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User Experiences Around Sentiment Analyses, Facilitating Workplace Learning

  • Christian Voigt
  • Barbara Kieslinger
  • Teresa Schäfer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10283)

Abstract

User acceptance is key for the adoption of a new technology. In this work we experiment with a novel service for tutors in workplace learning settings. Sentiment analysis is a way to extract feelings and emotions from a text. In a learning setting such a sentiment analysis can be part of learning analytics. It has the potential to foster the understanding of emotions in shared discussions in learning environments, detect group dynamics as well as the impact of certain topics on learners’ sentiments. However, sentiment analysis presents some challenges too, as lived experiences, expectations and ultimately acceptance of this technology varies greatly and can become barriers to adoption. In order to design a system for learning analytics accepted by tutors we experimented with proof-of-concept prototypes and received valuable feedback from tutors regarding the usefulness of the overall sentiment analysis as well as certain features. The qualitative feedback confirms the overall interest of tutors in sentiment analysis and gives important hints towards more detailed analytical elements.

Keywords

Sentiment analysis Learning analytics User experience 

Notes

Acknowledgments

This work is part of the EmployID project, which has received funding from the European Union’s Seventh Framework Programme for research, technological development an demonstration under grant agreement no. 619619.

References

  1. 1.
    Wright, P., McCarthy, J.: Experience-centered design: designers, users, and communities in dialogue. Synth. Lect. Hum. Centered Inf. 3, 1–123 (2010)CrossRefGoogle Scholar
  2. 2.
    Pine, B.J., Gilmore, J.H.: Welcome to the experience economy. Harvard Bus. Rev. 76, 97–105 (1998)Google Scholar
  3. 3.
    Liu, B.: Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. 5, 1–167 (2012)CrossRefGoogle Scholar
  4. 4.
    Nielsen, F.Å.: A new ANEW: evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903 (2011)
  5. 5.
    Plutchik, R., Kellerman, H.: The Measurement of Emotions. Academic Press, San Diego (2013)Google Scholar
  6. 6.
    Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl. Based Syst. 89, 14–46 (2015)CrossRefGoogle Scholar
  7. 7.
    Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5, 1093–1113 (2014)CrossRefGoogle Scholar
  8. 8.
    Siemens, G., Long, P.: Penetrating the fog: analytics in learning and education. EDUCAUSE Rev. 46, 30 (2011)Google Scholar
  9. 9.
    Clow, D.: An overview of learning analytics. Teach. High. Educ. 18, 683–695 (2013)CrossRefGoogle Scholar
  10. 10.
    Gašević, D., Dawson, S., Siemens, G.: Let’s not forget: Learning analytics are about learning. TechTrends. 59, 64–71 (2015)Google Scholar
  11. 11.
    Khalil, M., Kastl, C., Ebner, M.: Portraying MOOCs learners: a clustering experience using learning analytics. In: The European Stakeholder Summit on Experiences and Best Practices in and Around MOOCs (EMOOCS 2016), Graz, Austria, pp. 265–278 (2016)Google Scholar
  12. 12.
    Voigt, C., Swatman, P.M.C.: Online case discussions – tensions in activity systems. In: Presented at the 6th IEEE International Conference on Advanced Learning Technologies (ICALT), 5–7 July (2006)Google Scholar
  13. 13.
    Sproll, S., Peissner, M., Sturm, C.: From product concept to user experience: exploring UX potentials at early product stages. In: Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries, pp. 473–482. ACM (2010)Google Scholar
  14. 14.
    Brown, A.: A dynamic model of occupational identity formation. In: Brown, A. (Ed.) Promoting Vocational Education and Training: European Perspectives. pp. 59–67. University of Tampere Press, Tampere (1997)Google Scholar
  15. 15.
    Rudd, J., Stern, K., Isensee, S.: Low vs. high-fidelity prototyping debate. Interactions 3, 76–85 (1996)CrossRefGoogle Scholar
  16. 16.
    Bussolon, S.: The X factor. In: Marcus, A. (ed.) DUXU 2016. LNCS, vol. 9746, pp. 15–24. Springer, Cham (2016). doi: 10.1007/978-3-319-40409-7_2 CrossRefGoogle Scholar
  17. 17.
    Carroll, J.M.: Making Use: Scenario-Based Design of Human-Computer Interactions. MIT Press, Cambridge (2000)CrossRefGoogle Scholar
  18. 18.
    Hassenzahl, M.: Experience design: technology for all the right reasons. Synth. Lect. Hum. Centered Inf. 3, 1–95 (2010)CrossRefGoogle Scholar
  19. 19.
    Asaro, P.M.: Transforming society by transforming technology: the science and politics of participatory design. Account. Manage. Inf. Technol. 10, 257–290 (2000)Google Scholar
  20. 20.
    Rettig, M.: Prototyping for tiny fingers. Commun. ACM 37, 21–27 (1994)CrossRefGoogle Scholar
  21. 21.
    Kankainen, A.: UCPCD: user-centered product concept design. In: Proceedings of the 2003 Conference on Designing for User Experiences, pp. 1–13. ACM (2003)Google Scholar
  22. 22.
    Carroll, J.M., Chin, G., Rosson, M.B., Neale, D.C.: The development of cooperation: five years of participatory design in the virtual school. In: Proceedings of the 3rd Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pp. 239–251. ACM (2000)Google Scholar
  23. 23.
    Soller, A., Ogata, H., Hesse, F.: Design, modeling, and analysis of collaborative learning. In: The Role of Technology in CSCL, pp. 13–20 (2007)Google Scholar
  24. 24.
    Plass, J.L., Kaplan, U.: Emotional design in digital media for learning. In: Emotions, Technology, Design, and Learning, pp. 131–162 (2015)Google Scholar
  25. 25.
    Regan, K., Evmenova, A., Baker, P., Jerome, M.K., Spencer, V., Lawson, H., Werner, T.: Experiences of instructors in online learning environments: Identifying and regulating emotions. Internet High. Educ. 15, 204–212 (2012)CrossRefGoogle Scholar
  26. 26.
    Plutchik, R.: Emotions: a general psychoevolutionary theory. Approaches Emot. 1984, 197–219 (1984)Google Scholar
  27. 27.
    Callon, M., Courtial, J.-P., Turner, W.A., Bauin, S.: From translations to problematic networks: an introduction to co-word analysis. Soc. Sci. Inf. 22, 191–235 (1983)CrossRefGoogle Scholar
  28. 28.
    Suero Montero, C., Suhonen, J.: Emotion analysis meets learning analytics: online learner profiling beyond numerical data. In: Proceedings of the 14th Koli Calling International Conference on Computing Education Research, pp. 165–169. ACM (2014)Google Scholar
  29. 29.
    Ferguson, R., Brasher, A., Clow, D., Griffiths, D., Drachsler, H.: Learning analytics: visions of the future. In: 6th International Learning Analytics and Knowledge (LAK) Conference, April 25–29 , Edinburgh, Scotland (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christian Voigt
    • 1
  • Barbara Kieslinger
    • 1
  • Teresa Schäfer
    • 1
  1. 1.Zentrum Fuer Soziale InnovationTechnology and KnowledgeViennaAustria

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