Skip to main content
SpringerLink
Log in

The Value of Epistemic Network Analysis in Single-Case Learning Analytics: A Case Study in Lifelong Learning

  • Conference paper
  • First Online:
Advances in Quantitative Ethnography (ICQE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1312))

Included in the following conference series:

  • 1636 Accesses

Abstract

Certain educational contexts like lifelong learning have been comparatively understudied, due to the uniqueness of such learning processes, which make it difficult to ascertain generalizable models or average intervention effects. The ability to collect large amounts of data from a single learner longitudinally, throughout their lifelong trajectory, and the use of learning analytics (LA), presents novel opportunities in this regard. However, quantitative data and models may not be enough to deeply understand such unique learning processes and their context, without the help of qualitative data and ethnographic methods of collecting and analyzing it. This paper presents an approach to understanding lifelong learning that combines both qualitative and quantitative data (which we have termed single-case learning analytics). We illustrate how these two kinds of longitudinal data can be combined and the role of Epistemic Network Analysis (ENA) in this approach, through a case study in which daily quantitative and qualitative data were gathered about a lifelong learner’s social-emotional learning (SEL), for about nine months. Our results show how qualitative data (and ENA outputs) can be used to not only close the interpretive loop but also help interpret and improve the accuracy of quantitative models of a single learner’s process and contextual influences. Along with ongoing advances in automated coding, this approach opens the door to user-facing, ENA-enhanced LA to support personalized learning over extended periods of time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Field, J.: Is lifelong learning making a difference? Research-based evidence on the impact of adult learning. In: Aspin, D.N., Chapman, J., Evans, K., Bagnall, R. (eds.) Second international handbook of lifelong learning, pp. 887–897. Springer Netherlands, Dordrecht (2012). https://doi.org/10.1007/978-94-007-2360-3_54

    Chapter  Google Scholar 

  2. Ferguson, R.: Learning analytics: drivers, developments and challenges. Int. J. Technol. Enhanced Learn. 4(5/6), 304 (2012). https://doi.org/10.1504/IJTEL.2012.051816

    Article  Google Scholar 

  3. Shaffer, D.W.: Quantitative ethnography. Lulu.com (2017)

    Google Scholar 

  4. Swiecki, Z., Shaffer, D.W.: iSENS: an integrated approach to combining epistemic and social network analyses. In: Proceedings of the Tenth International Conference on Learning Analytics and Knowledge, pp. 305–313 (2020)

    Google Scholar 

  5. Melzner, N., Greisel, M., Dresel, M., Kollar, I.: Using process mining (PM) and epistemic network analysis (ENA) for comparing processes of collaborative problem regulation. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds.) ICQE 2019. CCIS, vol. 1112, pp. 154–164. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33232-7_13

    Chapter  Google Scholar 

  6. Weissberg, R.P., Durlak, J.A., Domitrovich, C.E., Gullotta, T.P.: Social and emotional learning: past, present, and future. In: Handbook of Social and Emotional Learning: Research and Practice, pp. 3–19. The Guilford Press, New York (2015)

    Google Scholar 

  7. Learning Compass 2030 - Organisation for Economic Co-operation and Development. https://www.oecd.org/education/2030-project/teaching-and-learning/learning/learning-compass-2030/. Accessed 11 June 2020

  8. Corcoran, R.P., Cheung, A.C.K., Kim, E., Xie, C.: Effective universal school-based social and emotional learning programs for improving academic achievement: a systematic review and meta-analysis of 50 years of research. Educ. Res. Rev. 25, 56–72 (2018). https://doi.org/10.1016/j.edurev.2017.12.001

    Article  Google Scholar 

  9. Conley, C.S.: SEL in higher education. In: Handbook of Social and Emotional Learning: Research and Practice, pp. 197–212 (2015)

    Google Scholar 

  10. Akcaalan, M.: Investigation of the relationships between lifelong learning and social emotional learning. Online Submiss. 1, 14–20 (2016)

    Google Scholar 

  11. Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., Specht, M.: Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data. In: Proceedings of the Seventh International Learning Analytics and Knowledge Conference, pp. 188–197. ACM (2017)

    Google Scholar 

  12. Cherniss, C., Adler, M.: Promoting Emotional Intelligence in Organizations: Make Training in Emotional Intelligence Effective. American Society for Training and Development (2000)

    Google Scholar 

  13. Amabile, T.M., Barsade, S.G., Mueller, J.S., Staw, B.M.: Affect and creativity at work. Adm. Sci. Q. 50(3), 367–403 (2005). https://doi.org/10.2189/asqu.2005.50.3.367

  14. Amabile, T., Kramer, S.: The Progress Principle: Using Small Wins to Ignite Joy, Engagement, and Creativity at Work. Harvard Business Press, Boston (2011)

    Google Scholar 

  15. Parmentier, M., Pirsoul, T., Nils, F.: Emotional processes in career adaptation: a longitudinal study in an adult education program. Presented at the 50th International Conference of EUCEN: Times of Transition – The Role of University Lifelong Learning (2018)

    Google Scholar 

  16. Foster, A., Shah, M., Barany, A., Talafian, H.: Tracing identity exploration trajectories with quantitative ethnographic techniques: a case study. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds.) ICQE 2019. CCIS, vol. 1112, pp. 77–88. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33232-7_7

    Chapter  Google Scholar 

  17. Hod, Y., Katz, S., Eagan, B.: Refining qualitative ethnographies using epistemic network analysis: a study of socioemotional learning dimensions in a humanistic knowledge building community. Comput. Educ. 103943 (2020)

    Google Scholar 

  18. Bauer, E., et al.: Using ENA to analyze pre-service teachers’ diagnostic argumentations: a conceptual framework and initial applications. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds.) ICQE 2019. CCIS, vol. 1112, pp. 14–25. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33232-7_2

    Chapter  Google Scholar 

  19. Kazdin, A.E.: Single-Case Research Designs: Methods for Clinical and Applied Settings. Oxford University Press, Oxford (2011)

    Google Scholar 

  20. Fielding, N.G.: Triangulation and mixed methods designs: Data integration with new research technologies. J. Mixed Methods Res. 6(2), 124–136 (2012). https://doi.org/10.1177/1558689812437101

    Article  Google Scholar 

  21. Houghton, C., Casey, D., Shaw, D., Murphy, K.: Rigour in qualitative case-study research. Nurse Res. 20, 12–17 (2013)

    Article  Google Scholar 

  22. Wang, F., Hannafin, M.J.: Design-based research and technology-enhanced learning environments. Educ. Technol. Res. Dev. 53, 5–23 (2005)

    Article  Google Scholar 

  23. Sambrook, S.A., Jones, N., Doloriert, C.: Employee engagement and autoethnography: being and studying self. J. Workplace Learn. 26, 172–187 (2014)

    Article  Google Scholar 

  24. Woolston, C.: Feeling overwhelmed by academia? You are not alone. Nature 557, 129 (2018)

    Google Scholar 

  25. Choe, E.K., Lee, N.B., Lee, B., Pratt, W., Kientz, J.A.: Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1143–1152 (2014)

    Google Scholar 

  26. Walker, M.: Why We Sleep: The New Science of Sleep and Dreams. Penguin, Harmondsworth (2017)

    Google Scholar 

  27. Cirillo, F.: The pomodoro technique (the pomodoro). Agile Process. Softw. Eng. 54, 35 (2006)

    Google Scholar 

  28. Essiam, J.O., Mensah, M.E., Kudu, L.K., Gyamfi, G.D.: Influence of job stress on job satisfaction among university staff: analytical evidence from a public university in Ghana. Int. J. Econ. Commer. Manag. 3, 1–5 (2015)

    Google Scholar 

  29. Amabile, T.M., Mueller, J.S., Archambault, S.M.: Coding manual for the DENA coding scheme (Detailed Event Narrative Analysis) (2003)

    Google Scholar 

  30. Lane, J.D., Gast, D.L.: Visual analysis in single case experimental design studies: brief review and guidelines. Neuropsychol. Rehabil. 24, 445–463 (2014)

    Article  Google Scholar 

  31. Cai, Z., Siebert-Evenstone, A., Eagan, B., Shaffer, D.W., Hu, X., Graesser, A.C.: nCoder+: a semantic tool for improving recall of nCoder coding. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds.) ICQE 2019. CCIS, vol. 1112, pp. 41–54. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33232-7_4

    Chapter  Google Scholar 

  32. Harper, M., Cole, P.: Member checking: Can benefits be gained similar to group therapy. Qual. Rep. 17, 510–517 (2012)

    Google Scholar 

  33. Shaffer, D.W., Serlin, R.C.: What good are statistics that don’t generalize? Educ. Researcher 33, 14–25 (2004)

    Google Scholar 

Download references

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 669074.

Author information

Authors and Affiliations

  1. Tallinn University, 10120, Tallinn, Estonia

    Luis P. Prieto, María Jesús Rodríguez-Triana & Tobias Ley

  2. University of Wisconsin-Madison, Madison, WI, 53715, USA

    Brendan Eagan

Authors
  1. Luis P. Prieto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. María Jesús Rodríguez-Triana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tobias Ley
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brendan Eagan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luis P. Prieto .

Editor information

Editors and Affiliations

  1. University of Wisconsin–Madison, Madison, WI, USA

    Andrew R. Ruis

  2. Pepperdine University, Malibu, CA, USA

    Seung B. Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Prieto, L.P., Rodríguez-Triana, M.J., Ley, T., Eagan, B. (2021). The Value of Epistemic Network Analysis in Single-Case Learning Analytics: A Case Study in Lifelong Learning. In: Ruis, A.R., Lee, S.B. (eds) Advances in Quantitative Ethnography. ICQE 2021. Communications in Computer and Information Science, vol 1312. Springer, Cham. https://doi.org/10.1007/978-3-030-67788-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-67788-6_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67787-9

  • Online ISBN: 978-3-030-67788-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation