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We Know What You Did Last Semester: Learners’ Perspectives on Screen Recordings as a Long-Term Data Source for Learning Analytics

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Addressing Global Challenges and Quality Education (EC-TEL 2020)

Abstract

Students are an important stakeholder in the learning analytics process. Their perspective on data collection is highly relevant for developing and implementing successful learning analytics, especially if the data collection goes beyond anonymized learning management system (LMS) data. We use screen recordings and LMS log files as an unobtrusive and long-term method of collecting data. This way of collecting field data is highly invasive and conflicts with the privacy of participating students. It is important not just to realize a technical solution of data collection but to keep up with implications for participants and their expectations. We evaluate our research set-up and data collection method by conducting qualitative interviews with 15 participants on how they perceived this means of data collection, and derived implications and requirements in terms of what is relevant for students, to agree on this way of data collection and which aspects can be problematic.

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References

  1. Blikstein, P.: Using learning analytics to assess students’ behavior in open-ended programming tasks. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 110–116. ACM, New York, NY, USA (2011). https://doi.org/10.1145/2090116.2090132

  2. Breiter, A., Hepp, A.: Die Komplexität der Datafizierung: zur Herausforderung, digitale Spuren in ihrem Kontext zu analysieren. In: Katzenbach, C., Pentzold, C., Kannengießer, S., Adolf, M., Taddicken, M. (Hrsg.) Neue Komplexitäten für Kommunikationsforschung und Medienanalyse: Analytische Zugänge und empirische Studien, Berlin, pp. 27–48 (2018)

    Google Scholar 

  3. Brown, A.: Music Technology and Education: Amplifying Musicality. Routledge, Oxford (2014)

    Book  Google Scholar 

  4. Bryman, A.: Social Research Methods. Oxford University Press (2015)

    Google Scholar 

  5. Burn, A.: Liber Ludens: Games, play and learning. In: Haythornthwaite, A., Fransman, C., Kazmer, M. (Hg.) The Sage Handbook of e-learning Research. Sage, London (2016)

    Google Scholar 

  6. Cassidy, G.G., Paisley, A.M.J.: Music-games: A case study of their impact. Res. Stud. Music Educ. 35(1), 119–138 (2013)

    Article  Google Scholar 

  7. Clow, D.: An overview of learning analytics. Teach. High. Educ. 18(6), 683–695 (2013)

    Article  Google Scholar 

  8. Directive (EU) 2016/680 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal of-fences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision 2008/977/JHAOJ L 119, pp. 89–131, May 4, 2016

    Google Scholar 

  9. Drachsler, H., Greller, W.: The pulse of learning analytics understandings and expectations from the stakeholders. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 120–129 (2012). https://doi.org/10.1145/2330601.2330634

  10. Green, L.: How popular musicians learn: a way ahead for music education. Ashgate, Aldershot (2002)

    Google Scholar 

  11. Green, L.: Music, Informal Learning and the School: A New Classroom Pedagogy. Ashgate, Aldershot (2008)

    Google Scholar 

  12. Huang, S., Fang, N.: Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Comput. Educ. 61, 133–145 (2013)

    Article  Google Scholar 

  13. Ifenthaler, D., Schumacher, C.: Student perceptions of privacy principles for learning analytics. Education Tech. Research Dev. 64(5), 923–938 (2016). https://doi.org/10.1007/s11423-016-9477-y

    Article  Google Scholar 

  14. Jaffurs, S.E.: The impact of informal learning practices in the classroom, oh how I learned how to teach from a garage band. Int. J. Music Educ. 22(3), 189–200 (2004)

    Article  Google Scholar 

  15. Karlsen, S.: BoomTown music education and the need for authenticity – In-formal learning put into practice in Swedish post-compulsory music education. Br. J. Music Educ. 27(1), 35–46 (2010)

    Article  Google Scholar 

  16. Krieter, P., Breiter, A.: Analyzing mobile application usage: generating log files from mobile screen recordings. In: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 1–10 (2018)

    Google Scholar 

  17. Krieter, P., Breiter, A.: Track every move of your students: Log files for learning analytics from mobile screen recordings. In: DeLFI 2018-Die 16. E-Learning Fachtagung Informatik (2018)

    Google Scholar 

  18. Krieter, P.: Mobile screen recordings to log file (2018). https://github.com/pkrieter/mobile-screen-recordings-to-log-file/

  19. Krieter, P.: Can I record your screen? Mobile screen recordings as a long-term data source for user studies. In: Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia, pp. 1–10 (2019)

    Google Scholar 

  20. Mayring, P.: Qualitative Inhaltsanalyse. Grundlagen und Techniken, 7th edn. (1st edn., 1983). Weinheim: Deutscher Studien Verlag (2000)

    Google Scholar 

  21. McPherson, J., Tong, H.L., Fatt, S.J., Liu, D.Y.T.: Student perspectives on data provision and use: Starting to unpack disciplinary differences. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 158–167 (2016). https://doi.org/10.1145/2883851.2883945

  22. Papamitsiou, Z., Economides, A.: Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educ. Technol. Soc. 17(4), 49–64 (2014)

    Google Scholar 

  23. Pardo, A., Kloos, C.D.: Stepping out of the Box: Towards analytics outside the learning management system. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 163–167. ACM, New York, NY, USA (2011)

    Google Scholar 

  24. Perrotta, C., Williamson, B.: The social life of learning analytics: cluster analysis and the ‘performance’ of algorithmic education. Learn. Media Technol. 43, 1–14 (2016)

    Google Scholar 

  25. Portowitz, A., Peppler, K.A., Downton, M.: In harmony: A technology-based music education model to enhance musical understanding and general learning skills. Int. J. Music Educ. 32(2), 242–260 (2014)

    Article  Google Scholar 

  26. Prinsloo, P., Slade, S.: An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 240–244 (2013). https://doi.org/10.1145/2460296.2460344

  27. Prinsloo, P., Slade, S.: Student privacy self-management: Implications for learning analytics. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, pp. 83–92 (2015). https://doi.org/10.1145/2723576.2723585

  28. Reeves, B., et al.: Screenomics: a framework to capture and analyze personal life experiences and the ways that technology shapes them. Hum. Comput. Interact. 0(0), 1–52 (2019). https://doi.org/10.1080/07370024.2019.1578652

  29. Reigeluth, T.B.: Why data is not enough: Digital traces as control of self and self-control. Surveill. Soc. 12, 243–254 (2014)

    Article  Google Scholar 

  30. Schumacher, C., Ifenthaler, D.: Features students really expect from learning analytics. Comput. Hum. Behav. 78, 397–407 (2018). https://doi.org/10.1016/j.chb.2017.06.030

    Article  Google Scholar 

  31. Slade, S., Prinsloo, P: Student perspectives on the use of their data: Between intrusion, surveillance and care. Euro. J. Open Dist. E-Learn. 18(1), Article 1 (2015). https://www.eurodl.org/?p=special&sp=articles&inum=6&article=673&article=679

  32. Tang, J.C., et al. Unobtrusive but invasive: using screen recording to collect field data on computer-mediated interaction. In: Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work. ACM (2006)

    Google Scholar 

  33. Tempelaar, D.T., Rienties, B., Giesbers, B.: In search for the most informative data for feedback generation: Learning analytics in a data-rich con-text. Comput. Hum. Behav. 47, 157–167 (2015)

    Article  Google Scholar 

  34. Wolf, K.D., Rummler, K.: Mobile learning with videos in online communities: The example of draufhaber.tv. MedienPädagogik, Themenheft 19 “Mobile Learning in Widening Contexts: Concepts and Cases” (2011)

    Google Scholar 

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Acknowledgements

We would like to thank our participants for their cooperation and the opportunity to use their data for our research and also to make themselves available for interviews. This work was funded by the German Ministry of Education and Research (reference number: 01JKD1709B).

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Correspondence to Philipp Krieter .

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Krieter, P., Viertel, M., Breiter, A. (2020). We Know What You Did Last Semester: Learners’ Perspectives on Screen Recordings as a Long-Term Data Source for Learning Analytics. In: Alario-Hoyos, C., Rodríguez-Triana, M.J., Scheffel, M., Arnedillo-Sánchez, I., Dennerlein, S.M. (eds) Addressing Global Challenges and Quality Education. EC-TEL 2020. Lecture Notes in Computer Science(), vol 12315. Springer, Cham. https://doi.org/10.1007/978-3-030-57717-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-57717-9_14

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