Skip to main content

Tweets Reveal More Than You Know: A Learning Style Analysis on Twitter

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 7563)

Abstract

Adaptation and personalization of e-learning and technology-enhanced learning (TEL) systems in general, have become a tremendous key factor for the learning success with such systems. In order to provide adaptation, the system needs to have access to relevant data about the learner. This paper describes a preliminary study with the goal to infer a learner’s learning style from her Twitter stream. We selected the Felder-Silverman Learning Style Model (FSLSM) due to its validity and widespread use and collected ground truth data from 51 study participants based on self-reports on the Index of Learning Style questionnaire and tweets posted on Twitter. We extracted 29 features from each subject’s Twitter stream and used them to classify each subject as belonging to one of the two poles for each of the four dimensions of the FSLSM. We found a more than by chance agreement only for a single dimension: active/reflective. Further implications and an outlook are presented.

Keywords

  • Learning Style
  • Learning Preference
  • Twitter User
  • Chance Agreement
  • Twitter Stream

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-33263-0_12
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-642-33263-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Burleson, W., Picard, R.: Gender-specific approaches to developing emotionally intelligent learning companions. IEEE Intelligent Systems 22(4), 62–69 (2007)

    CrossRef  Google Scholar 

  2. Kalyuga, S., Sweller, J.: Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology Research and Development 53, 83–93 (2005), doi:10.1007/BF02504800

    CrossRef  Google Scholar 

  3. Chen, C.M., Lee, H.M., Chen, Y.H.: Personalized e-learning system using item response theory. Computers & Education 44(3), 237–255 (2005)

    CrossRef  Google Scholar 

  4. Blanchard, E., Razaki, R., Frasson, C.: Cross-cultural adaptation of elearning contents: a methodology. In: International Conference on E-Learning (2005)

    Google Scholar 

  5. Stash, N., Cristea, A., Bra, P.D.: Adaptation to learning styles in e-learning: Approach evaluation. In: Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2006, pp. 284–291 (2006)

    Google Scholar 

  6. Pashler, H., McDaniel, M., Rohrer, D., Bjork, R.: Learning styles. Psychological Science in the Public Interest 9(3), 105–119 (2008)

    Google Scholar 

  7. Ivy, L., Cheung, C., Lee, M.: Understanding Twitter Usage: What Drive People Continue to Tweet? In: Proceedings of Pacific-Asia Conference on Information Systems, Taipei, Taiwan (2010)

    Google Scholar 

  8. Kolb, D.A.: Experiential learning: Experience as the source of learning and development. Prentice Hall, Englewood Cliffs (1984)

    Google Scholar 

  9. Leite, W.L., Svinicki, M., Shi, Y.: Attempted validation of the scores of the vark: Learning styles inventory with multitrait-multimethod confirmatory factor analysis models. Educational and Psychological Measurement 70(2), 323–339 (2009)

    CrossRef  Google Scholar 

  10. Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Journal of Engineering Education 78(7), 674–681 (1988)

    Google Scholar 

  11. Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. International Journal of Engineering Education 21(1), 103–112 (2005)

    Google Scholar 

  12. Geiger, M.A., Pinto, J.K.: Changes in learning style preference during a three-year longitudinal study. Psychological Reports 69(3), 755–762 (1991)

    CrossRef  Google Scholar 

  13. Hauff, C., Houben, G.J.: Deriving Knowledge Profiles from Twitter. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds.) EC-TEL 2011. LNCS, vol. 6964, pp. 139–152. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  14. Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65. ACM (2007)

    Google Scholar 

  15. Naaman, M., Boase, J., Lai, C.H.: Is it really about me?: message content in social awareness streams. In: CSCW 2010, pp. 189–192 (2010)

    Google Scholar 

  16. Westman, S., Freund, L.: Information interaction in 140 characters or less: genres on twitter. In: IIiX 2010, pp. 323–328 (2010)

    Google Scholar 

  17. Zhao, D., Rosson, M.B.: How and why people twitter: the role that micro-blogging plays in informal communication at work. In: GROUP 2009, pp. 243–252 (2009)

    Google Scholar 

  18. Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on twitter: a first look. In: AND 2010, pp. 73–80 (2010)

    Google Scholar 

  19. Hecht, B., Hong, L., Suh, B., Chi, E.H.: Tweets from justin bieber’s heart: the dynamics of the location field in user profiles. In: CHI 2011, pp. 237–246 (2011)

    Google Scholar 

  20. Mislove, A., Lehmann, S., Ahn, Y.Y., Onnela, J.P., Rosenquist, J.N.: Understanding the Demographics of Twitter Users. In: ICWSM 2011 (2011)

    Google Scholar 

  21. Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: SMUC 2010, pp. 37–44 (2010)

    Google Scholar 

  22. Graf, S., Kinshuk: An approach for detecting learning styles in learning management systems. In: Sixth International Conference on Advanced Learning Technologies, pp. 161–163 (July 2006)

    Google Scholar 

  23. Garcia, P., Amandi, A., Schiaffino, S., Campo, M.: Evaluating bayesian networks precision for detecting students learning styles. Computers & Education 49(3), 794–808 (2007)

    CrossRef  Google Scholar 

  24. Sanders, D., Bergasa-Suso, J.: Inferring learning style from the way students interact with a computer user interface and the www. IEEE Transactions on Education 53(4), 613–620 (2010)

    CrossRef  Google Scholar 

  25. Bergasa-Suso, J., Sanders, D., Tewkesbury, G.: Intelligent browser-based systems to assist internet users. IEEE Transactions on Education 48(4), 580–585 (2005)

    CrossRef  Google Scholar 

  26. Fang Zhang, L.: Does the big five predict learning approaches? Personality and Individual Differences 34(8), 1431–1446 (2003)

    CrossRef  Google Scholar 

  27. Litzinger, T., Lee, S., Wise, J., Felder, R.: Intelligent browser-based systems to assist internet users. Journal of Engineering Education 96(4), 309–319 (2007)

    Google Scholar 

  28. Strapparava, C., Valitutti, R.: Wordnet-affect: an affective extension of wordnet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation, pp. 1083–1086 (2004)

    Google Scholar 

  29. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)

    MathSciNet  MATH  CrossRef  Google Scholar 

  30. Zywno, M.: A contribution to validation of score meaning for felder-solomans index of learning styles. In: Proceedings of the 2003 American Society for Engineering Annual Conference and Exposition (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hauff, C., Berthold, M., Houben, GJ., Steiner, C.M., Albert, D. (2012). Tweets Reveal More Than You Know: A Learning Style Analysis on Twitter. In: Ravenscroft, A., Lindstaedt, S., Kloos, C.D., Hernández-Leo, D. (eds) 21st Century Learning for 21st Century Skills. EC-TEL 2012. Lecture Notes in Computer Science, vol 7563. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33263-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33263-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33262-3

  • Online ISBN: 978-3-642-33263-0

  • eBook Packages: Computer ScienceComputer Science (R0)