Abstract
From its inception, learning analytics (LA) offered the potential to be a game changer for higher education. However, accounts of its widespread implementation, especially by teachers, within institutions are rare which raises questions about its ability to scale and limits its potential to impact student success. Additionally, amidst the backdrop of higher education’s contemporary challenges including massification and diversification, entire cohorts (not just those identified as “at risk” by traditional LA) feel disconnected and unsupported in their learning journey. Increasing pressures on teachers are also diminishing their ability to provide meaningful support and personal attention to students. For LA, related adoption barriers have been identified including workload pressures, lack of suitable or customizable tools, and unavailability of meaningful data. In this chapter, we present a teacher-friendly ’LA lifecycle’ that seeks to address these challenges and critically assess the adoption and impact of a unique solution in the form of an LA platform that is designed to be adaptable by teachers to diverse contexts. In this chapter, these contexts span three universities and over 72,000 students and 1,500 teachers. This platform, the Student Relationship Engagement System (SRES), allows teachers to collect, curate, analyze, and act on data of their choosing that aligns to their specific contexts. It also provides the ability to close the loop on support actions and guide reflective practice. In contrast to other platforms that focus on data visualization or algorithmic predictions, the SRES directly helps teachers to act on data to provide at-scale personalized support for study success. This way, the nuances of learning designs and teaching contexts can be directly applied to data-informed support actions. In our case studies, we highlight how this practical approach to LA directly addressed teachers’ and students’ needs of timely and personalized support and how the platform has impacted student and teacher outcomes. Through this, we develop implications for integrating teachers’ specific needs into LA, the forms of tools that may yield impact, and perspectives on authentic LA adoption.
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Notes
- 1.
We use “teachers” in this chapter to refer to educators who design and deliver learning experiences for students. This includes coordinators who have additional responsibilities such as broader curriculum design and ownership, as well as tutors (or teaching assistants) who work under coordinators.
- 2.
“Course” is defined in this chapter as an individual component of an academic program that a student takes, usually lasting a semester. For example, it is referred to as a “unit of study” at the University of Sydney, a “subject” at the University of Melbourne, and a “course” at the University of New South Wales.
References
Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers & Education, 62, 130–148.
Alli, N., Rajan, R., & Ratliff, G. (2016). How personalized learning unlocks student success. Educause Review Online, March/April, 12–21.
Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 24(4), 470–497.
Boud, D., & Middleton, H. (2003). Learning from others at work: Communities of practice and informal learning. Journal of Workplace Learning, 15(5), 194–202.
Bryson, C., & Hand, L. (2007). The role of engagement in inspiring teaching and learning. Innovations in Education and Teaching International, 44(4), 349–362. https://doi.org/10.1080/14703290701602748
Bucic, T., Vigentini, L., & King, M. E. (2018). Disruptive innovation in the marketing classroom for a personalized learning journey. In Paper presented at the Marketing Management Association Fall Educators’ Conference, Kansas City, MO.
Coates, H., James, R., & Baldwin, G. (2005). A critical examination of the effects of learning management systems on university teaching and learning. Tertiary Education and Management, 11(1), 19–36.
Colvin, C., Rogers, T., Wade, A., Dawson, S., Gašević, D., Buckingham Shum, S., … Fisher, J. (2016). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Canberra, Australia: Australian Government Office for Learning and Teaching.
Credé, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class attendance in college: A meta-analytic review of the relationship of class attendance with grades and student characteristics. Review of Educational Research, 80(2), 272–295.
Dawson, S., Jovanovic, J., Gašević, D., & Pardo, A. (2017). From prediction to impact: Evaluation of a learning analytics retention program. In Paper presented at the Proceedings of the Seventh International Learning Analytics & Knowledge Conference, Vancouver, British Columbia, Canada.
Farr-Wharton, B., Charles, M. B., Keast, R., Woolcott, G., & Chamberlain, D. (2018). Why lecturers still matter: The impact of lecturer-student exchange on student engagement and intention to leave university prematurely. Higher Education, 75(1), 167–185. https://doi.org/10.1007/s10734-017-0190-5
Ferguson, R., Clow, D., Macfadyen, L., Essa, A., Dawson, S., & Alexander, S. (2014). Setting learning analytics in context: Overcoming the barriers to large-scale adoption. In Paper presented at the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis, IN.
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
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
Jacques, P. H., Garger, J., Thomas, M., & Vracheva, V. (2012). Effects of early Leader–Member Exchange perceptions on academic outcomes. Learning Environments Research, 15(1), 1–15. https://doi.org/10.1007/s10984-012-9100-z
Kahu, E. R., & Nelson, K. (2018). Student engagement in the educational interface: Understanding the mechanisms of student success. Higher Education Research and Development, 37(1), 58–71. https://doi.org/10.1080/07294360.2017.1344197
Krause, K. L., & Coates, H. (2008). Students’ engagement in first-year university. Assessment & Evaluation in Higher Education, 33(5), 493–505.
Kuh, G. D., Kinzie, J. L., Buckley, J. A., Bridges, B. K., & Hayek, J. C. (2006). What matters to student success: A review of the literature (Vol. 8). Washington, DC: National Postsecondary Education Cooperative.
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.
Liu, D. Y. T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-driven personalization of student learning support in higher education. In Learning analytics: Fundaments, applications, and trends (pp. 143–169). Zürich, Switzerland: Springer.
Liu, D. Y. T., Rogers, T., & Pardo, A. (2015). Learning analytics - are we at risk of missing the point? In Paper presented at the 32nd ASCILITE Conference, Perth, WA.
Liu, D. Y. T., Taylor, C. E., Bridgeman, A. J., Bartimote-Aufflick, K., & Pardo, A. (2016). Empowering instructors through customizable collection and analyses of actionable information. In Paper presented at the 1st Learning Analytics for Curriculum and Program Quality Improvement Workshop.
Lizzio, A. (2006). Five senses of success: Designing effective orientation and engagement processes. Griffith University First Year Experience Project. Griffith University. Retrieved from www.griffith.edu.au/__data/assets/pdf_file/0008/51875/Alfs-5-Senors-Paper-FYE-Project-2006.pdf
Lonn, S., & Teasley, S. D. (2009). Saving time or innovating practice: Investigating perceptions and uses of Learning Management Systems. Computers & Education, 53(3), 686–694. https://doi.org/10.1016/j.compedu.2009.04.008
Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3), 149–163.
Newman-Ford, L., Fitzgibbon, K., Lloyd, S., & Thomas, S. (2008). A large-scale investigation into the relationship between attendance and attainment: A study using an innovative, electronic attendance monitoring system. Studies in Higher Education, 33(6), 699–717.
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090
Pardo, A. (2017). A feedback model for data-rich learning experiences. Assessment & Evaluation in Higher Education, 43(3), 1–11. https://doi.org/10.1080/02602938.2017.1356905
Pardo, A., & Dawson, S. (2016). Learning analytics: How can data be used to improve learning practice? In P. Reimann, S. Bull, M. Kickmeier-Rust, R. Vatrapu, & B. Wasson (Eds.), Measuring and visualizing learning in the information-rich classroom (pp. 41–55). New York, NY: Routledge.
Pearce, J., & Down, B. (2011). Relational pedagogy for student engagement and success at university. The Australian Educational Researcher, 38(4), 483–494. https://doi.org/10.1007/s13384-011-0037-5
Reason, R. D., Terenzini, P. T., & Domingo, R. J. (2006). First things first: Developing academic competence in the first year of college. Research in Higher Education, 47(2), 149–175.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.
Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Paper presented at the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, Canada.
Tanes, Z., Arnold, K. E., King, A. S., & Remnet, M. A. (2011). Using signals for appropriate feedback: Perceptions and practices. Computers & Education, 57(4), 2414–2422.
Thomson, K. (2015). Informal conversations about teaching and their relationship to a formal development program: Learning opportunities for novice and mid-career academics. International Journal for Academic Development, 20(2), 137–149. https://doi.org/10.1080/1360144X.2015.1028066
Tinto, V. (2006). Research and practice of student retention: What next? Journal of College Student Retention: Research, Theory and Practice, 8(1), 1–19.
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509.
Vigentini, L., Kondo, E., Samnick, K., Liu, D. Y. T., King, D., & Bridgeman, A. J. (2017). Recipes for institutional adoption of a teacher-driven learning analytics tool: Case studies from three Australian universities. In Paper presented at the ASCILITE 2017.
Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge, England: Cambridge University Press.
West, D., Huijser, H., Lizzio, A., Toohey, D., Miles, C., Searle, B., & Bronnimann, J. (2015). Learning analytics: Assisting universities with student retention, final report (part 1). Australian Government Office for Learning and Teaching.
West, R. E., Waddoups, G., & Graham, C. R. (2007). Understanding the experiences of instructors as they adopt a course management system. Educational Technology Research and Development, 55(1), 1–26.
Zepke, N., & Leach, L. (2010). Improving student engagement: Ten proposals for action. Active Learning in Higher Education, 11(3), 167–177.
Acknowledgments
The authors wish to thank the designers, developers, and directors who help to build and assist academics with the SRES, including but certainly not limited to Kevin Samnick, Melissa Makin, Joshua Lilly, Melanie Keep, Adam Bridgeman, Ruth Weeks, and Uli Felzmann.
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Arthars, N., Dollinger, M., Vigentini, L., Liu, D.YT., Kondo, E., King, D.M. (2019). Empowering Teachers to Personalize Learning Support. In: Ifenthaler, D., Mah, DK., Yau, J.YK. (eds) Utilizing Learning Analytics to Support Study Success. Springer, Cham. https://doi.org/10.1007/978-3-319-64792-0_13
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