Advertisement

Learning Analytics

Spezielle Forschungsmethoden in der Bildungstechnologie
  • Dirk IfenthalerEmail author
  • Hendrik Drachsler
Living reference work entry
Part of the Springer Reference Psychologie book series (SRP)

Zusammenfassung

Der Forschungsbereich um Learning Analytics hat sich in den vergangenen fünf Jahren rasant entwickelt. Learning Analytics verwenden statische Daten von Lernenden und dynamische in Lernumgebungen gesammelte Daten über Aktivitäten (und den Kontext) des Lernenden, um diese in nahezu Echtzeit zu analysieren und zu visualisieren, mit dem Ziel der Modellierung und Optimierung von Lehr-Lernprozessen und Lernumgebungen. Learning Analytics kann sowohl auf Kursebene als auch auf curricularer Ebene sowie institutionsweit bzw. -übergreifend implementiert werden. Trotz der großen Aufmerksamkeit für das Thema Learning Analytics in der Wissenschaft steckt die praktische Anwendung von Learning Analytics noch in den Anfängen. Dennoch müssen Bildungsinstitutionen bereits jetzt Kapazitäten entwickeln, um der aktuellen Entwicklung folgen zu können. Es bleibt zu erwarten, dass neben datenschutzrechtlichen Regelungen in der Verwendung von Learning Analytics auch technische Standards zum Austausch von Daten aus dem Bildungskontext entwickelt werden.

Schlüsselwörter

Learning Analytics Educational Data Mining Datenanalyse Big Data Academic Analytics 

Literatur

  1. Bellin-Mularski, N., & Ifenthaler, D. (2014). Learning analytics: Datenanalyse zur Unterstützung von Lehren und Lernen in der Schule. SchulVerwaltung NRW, 25(11), 300–303.Google Scholar
  2. Berg, A., Scheffel, M., Ternier, S., Drachsler, H., & Specht, M. (2016). Dutch cooking with xAPI recipes – The good, the bad, and the consistent. 16th IEEE international conference on advancing learning technologies (ICALT 2016). Austin.Google Scholar
  3. Berland, M., Baker, R. S. J. d., & Bilkstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1–2), 205–220.  https://doi.org/10.1007/s10758-014-9223-7.CrossRefGoogle Scholar
  4. Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology & Society, 15(3), 3–26.Google Scholar
  5. d’Aquin, M., Dietze, S., Herder, E., Drachsler, H., & Taibi, D. (2014). Using linked data in learning analytics. eLearning papers, 36. http://www.openeducationeuropa.eu/en/download/file/fid/33993. Zugegriffen am 12.10.2017.
  6. Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2017). Learning pulse: A machine learning approach for predicting performance in self-regulated learning using multimodal data. 7th learning analytics and knowledge conference 2017. Vancouver.Google Scholar
  7. Drachsler, H. & Greller, W. (2016). Privacy and analytics – It’s a DELICATE issue. A checklist to establish trusted learning analytics. 6th learning analytics and knowledge conference 2016. (S. 89–98), 25–29 April 2016. Edinburgh/New York: ACM.Google Scholar
  8. Drachsler, H., & Kalz, M. (2016). The MOOC and learning analytics innovation cycle (MOLAC): A reflective summary of ongoing research and its challenges. Journal of Computer Assisted Learning, 32(3), 281–290.  https://doi.org/10.1111/jcal.12135.CrossRefGoogle Scholar
  9. Drachsler, H., Stoyanov, S., & Specht, M. (2014). The impact of learning analytics on the Dutch education system. Presentation given at the 4th international conference on learning analytics and knowledge. Indianapolis.Google Scholar
  10. Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of recommender systems to support learning. In F. Rici, L. Rokach & B. Shapira (Hrsg.), 2nd handbook on recommender systems (S. 421–451). USA: Springer.  https://doi.org/10.1007/978-1-4899-7637-6_12.CrossRefGoogle Scholar
  11. Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Educational Technology & Society, 15(3), 58–76.Google Scholar
  12. Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64.CrossRefGoogle Scholar
  13. Fazeli, S., Loni, B., Drachsler, H., & Sloep, P. (2014). Which recommender system can best fit social learning platforms? Presentation given at the 9th European conference on technology enhanced learning (EC-TEL2014). Graz.Google Scholar
  14. Gibson, D. C., & Ifenthaler, D. (2017). Preparing the next generation of education researchers for big data in higher education. In B. Kei Daniel (Hrsg.), Big data and learning analytics: Current theory and practice in higher education (S. 29–42). New York: Springer.CrossRefGoogle Scholar
  15. Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics Educational Technology & Society, 15(3), 42–57.Google Scholar
  16. Ifenthaler, D. (2012). Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios. Journal of Educational Technology & Society, 15(1), 38–52.Google Scholar
  17. Ifenthaler, D. (2014). AKOVIA: Automated knowledge visualization and assessment. Technology, Knowledge and Learning, 19(1–2), 241–248.  https://doi.org/10.1007/s10758-014-9224-6.CrossRefGoogle Scholar
  18. Ifenthaler, D. (2015). Learning analytics. In J. M. Spector (Hrsg.), The SAGE encyclopedia of educational technology (Bd. 2, S. 447–451). Thousand Oaks: Sage.Google Scholar
  19. Ifenthaler, D. (2017). Are higher education institutions prepared for learning analytics? TechTrends, 61(4), 366–371.  https://doi.org/10.1007/s11528-016-0154-0.CrossRefGoogle Scholar
  20. Ifenthaler, D., & Schumacher, C. (2016a). Learning Analytics im Hochschulkontext. WiSt – Wirtschaftswissenschaftliches Studium, 4, 172–177.Google Scholar
  21. Ifenthaler, D., & Schumacher, C. (2016b). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923–938.  https://doi.org/10.1007/s11423-016-9477-y.CrossRefGoogle Scholar
  22. Ifenthaler, D., & Tracey, M. W. (2016). Exploring the relationship of ethics and privacy in learning analytics and design: Implications for the field of educational technology. Educational Technology Research and Development, 64(5), 877–880.  https://doi.org/10.1007/s11423-016-9480-3.CrossRefGoogle Scholar
  23. Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1–2), 221–240.  https://doi.org/10.1007/s10758-014-9226-4.CrossRefGoogle Scholar
  24. Ifenthaler, D., Bellin-Mularski, N., & Mah, D.-K. (2015). Internet: Its impact and its potential for learning and instruction. In J. M. Spector (Hrsg.), The SAGE encyclopedia of educational technology (Bd. 1, S. 416–422). Thousand Oaks: Sage.Google Scholar
  25. Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough. Pitfalls of learning analytics dashboards in the educational practise. 12th European conference on technology-enhanced learning. Tallinn, 12–15 Sept. 2017.Google Scholar
  26. Johnson, L., Adams Becker, S., Cummins, M., Freeman, A., Ifenthaler, D., & Vardaxis, N. (2013). Technology outlook for Australian tertiary education 2013–2018: An NMC horizon project regional analysis. Austin: The New Media Consortium.Google Scholar
  27. Long, P. D., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 31–40.Google Scholar
  28. Macfadyen, L., & Dawson, S. (2012). Numbers are not enough. Why e-Learning analytics failed to inform an institutional strategic plan. Educational Technology & Society, 15(3), 149–163.Google Scholar
  29. New York Times. (2014). InBloom Student Data Repository to Close, 21 April 2014. http://bits.blogs.nytimes.com/2014/04/21/inbloom-student-data-repository-to-close/?_r=0. Zugegriffen am 12.10.2017.
  30. Pijeira-díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016). Investigating collaborative learning success with physiological coupling indices based on electrodermal activity. 6th learning analytics and knowledge conference 2016. 25–29 April 2016. Edinburgh.Google Scholar
  31. Pistilli, M. D., & Arnold, K. E. (2010). Purdue signals: Mining real-time academic data to enhance student success. About campus: Enriching the student learning experience, 15(3), 22–24.Google Scholar
  32. Scheffel, M. (2017). The evaluation framework for learning analytics. Doctoral thesis. Heerlen: Open Universiteit (Welten Institute, Research centre for learning, Teaching and technology). http://dspace.ou.nl/handle/1820/8259.
  33. Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality indicators for learning analytics. Educational Technology & Society, 17(4), 117–132.Google Scholar
  34. Scheffel, M., Drachsler, H., de Kraker, J., Kreijns, K., Slootmaker, A., & Specht, M. (2016). Widget, widget on the wall, am I performing well at all? IEEE Transactions on Learning Technologies, 10(1), 42–52.  https://doi.org/10.1109/TLT.2016.2622268.CrossRefGoogle Scholar
  35. Scheffel, M., Drachsler, H., Kreijns, K., de Kraker, J., & Specht, M. (2017a). Widget, widget as you lead, I am performing well indeed! – Using results from a formative offline study to inform an empirical online study about a learning analytics widget in a collaborative learning environment. Proceedings of the (LAK’17). Vancouver: ACM.Google Scholar
  36. Scheffel, M., Drachsler, H., Toisoul, C., Ternier, S., & Specht, M. (2017b). The Proof of the pudding: Examining validity and reliability of the evaluation framework for learning analytics. In E. Lavoué, H. Drachsler, K. Verbert, J. Broisin & M. Pérez-Sanagustín (Hrsg.), Data driven approaches in digital education. Proceedings of the 12th European conference on technology enhanced learning (EC-TEL 2017), LNCS (Bd. 10474, S. 194–208). Berlin/Heidelberg: Springer.Google Scholar
  37. Siemens, G., Dawson, S., & Lynch, G. (2014). Improving the quality and productivity of the higher education sector – Policy and strategy for systems-level deployment of learning analytics. Canberra, Australia: Office of Learning and Teaching, Australian Government. http://solaresearch.org/Policy_Strategy_Analytics.pdf.
  38. Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53–74.CrossRefGoogle Scholar
  39. Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3), 133–148.Google Scholar
  40. Zimmerman, B. J. (1995). Self-regulation involves more than metacognition: A social cognitive perspective. Educational Psychologist, 30(4), 217–221.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2018

Authors and Affiliations

  1. 1.Learning, Design and TechnologyUniversität MannheimMannheimDeutschland
  2. 2.Deutsches Institut für Internationale Pädagogsche ForschungGoethe Universität Frankfurt am MainFrankfurtDeutschland

Personalised recommendations