The case for Learning Analytics
As most internet users will know, analytics means big business.
From suggesting posts or pages to customising fashion preferences based on likes and views, analytics has become an invisible yet pervasive part of our online experience.
Business has long since harnessed the potential of analytics to identify trends, personalise the user experience and to drive advertising campaigns (Ferguson, 2012). In his text “Business Analytics”, Dinabandhu (p. 20) identifies the goals of business analytics as follows:
Providing real-time, actionable information
Providing tools to help decision making around customer goals
Providing analysis that helps forecast the future
Providing the insight and understanding to support informed decisions and providing the feedback that is needed to create a learning organization
Translated into pedagogical speak, it is not difficult to see why the potential of learning analytics (LA) to “generate meaningful, ongoing data to inform…teaching, track students’ progress, and personalize learning” (Tucker, 2015, p. 82) has garnered increasing interest in educational circles.
In both the 2017 Horizon Report on K-12 Education as well as the 2019 Higher Education Edition, a “growing focus on measuring learning” has been identified as a key mid-term trend in education. The ability of an integrated LA system to measure “academic readiness, school progress, skills acquisition, and student achievement” (p.16) is touted to have system-wide application, informing decision-making for institutions, practitioners as well as for students. On a global scale, the recently released World Development Report on education (2018) further highlights the importance of educational data, asserting its pivotal role in addressing the “invisible learning crisis” (WBG, 2018 p. 91).
Evidently, it goes without saying that the use of data in an educational setting is nothing new. Currently, many schools are “awash in information about most aspects of their operation” (James-Ward, Frey & Lapp, 2013 p.1). This data may take the form of either hard data (typically summative or formative assessment results) as well as soft data (the observation of student and adult behaviours in educational settings). While soft data is likely to inform pedagogical strategy over the course of the year, hard data is predominantly analysed at predetermined moments in time, such as at the conclusion of summative assessments, during professional learning sessions or at the start/end of the school year (James-Ward et. al., 2013).
The advent of LA augments this foundation by enabling both a retrospective and prospective approach to data analysis. In addition to analysing student achievement post factum, LA enables teachers to predict student outcomes, identify areas of difficulty and stage appropriate interventions in the form of just-in-time feedback (Bienkowski, Feng & Means, 2012). Soft data affordances of LA include the possibility of tracking the development of skills such as creativity and collaboration (Freeman, Adams Becker, Cummins, Davis, & Hall Giesinger 2017).
My hunch is that secondary education is currently missing out on the benefits of LA. Like me, I suspect that many of my colleagues are time-poor and unsure of how to access and use LA to their advantage. Furthermore, I imagine that not all teaching staff are aware of the vast data that is currently available and which, when used regularly and in a timely fashion, can be used to improve student learning outcomes. Of those who are, it is unlikely that all are data literate (James-Ward et. al., 2013) due to a lack of adequate professional learning and development.
Whereas to date, LA have predominantly informed tertiary and distance-learning contexts, my research seeks to consider the relevance and application of LA in secondary school face-to-face classrooms. My research aims to bring awareness of the affordances of LA for secondary teachers and the design and delivery of targeted PLD. Furthermore, given our mandate by the New Zealand Curriculum to engage in a cycle of continuous improvement through iterations of the teaching-as-inquiry cycle, my research will aid teacher-researchers in their quest to improve data-informed practice in secondary education in New Zealand.
References
Alexander, B., Ashford-Rowe, K., Barajas-Murphy, N., Dobbin, G., Knott, J., McCormack, M., Pomerantz, J., Seilhamer,R., & Weber, N. EDUCAUSE Horizon Report: 2019 Higher Education Edition Retrieved from www.educause.edu
Bag, D. (2017) Business Analytics. Retrieved from http://ebookcentral.proquest/com
Bienkowski, M., Feng, M., & Means, B. (2012) Enhancing teaching and learning through educational data mining and learning analytics: an issue brief. Retrieved from US Department of Education website: https://tech.ed.gov/wp-content/uploads/2014/03/edm-la-brief.pdf
Ferguson, R. (2012). Learning Analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4 (5/6), 304–317.
Freeman, A., Adams Becker, S., Cummins, M., Davis, A., & Hall Giesinger, C. (2017). NMC/CoSN Horizon Report: 2017 K–12 Edition. Retrieved from www.cdn.nmc.org
James-Ward, C., Frey, N., & Lapp, D. (2013). Using data to focus instructional improvement. Retrieved from https://ebookcentral.proquest.com
Tucker, C. (2015). The techy teacher: using data to personalize learning. Educational Leadership, 73(3). Retrieved from www.ascd.org
World Bank Group. (2018). Learning to realize education’s promise. Washington D.C.: Retrieved from www.worldbank.org
