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Inferring Students’ Personality from Their Communication Behavior in Web-based Learning Systems

  • Wen WuEmail author
  • Li Chen
  • Qingchang Yang
  • You Li
Article
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Abstract

Communication tools have been popular in web-based learning systems because of their ability to promote the interaction and potentially alleviate the high dropout issue. In recent years, with the increased awareness among researchers about the individual difference of the students, more and more personalized learning supports have been developed. Although personality has been considered as a valuable personal factor being incorporated into the provision of personalized learning, existing studies mainly acquire students’ personality via questionnaires, which unavoidably demands user efforts. In this paper, we are motivated to derive students’ Big-Five personality from their communication behavior in web-based learning systems. Concretely, we first identify a set of features that are significantly influenced by students’ personality, which not only include their communication activities carried out in both synchronous and asynchronous web-based learning environment, but also their linguistic content in conversational texts. We then develop inference model to unify these features for determining students’ five personality traits, and find that students’ usage of different communication tools can be effective in predicting their Big-Five personality.

Keywords

Web-based learning system Personality prediction Synchornous/asynchronous communication User survey Linguistic content 

Notes

Acknowledgements

We thank all participants who took part in our user survey. We also thank reviewers for their suggestions and comments. In addition, we thank Hong Kong Research Grants Council (RGC) for sponsoring the research work (under project RGC/HKBU12200415).

References

  1. Adali, S., & Golbeck, J. (2012). Predicting personality with social behavior. In Proceedings of the 4th international conference on advances in social networks analysis and mining (ASONAM 2012) (pp. 302–309): IEEE Computer Society.Google Scholar
  2. Ally, M. (2004). Foundations of educational theory for online learning. Theory and Practice of Online Learning, 2, 15–44.Google Scholar
  3. Amichai-Hamburger, Y., & Vinitzky, G. (2010). Social network use and personality. Computers in Human Behavior, 26, 1289–1295.CrossRefGoogle Scholar
  4. Armstrong, R.A. (2014). When to use the bonferroni correction. Ophthalmic and Physiological Optics, 34, 502–508.CrossRefGoogle Scholar
  5. Barrick, M.R., & Mount, M.K. (1991). The big five personality dimensions and job performance: a meta-analysis. Personnel Psychology, 44, 1–26.CrossRefGoogle Scholar
  6. Betts, K. (2009). Lost in translation: importance of effective communication in online education. Online Journal of Distance Learning Administration, 12, 1–13.Google Scholar
  7. Blau, I., & Barak, A. (2012). How do personality, synchronous media, and discussion topic affect participation? Educational Technology & Society, 15, 12–24.Google Scholar
  8. Branon, R.F., & Essex, C. (2001). Synchronous and asynchronous communication tools in distance education. TechTrends, 45, 36–36.CrossRefGoogle Scholar
  9. Busato, V.V., Prins, F.J., Elshout, J.J., Hamaker, C. (1998). The relation between learning styles, the big five personality traits and achievement motivation in higher education. Personality and Individual Differences, 26, 129–140.CrossRefGoogle Scholar
  10. Chamorro-Premuzic, T., & Furnham, A. (2008). Personality, intelligence and approaches to learning as predictors of academic performance. Personality and Individual Differences, 44, 1596–1603.CrossRefGoogle Scholar
  11. Chamorro-Premuzic, T., & Furnham, A. (2009). Mainly openness: the relationship between the big five personality traits and learning approaches. Learning and Individual Differences, 19, 524–529.CrossRefGoogle Scholar
  12. Chamorro-Premuzic, T., Furnham, A., Lewis, M. (2007). Personality and approaches to learning predict preference for different teaching methods. Learning and Individual Differences, 17, 241–250.CrossRefGoogle Scholar
  13. Chausson, O. (2010). Who watches what?: assessing the impact of gender and personality on film preferences. Paper published online on the MyPersonality project website. http://mypersonality.org/wiki/doku.php.
  14. Chen, G., Davis, D., Hauff, C., Houben, G.-J. (2016). On the impact of personality in massive open online learning. In Proceedings of the 24th conference on user modeling adaptation and personalization (UMAP 2016). ACM (pp. 121–130).Google Scholar
  15. Chen, S. -J., & Caropreso, E.J. (2004). Influence of personality on online discussion. Journal of Interactive Online Learning, 3, 1–17.Google Scholar
  16. Chittaranjan, G., Blom, J., Gatica-Perez, D. (2013). Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing, 17, 433–450.CrossRefGoogle Scholar
  17. Coetzee, D., Fox, A., Hearst, M.A., Hartmann, B. (2014). Chatrooms in moocs: all talk and no action. In Proceedings of the 1st ACM conference on learning@ scale conference (L@S2014). ACM (pp. 127–136).Google Scholar
  18. Costa, P., & McCrae, R. (1992). Neo pi-r: professional manual: revised neo pi-r and neo-ffi. Florida: Psychological Assessment Resources.Google Scholar
  19. Costa, P. Jr, Terracciano, A., McCrae, R.R. (2001). Gender differences in personality traits across cultures: robust and surprising findings. Journal of Personality and Social Psychology, 81, 322.CrossRefGoogle Scholar
  20. Costa, P.T. Jr. (1991). Clinical use of the five-factor model: an introduction. Journal of Personality Assessment, 57, 393–398.CrossRefGoogle Scholar
  21. Digman, J.M. (1990). Personality structure: emergence of the five-factor model. Annual Review of Psychology, 41, 417–440.CrossRefGoogle Scholar
  22. Duff, A., Boyle, E., Dunleavy, K., Ferguson, J. (2004). The relationship between personality, approach to learning and academic performance. Personality and Individual Differences, 36, 1907–1920.CrossRefGoogle Scholar
  23. Emerson, T.L., English, L., McGoldrick, K. (2016). Cooperative learning and personality types. International Review of Economics Education, 21, 21–29.CrossRefGoogle Scholar
  24. Farnadi, G., Sitaraman, G., Sushmita, S., Celli, F., Kosinski, M., Stillwell, D., Davalos, S., Moens, M. -F., De Cock, M. (2016). Computational personality recognition in social media. User Modeling and User-Adapted Interaction, 26, 109–142.CrossRefGoogle Scholar
  25. Felder, R.M., Felder, G.N., Dietz, E.J. (2002). The effects of personality type on engineering student performance and attitudes. Journal of Engineering Education, 91, 3–17.CrossRefGoogle Scholar
  26. Ferwerda, B., Schedl, M., Tkalcic, M. (2015). Predicting personality traits with instagram pictures. In Proceedings of the 3rd workshop on emotions and personality in personalized systems (EMPIRE 2015). ACM (pp. 7–10).Google Scholar
  27. Gao, R., & et al. (2013). Improving user profile with personality traits predicted from social media content. In Proceedings of the 7th ACM conference on recommender systems (RecSys 2013). ACM (pp. 355–358).Google Scholar
  28. Ghorbani, F., & Montazer, G.A. (2015). E-learners’ personality identifying using their network behaviors. Computers in Human Behavior, 51, 42–52.CrossRefGoogle Scholar
  29. Gill, A.J., & Oberlander, J. (2002). Taking care of the linguistic features of extraversion. In Proceedings of the annual meeting of the cognitive science society. volume 24.Google Scholar
  30. Golbeck, J., Robles, C., Turner, K. (2011). Predicting personality with social media. In CHI’11 extended abstracts on human factors in computing systems. ACM (pp. 253–262).Google Scholar
  31. Goldberg, L.R., & et al. (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality, 40, 84–96.CrossRefGoogle Scholar
  32. Gosling, S.D., Rentfrow, P.J., Swann, W.B. (2003). A very brief measure of the big-five personality domains. Journal of Research in Personality, 37, 504–528.CrossRefGoogle Scholar
  33. Halawa, M.S., Shehab, M.E., Hamed, R., Essam, M. (2015). Predicting student personality based on a data-driven model from student behavior on lms and social networks. In Proceedings of 5th IEEE international conference on digital information processing and communications (ICDIPC 2015). IEEE (pp. 294–299).Google Scholar
  34. Hanzaki, M.R., & Epp, C.D. (2018). The effect of personality and course attributes on academic performance in moocs. In European conference on technology enhanced learning. Springer (pp. 497–509).Google Scholar
  35. Hmelo-Silver, C.E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16, 235–266.CrossRefGoogle Scholar
  36. Hrastinski, S. (2008). Asynchronous and synchronous e-learning. Educause Quarterly, 31, 51–55.Google Scholar
  37. Kim, J., Lee, A., Ryu, H. (2013). Personality and its effects on learning performance: design guidelines for an adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics, 43, 450–461.CrossRefGoogle Scholar
  38. Komarraju, M., Karau, S.J., Schmeck, R.R. (2009). Role of the big five personality traits in predicting college students’ academic motivation and achievement. Learning and Individual Differences, 19, 47–52.CrossRefGoogle Scholar
  39. Kosinski, M., Stillwell, D., Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110, 5802–5805.CrossRefGoogle Scholar
  40. van Lankveld, G., Spronck, P., van den Herik, J., Arntz, A. (2011). Games as personality profiling tools. In Proceedings of IEEE conference on computational intelligence and games (CIG 2011). IEEE (pp. 197–202).Google Scholar
  41. Lockhart, R., Taylor, J., Tibshirani, R.J., Tibshirani, R. (2014). A significance test for the lasso. Annals of Statistics, 42, 413.MathSciNetCrossRefzbMATHGoogle Scholar
  42. Lynn, R., & Martin, T. (1997). Gender differences in extraversion, neuroticism, and psychoticism in 37 nations. The Journal of Social Psychology, 137, 369–373.CrossRefGoogle Scholar
  43. Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K. (2007). Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of Artificial Intelligence Research, 30, 457–500.CrossRefzbMATHGoogle Scholar
  44. Mandernach, B.J. (2009). Effect of instructor-personalized multimedia in the online classroom. The International Review of Research in Open and Distributed Learning 10.Google Scholar
  45. McCrae, R.R., & John, O.P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60, 175–215.CrossRefGoogle Scholar
  46. Moore, M.G., & Kearsley, G. (2011). Distance education: a systems view of online learning. Cengage learning.Google Scholar
  47. Myers, I.B., McCaulley, M.H., Quenk, N.L., Hammer, A.L. (1998). MBTI manual: a guide to the development and use of the Myers-Briggs Type Indicator volume 3. Palo Alto: Consulting Psychologists Press.Google Scholar
  48. Naidu, S., & Järvelä, S. (2006). Analyzing cmc content for what? Computers & Education, 46, 96–103.CrossRefGoogle Scholar
  49. Nunnally, J.C., Bernstein, I.H., Berge, J. M. t. (1967). Psychometric theory volume 226. JSTOR.Google Scholar
  50. Oztok, M., Zingaro, D., Brett, C., Hewitt, J. (2013). Exploring asynchronous and synchronous tool use in online courses. Computers & Education, 60, 87–94.CrossRefGoogle Scholar
  51. Pavalache-Ilie, M., & Cocorada, S. (2014). Interactions of students’ personality in the online learning environment. Procedia-Social and Behavioral Sciences, 128, 117–122.CrossRefGoogle Scholar
  52. Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K. (2015). The development and psychometric properties of liwc2015. UT Faculty/Researcher Works.Google Scholar
  53. Perrett, D., Schaffer, J., Piccone, A., Roozeboom, M., et al. (2006). Bonferroni adjustments in tests for regression coefficients. Multiple Linear Regression Viewpoints, 32, 1–6.Google Scholar
  54. Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J. (2011). Our twitter profiles, our selves: predicting personality with twitter. In Privacy, security, risk and trust (PASSAT) and 2011 IEEE 3rd inernational conference on social computing (SocialCom 2011). IEEE (pp. 180–185).Google Scholar
  55. Rammstedt, B., & John, O.P. (2007). Measuring personality in one minute or less: a 10-item short version of the big five inventory in english and German. Journal of Research in Personality, 41, 203–212.CrossRefGoogle Scholar
  56. Reid, J.M. (1987). The learning style preferences of esl students. TESOL Quarterly, 21, 87–111.CrossRefGoogle Scholar
  57. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582–599.CrossRefGoogle Scholar
  58. Rovai, A.P. (2002). Development of an instrument to measure classroom community. The Internet and Higher Education, 5, 197–211.CrossRefGoogle Scholar
  59. Seber, G.A., & Lee, A.J. (2012). Linear regression analysis volume 329. New York: Wiley.Google Scholar
  60. Shen, J., Brdiczka, O., Liu, J. (2013). Understanding email writers: personality prediction from email messages. In User modeling, adaptation, and personalization. Springer (pp. 318–330).Google Scholar
  61. Shen, J., Brdiczka, O., Liu, J. (2015). A study of facebook behavior: what does it tell about your neuroticism and extraversion? Computers in Human Behavior, 45, 32–38.CrossRefGoogle Scholar
  62. Simon, B., Davis, K., Griswold, W.G., Kelly, M., Malani, R. (2008). Noteblogging: taking note taking public. ACM SIGCSE Bulletin, 40, 417–421.CrossRefGoogle Scholar
  63. So, H. -J., & Brush, T.A. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: relationships and critical factors. Computers & Education, 51, 318–336.CrossRefGoogle Scholar
  64. Solimeno, A., Mebane, M.E., Tomai, M., Francescato, D. (2008). The influence of students and teachers characteristics on the efficacy of face-to-face and computer supported collaborative learning. Computers & Education, 51, 109–128.CrossRefGoogle Scholar
  65. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.MathSciNetCrossRefzbMATHGoogle Scholar
  66. Vonderwell, S. (2003). An examination of asynchronous communication experiences and perspectives of students in an online course: a case study. The Internet and Higher Education, 6, 77–90.CrossRefGoogle Scholar
  67. Wallace, J. (1992). Do students who prefer to learn alone achieve better than students who prefer to learn with peers? Education, 113, 630–635.Google Scholar
  68. Weber, G., & Brusilovsky, P. (2001). Elm-art: an adaptive versatile system for web-based instruction. International Journal of Artificial Intelligence in Education, 12, 351–384.Google Scholar
  69. Wei, H., Zhang, F., Yuan, N.J., Cao, C., Fu, H., Xie, X., Rui, Y., Ma, W.-Y. (2017). Beyond the words: predicting user personality from heterogeneous information. In Proceedings of the 10th ACM international conference on web search and data mining (WSDM 2017). ACM (pp. 305–314).Google Scholar
  70. Wild, R.H., & Winniford, M. (1993). Remote collaboration among students using electronic mail. Computers & Education, 21, 193–203.CrossRefGoogle Scholar
  71. Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O’donnell, J., Rowe, C.M. (1985). Statistics for the evaluation and comparison of models. American geophysical union.Google Scholar
  72. Wilson, E.V. (2000). Student characteristics and computer-mediated communication. Computers & Education, 34, 67–76.CrossRefGoogle Scholar
  73. Wu, W., & Chen, L. (2015). Implicit acquisition of user personality for augmenting movie recommendations. In Proceedings of the 23rd international conference on user modeling, adaptation, and personalization (UMAP 2015). Springer (pp. 302–314).Google Scholar
  74. Wu, W., Chen, L., Yang, Q. (2016). Students’ personality and chat room behavior in synchronous online learning. In Proceedings of the 24th Conference on User Modeling Adaptation and Personalization (UMAP 2016). Late-Breaking Results: ACM.Google Scholar
  75. Zar, J.H. (2005). Spearman rank correlation. In P. Armitage & T. Colton (Eds.), Encyclopedia of Biostatistics.  https://doi.org/10.1002/0470011815.b2a15150.
  76. Zheng, S., Rosson, M.B., Shih, P.C., Carroll, J.M. (2015). Understanding student motivation, behaviors and perceptions in moocs. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (CSCW 2015). ACM (pp. 1882–1895).Google Scholar

Copyright information

© International Artificial Intelligence in Education Society 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceHong Kong Baptist UniversityHong KongChina
  2. 2.Adaptive Learning CenterHong Kong Baptist UniversityHong KongChina
  3. 3.Jiachen Technology LimitedHong KongChina

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