Advertisement

Utilising Learning Analytics for Study Success: Reflections on Current Empirical Findings

  • Dirk IfenthalerEmail author
  • Dana-Kristin Mah
  • Jane Yin-Kim Yau
Chapter

Abstract

The success of learning analytics in improving higher education students’ learning has yet to be proven systematically and based on rigorous empirical findings. Only a few works have tried to address this but limited evidence is shown. This chapter aims to form a critical reflection on empirical evidence demonstrating how learning analytics have been successful in facilitating study success in continuation and completion of students’ university courses. We present a critical reflection on empirical evidence linking study success and LA. Literature review contributions to learning analytics were first analysed, followed by individual experimental case studies containing research findings and empirical conclusions. Findings are reported focussing on positive evidence on the use of learning analytics to support study success, insufficient evidence on the use of learning analytics and link between learning analytics and intervention measures to facilitate study success.

Keywords

Study success Learning analytics Personalised learning Data privacy and ethics Adaptive learning 

Notes

Acknowledgements

The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany (BMBF, project number 16DHL1038).

References

  1. Bote-Lorenzo, M., & Gomez-Sanchez, E. (2017). Predicting the decrease of engagement indicators in a MOOC. In International Conference on Learning Analytics & Knowledge, Vancouver, Canada.Google Scholar
  2. Buckingham Shum, S., & McKay, T. A. (2018). Architecting for learning analytics. Innovating for sustainable impact. Educause Review, 53(2), 25–37.Google Scholar
  3. Carvalho da Silva, J., Hobbs, D., & Graf, S. (2014). Integrating an at-risk student model into learning management systems. In Nuevas Ideas en Informatica Educativa.Google Scholar
  4. Drachsler, H., & Greller, W. (2016). Privacy and analytics - It's a DELICATE issue. A checklist for trusted learning analytics. Paper presented at the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, UK.Google Scholar
  5. Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., … Vuorikari, R. (2016). Research evidence on the use of learning analytics – Implications for education policy. In R. Vuorikari & J. Castano Munoz (Eds.). Joint Research Centre Science for Policy Report.Google Scholar
  6. Ferguson, R. & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. In International Conference on Learning Analytics & Knowledge, Vancouver, Canada.Google Scholar
  7. Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. Internet and Higher Education, 28, 68–84.CrossRefGoogle Scholar
  8. Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.  https://doi.org/10.1007/s11528-014-0822-x CrossRefGoogle Scholar
  9. Grawemeyer, B., Mavrikis, M., Holmes, W., Gutierrez-Santos, S., Wiedmann, M., & Rummel, N. (2016). Determination of off-task/on-task behaviours. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, UK.Google Scholar
  10. Hlosta, M., Zdrahal, Z., & Zendulka, J. (2017). Ouroboros: Early identification of at-risk students without models based on legacy data. In Proceedings of the Seventh International Conference on Learning Analytics & Knowledge, Vancouver, Canada.Google Scholar
  11. 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
  12. Ifenthaler, D. (2015). Learning analytics. In J. M. Spector (Ed.), The SAGE encyclopedia of educational technology (Vol. 2, pp. 447–451). Thousand Oaks, CA: Sage.Google Scholar
  13. Ifenthaler, D. (2017a). Are higher education institutions prepared for learning analytics? TechTrends, 61(4), 366–371.  https://doi.org/10.1007/s11528-016-0154-0 CrossRefGoogle Scholar
  14. Ifenthaler, D. (2017b). Learning analytics design. In L. Lin & J. M. Spector (Eds.), The sciences of learning and instructional design. Constructive articulation between communities (pp. 202–211). New York, NY: Routledge.CrossRefGoogle Scholar
  15. Ifenthaler, D., Gibson, D. C., & Dobozy, E. (2018). Informing learning design through analytics: Applying network graph analysis. Australasian Journal of Educational Technology, 34(2), 117–132.  https://doi.org/10.14742/ajet.3767 CrossRefGoogle Scholar
  16. Ifenthaler, D., Greiff, S., & Gibson, D. C. (2018). Making use of data for assessments: Harnessing analytics and data science. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.), International handbook of IT in primary and secondary education (2nd ed., pp. 649–663). New York, NY: Springer.Google Scholar
  17. Ifenthaler, D., & Schumacher, C. (2016). 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
  18. 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
  19. 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
  20. Kilis, S., & Gülbahar, Y. (2016). Learning analytics in distance education: A systematic literature review. Paper presented at the 9th European Distance and E-learning Network (EDEN) Research Workshop, Oldenburg, Germany.Google Scholar
  21. Mah, D.-K. (2016). Learning analytics and digital badges: Potential impact on student retention in higher education. Technology, Knowledge and Learning, 21(3), 285–305.  https://doi.org/10.1007/s10758-016-9286-8 CrossRefGoogle Scholar
  22. Manai, O., Yamada, H., & Thorn, C. (2016). Real-time indicators and targeted supports: Using online platform data to accelerate student learning. In International Conference on Learning Analytics & Knowledge, UK, 2016.Google Scholar
  23. Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., … Nesbit, J. C. (2016). What if learning analytics were based on learning science? Australasian Journal of Educational Technology, 32(6), 1–18.  https://doi.org/10.14742/ajet.3058 CrossRefGoogle Scholar
  24. McLoughlin, C., & Lee, M. J. W. (2010). Personalized and self regulated learning in the Web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1), 28–43.CrossRefGoogle Scholar
  25. Papamitsiou, Z., & Economides, A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.Google Scholar
  26. 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.CrossRefGoogle Scholar
  27. Robinson, C., Yeomans, M., Reich, J., Hulleman, C., & Gehlbach, H. (2016). Forecasting student achievement in MOOCs with natural language processing. In International Conference on Learning Analytics & Knowledge, UK.Google Scholar
  28. Sarrico, C. S. (2018). Completion and retention in higher education. In J. C. Shin & P. Teixeira (Eds.), Encyclopedia of international higher education systems and institutions. Dordrecht, The Netherlands: Springer.Google Scholar
  29. Schumacher, C., & Ifenthaler, D. (2018a). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407.  https://doi.org/10.1016/j.chb.2017.06.030 CrossRefGoogle Scholar
  30. Schumacher, C., & Ifenthaler, D. (2018b). The importance of students’ motivational dispositions for designing learning analytics. Journal of Computing in Higher Education, 30, 599.  https://doi.org/10.1007/s12528-018-9188-y CrossRefGoogle Scholar
  31. Sclater, N., & Mullan, J. (2017). Learning analytics and student success – Assessing the evidence. Bristol, UK: JISC.Google Scholar
  32. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.  https://doi.org/10.1177/0002764213479366 CrossRefGoogle Scholar
  33. Suchithra, R., Vaidhehi, V., & Iyer, N. E. (2015). Survey of learning analytics based on purpose and techniques for improving student performance. International Journal of Computer Applications, 111(1), 22–26.CrossRefGoogle Scholar
  34. Tickle, L. (2015). How universities are using data to stop students dropping out. Guardian. Retrieved from https://www.theguardian.com/guardian-professional/2015/jun/30/how-universities-are-using-data-to-stop-students-dropping-out
  35. 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
  36. West, D., Huijser, H., & Heath, D. (2016). Putting an ethical lens on learning analytics. Educational Technology Research and Development, 64(5), 903–922.  https://doi.org/10.1007/s11423-016-9464-3 CrossRefGoogle Scholar
  37. Yang, D., Sinha, T., Adamson, D., & Rose, C. (2013). “Turn on, tune in, drop out”: Anticipating student dropouts in massive open online courses. In Neural Information Processing Systems.Google Scholar
  38. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dirk Ifenthaler
    • 1
    • 2
    Email author
  • Dana-Kristin Mah
    • 1
  • Jane Yin-Kim Yau
    • 1
  1. 1.University of MannheimMannheimGermany
  2. 2.Curtin UniversityPerthAustralia

Personalised recommendations