), Becoming political in a digital age. Charlotte, NC: Information Age Publishing.Google Scholar
  44. Reich, J., Tingley, D., Leder-Luis, J., Roberts, M. E., & Stewart, B. M. (2015). Computer-assisted reading and discovery for student generated text in massive open online courses. Journal of Learning Analytics., 2(1), 156–184.CrossRefGoogle Scholar
  45. Rheingold, H. (2000). The virtual community: Homesteading on the electronic frontier. Cambridge: MIT press.Google Scholar
  46. Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., & Rand, D. G. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58(4), 1064–1082.CrossRefGoogle Scholar
  47. Roberts, M. E., Stewart, B. M., & Airoldi, E. M. (2016a). A model of text for experimentation in the social sciences. Journal of the American Statistical Association, just-accepted, 1–49.MathSciNetGoogle Scholar
  48. Roberts, M. E., Stewart, B. M., & Tingley, D. (2016). Navigating the local modes of big data. In R. M. Alvarez (Ed.).Computational Social Science. Cambridge: Cambridge University Press.Google Scholar
  49. Schaffner, B., Ansolabehere, S. (2015) 2010-2014 cooperative congressional election study panel survey.  https://doi.org/10.7910/DVN/TOE8I1.
  50. Siemens, G. (2005). Connectivism: Learning as network-creation. ASTD Learning News, 10(1).Google Scholar
  51. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B: Methodological, 111–147.Google Scholar
  52. Stump, G. S., DeBoer, J., Whittinghill, J., & Breslow, L. (2013). Development of a framework to classify MOOC discussion forum posts: Methodology and challenges. In: NIPS Workshop on Data Driven Education, 1–20.Google Scholar
  53. Sunstein, C. R. (2017). # Republic: Divided democracy in the age of social media. Princeton: Princeton University Press.CrossRefGoogle Scholar
  54. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B: Methodological, 58(1), 267–288.Google Scholar
  55. Varma, S., & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7(1), 91.CrossRefGoogle Scholar
  56. Welbers, K., & de Nooy, W. (2014). Stylistic accommodation on an internet forum as bonding: Do posters adapt to the style of their peers? American Behavioral Scientist, 58(10), 1361–1375.CrossRefGoogle Scholar
  57. Wen, M., Yang, D. & Rośe, C.P. (2014). Linguistic reflections of student engagement in massive open online courses. In Proceedings of the International Conference on Weblogs and Social Media, 525–534.Google Scholar
  58. World Values Survey Association. (2009). World Values Survey 1981–2008 official aggregate v. 20090901. Madrid: ASEP/JDS.Google Scholar
  59. Yang, D., Wen, M., Howley, I., Kraut, R., & Rose, C. (2015, March). Exploring the effect of confusion in discussion forums of massive open online courses. In Proc 2nd ACM Conference on Learning@Scale, 121–130.Google Scholar

Copyright information

© International Artificial Intelligence in Education Society 2017

Authors and Affiliations

  • Michael Yeomans
    • 1
    Email author
  • Brandon M. Stewart
    • 2
  • Kimia Mavon
    • 1
  • Alex Kindel
    • 2
  • Dustin Tingley
    • 1
  • Justin Reich
    • 3
  1. 1.Harvard UniversityCambridgeUSA
  2. 2.Princeton UniversityPrincetonUSA
  3. 3.Massachusetts Institute of TechnologyCambridgeUSA

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