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Technology, Knowledge and Learning

, Volume 23, Issue 1, pp 1–20 | Cite as

Are We on Our Way to Becoming a “Helicopter University”? Academics’ Views on Learning Analytics

  • Joel A. Howell
  • Lynne D. Roberts
  • Kristen Seaman
  • David C. Gibson
Original research

Abstract

Higher education institutions are developing the capacity for learning analytics. However, the technical development of learning analytics has far exceeded the consideration of ethical issues around learning analytics. We examined higher education academics’ knowledge, attitudes, and concerns about the use of learning analytics though four focus groups (N = 35). Thematic analysis of the focus group transcripts identified five key themes. The first theme, ‘Facilitating learning’, represents academics’ perceptions that, while currently unrealized, there could be several benefits to learning analytics that would help their students. Three themes; ‘Where are the ethics?’, ‘What about the students!’, and ‘What about me!’ represented academics’ perceptions of how learning analytics could pose some considerable difficulties within a higher education context. A final theme ‘Let’s move forward together’ reflected that despite some challenges and concerns about learning analytics, academics perceived scope for learning analytics to be beneficial if there is collaboration between academics, students, and the university. The findings highlight the need to include academics in the development of learning analytics policies and procedures to promote the suitability and widespread adoption of learning analytics in the higher education sector.

Keywords

Learning analytics Higher education Academic attitudes Big data 

References

  1. Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16, 227–247.CrossRefGoogle Scholar
  2. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. Paper presented at the Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. Retrieved from http://s3.amazonaws.com/.
  3. Atif, A., Bilgin, A., & Richards, D. (2015). Student preferences and attitudes to the use of early alerts. Paper presented at Twenty-first Americas Conference on Information Systems, Puerto Rico. Retrieved from http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1297&context=amcis2015.
  4. Beattie, S., Woodley, C., & Souter, K. (2014). Creepy analytics and learner data rights. In B. Hegarty, J. McDonld, & S. K. Loke (Eds.), Rhetoric and Reality: Critical Perspectives on Educational Technology. Proceedings Ascilite, pp. 421–425. Retrieved from http://www.ascilite.org/conferences/dunedin2014/files/concisepapers/69-Beattie.pdf.
  5. Braun, V., & Clarke, V. (2006) Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101. doi: 10.1191/1478088706qp063oa.CrossRefGoogle Scholar
  6. Castleberry, A. (2014). NVivo 10 [software program]. Version 10. QSR International; 2012. American Journal of Pharmaceutical Education, 78(1), 25. doi: 10.5688/ajpe78125.CrossRefGoogle Scholar
  7. Colvin, C., Rogers, T., Wade, A., Dawson, S., Gašević, D., Buckingham Shum, S., & Fisher, J. (2015). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Sydney: Australian Office for Learning and Teaching. Retrieved from http://www.research.ed.ac.uk/portal/files/21121591/Final_Report_190615.pdf.
  8. Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In B. Hegarty, J. McDonld, & S. K. Loke (Eds.), Rhetoric and Reality: Critical Perspectives on Educational Technology. Proceedings Ascilite, pp. 629–433. Retrieved from http://ascilite.org/conferences/dunedin2014/files/concisepapers/223-Corrin.pdf.
  9. Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers. In Electric Dreams. 30th Ascilite Conference Proceedings 2013, pp. 201–205. Retrieved from http://ascilite.org/conferences/sydney13/program/papers/Corrin.pdf.
  10. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1002.CrossRefGoogle Scholar
  11. de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., et al. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46, 1175–1188. doi: 10.1111/bjet.12212.CrossRefGoogle Scholar
  12. Dede, C., Ho, A., & Mitros, P. (2016). Big data analysis in higher education: Promises and pitfalls. EDUCAUSE Review, 51(5), 22–34.Google Scholar
  13. Drachsler, H., & Greller, W. (2012). 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). ACM. Retrieved from http://dspace.learningnetworks.org/.
  14. Draschler, H., & Greller, W. (2016). Privacy and analytics: It’s a delicate issue a checklist for trusted learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 89–98). ACM. doi: 10.1145/2883851.2883893.
  15. 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
  16. Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. doi: 10.1007/s11528-014-0822-x.CrossRefGoogle Scholar
  17. Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57. Retrieved from http://www.jstor.org/stable/jeductechsoci.15.3.42.
  18. Guest, G., Namey, E., & McKenna, K. (2016). How many focus groups are enough? Building an evidence base for nonprobability sample sizes. Field Methods, 28, 1–20. doi: 10.1177/1525822x16639015.Google Scholar
  19. Kerly, A., Ellis, R., & Bull, S. (2008). CALMsystem: A conversational agent for learner modelling. Knowledge-Based Systems, 21(3), 238–246. doi: 10.1016/j.knosys.2007.11.015.CrossRefGoogle Scholar
  20. Kosba, E., Dimitrova, V., & Boyle, R. (2005). Using student and group models to support teachers in web-based distance education. In International Conference on User Modeling (pp. 124–133). Berlin: Springer.Google Scholar
  21. Kregor, G., Breslin, M., & Fountain, W. (2012). Experience and beliefs of technology users at an Australian university: Keys to maximising e-learning potential. Australasian Journal of Educational Technology, 28, 1382–1404. doi: 10.14742/ajet.777.CrossRefGoogle Scholar
  22. Lawson, C., Beer, C., Rossi, D., Moore, T., & Fleming, J. (2016). Identification of “at risk” students using learning analytics: The ethical dilemmas of intervention strategies in a higher education institution. Educational Technology Research and Development, 64(5), 957–968. doi: 10.1007/s11423-016-9459-0.CrossRefGoogle Scholar
  23. Lincoln, Y. S., & Guba, E. (1985). Naturalistic enquiry. Beverly Hills, CA: Sage.Google Scholar
  24. Miles, C. (2015). Australian university teachers’ engagement with learning analytics: Still early days. In EdMedia: World Conference on Educational Media and Technology (Vol. 2015, No. 1, pp. 108–108).Google Scholar
  25. Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45, 438–450. doi: 10.1111/bjet.12.CrossRefGoogle Scholar
  26. Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The International Review of Research in Open and Distributed Learning, 15(4), 306–331. doi: 10.19173/irrodl.v15i4.1881.CrossRefGoogle Scholar
  27. Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room—the obligation to act. In LAK’17 Proceedings of the Seventh International Learning Analytics and Knowledge Conference (pp. 46–65). ACM. doi: 10.1145/3027385.3027406.
  28. Reed, P. (2012). Awareness, attitudes and participation of teaching staff towards the open content movement in one university. Research in Learning Technology. doi: 10.3402/rlt.v20i0.18520.Google Scholar
  29. Reed, P. (2014). Staff experience and attitudes towards technology enhanced learning initiatives in one Faculty of Health and Life Sciences. Research in Learning Technology. doi: 10.3402/rlt.v22.22770.Google Scholar
  30. Reimers, G., Neovesky, A., & der Wissenschaften, A. (2015). Student focused dashboards. Paper presented at the 7th International Conference on Computer Supported Education. Retrieved from http://s3.amazonaws.com.
  31. Roberts, L., Chang, V., & Gibson, D. (2016a). Ethical considerations in adopting a university- and system-wide approach to data and learning analytics. In B. Kei Daniel (Ed.), Big data and learning analytics in higher education (pp. 89–108). Switzerland: Springer.Google Scholar
  32. Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. C. (2016b). Student attitudes toward learning analytics in higher education: “the fitbit version of the learning world”. Frontiers in Psychology, 7, 1959. doi: 10.3389/fpsyg.2016.01959.Google Scholar
  33. Roberts, L. D., Howell, J. A., & Seaman, K. (2017). Give me a customizable dashboard: Personalized learning analytics dashboards in higher education. Technology, Knowledge and Learning. doi: 10.1007/s10758-017-9316-1.Google Scholar
  34. Santos, J. L., Verbert, K., Govaerts, S., & Duval, E. (2013). Addressing learner issues with StepUp!: An evaluation. Paper presented at the Proceedings of the Third International Conference on Learning Analytics and Knowledge. Retrieved from https://lirias.kuleuven.be/bitstream/123456789/396691/1/lak-santos-final.pdf.
  35. Scholes, V. (2016). The ethics of using learning analytics to categorize students on risk. Educational Technology Research and Development, 64, 939–955. doi: 10.1007/s11423-016-9458-1.CrossRefGoogle Scholar
  36. Sclater, N. (Producer) (2015a). Jisc learning analytics architecture. Retrieved from https://www.youtube.com/watch?v=PoH0NXUbrjw.
  37. Sclater, N. (2015b). What do students want from a learning analytics app. Retrieved from http://analytics.jiscinvolve.org.
  38. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57, 1380–1400. doi: 10.1177/0002764213498851.CrossRefGoogle Scholar
  39. Slade, S., & Prinsloo, P. (2013). Learning analytics ethical issues and dilemmas. American Behavioral Scientist, 57, 1510–1529. doi: 10.1177/0002764213479366.CrossRefGoogle Scholar
  40. Slade, S., & Prinsloo, P. (2015). Student perspectives on the use of their data: Between intrusion, surveillance and care. European Journal of Open, Distance and E-learning, 18(1), 291–300.Google Scholar
  41. Swenson, J. (2014). Establishing an ethical literacy for learning analytics. In LAK’14. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, pp. 246–250. Retireved from http://dl.acm.org/citation.cfm?id=2567613.
  42. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.CrossRefGoogle Scholar
  43. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186–204.CrossRefGoogle Scholar
  44. West, D., Huijser, H., & Heath, D. (2016). Putting an ethical lens on learning analytics. Educational Technology Research and Development, 64, 903–922. doi: 10.1007/s11423-016-9464-3.CrossRefGoogle Scholar
  45. West, D., Huijser, H., Heath, D., Lizzio, A., Toohey, D., & Miles, C. (2015). Higher education teachers’ experiences with learning analytics in relation to student retention. In T. Reiners, B. R. von Konsky, D. Gibson, V. Chang, L. Irving & K. Clarke (Eds.), Globally Connected, Digitally Enabled: Proceedings of the Ascilite 2015 Conference (pp. 296–307). Perth, 29 November–2 December.Google Scholar
  46. Willis, J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology derived from a cross continental, cross-instititutational perspective. Educational Technology Research and Development, 64, 881–901. doi: 10.1007/s11423-016-9463-4.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.School of Psychology and Speech PathologyCurtin UniversityPerthAustralia
  2. 2.Faculty of Health SciencesCurtin UniversityPerthAustralia
  3. 3.Curtin Learning and TeachingCurtin UniversityPerthAustralia

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