The key literature for my study

Just as consumers worldwide have increasingly come to expect the customisation of products and services, so too have visions of ‘personalisation’ begun to penetrate K-12 education. “Personalised Learning” (PL) has been heralded as a replacement for a “one-size-fits-all” education system (Patrick et al, 2013), a claim bolstered by studies which point to significant achievement gains for students in personalised learning settings (Pane et al, 2015). High levels of student agency (Basham et al, 2016; Bingham, 2017; Olofson et al, 2018); diverse learning pathways (Patrick et al, 2013; Pane et al, 2015; Basham et al, 2016; Bingham, 2017; Bingham et al, 2018; Olofson et al, 2018); and renegotiated teacher-student relationships (Patrick et al, 2013; Pane et al, 2015) are some of the many arguments forwarded by proponents of this approach to learning.

In the quest to personalise the learning experience, online learning platforms have deployed “learning analytics” (LA), a technology originally embraced by the business community. LA are designed to capture “data in or about the learning context (in situ), and the timely application of that data to improve learning and the learning environment.” (Rogers et al, 2016, p. 233). Studies showing significant improvement in student grades, higher retention rates and the ability for teachers to optimise learning for all students have buoyed enthusiasm for LA amongst the education sector (Arnold & Pistilli, 2012; Van Leeuwen, 2019).

Despite the acclaimed affordances of both PL and LA, critics have decried the “marketisation of education” (Hartley, 2007, p. 630), in which theoretically established pedagogical models have been displaced by data-driven business tools. Moreover, without the endorsement of teaching staff and the provision of effective professional development (Rienties et al, 2018), the promising synergies of personalised learning and learning analytics are likely to be relegated to the wasteland of educational fads. In this respect, a recent report commissioned by the Ministry of Education in New Zealand highlights the importance of collaborative, participatory and supportive professional development (Newton, 2017). It is both ironic and fitting that the use of LA to personalise student learning in a NZ middle school hinges on the effective personalisation of teacher professional learning.

References

Arnold, K.E., & Pistilli, M.D. (2012, April 29 — May 2). Course signals at Purdue: Using learning analytics to increase student success [Paper presentation]. Learning Analytics and Knowledge Conference (LAK), Vancouver, BC, Canada. https://doi.org/10.1145/2330601.2330666

Basham, J. D., Hall, T. E., Carter, R. A., & Stahl, W. M. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126–136. https://doi.org/10.1177/0162643416660835

Bingham, A. J. (2017). Personalized learning in high technology charter schools. Journal of Educational Change, 18(4), 521–549. https://doi.org/10.1007/s10833-017-9305-0

Bingham, A. J., Pane, J. F., Steiner, E. D., & Hamilton, L. S. (2018). Ahead of the curve: Implementation challenges in personalized learning school models. Educational Policy, 32(3), 454–489. https://doi.org/10.1177/0895904816637688

Hartley, D. (2007). Personalisation: the emerging ‘revised’ code of education? Oxford Review of Education, 33(5), 629–642. https://doi.org/10.1080/03054980701476311

Newton, C. (2017). Towards digital enablement: A literature review. Ministry of Education. https://www.educationcounts.govt.nz/publications/schooling/towards-digital-enablement-a-literature-review

Olofson, M. W., Downes, J. M., Petrick Smith, C., LeGeros, L., & Bishop, P. A. (2018). An instrument to measure teacher practices to support personalized learning in the middle grades. RMLE Online: Research in Middle Level Education, 41(7), 1–21. https://doi.org/10.1080/19404476.2018.1493858

Pane, J., Steiner, E., Baird, M., & Hamilton, L. (2015). Continued Progress: Promising Evidence on Personalized Learning. RAND Corporation. https://www.jstor.org/stable/10.7249/j.ctt19w73mb

Patrick, S., Kennedy, K., & Powell, A. (2013). Mean what you say: Defining and integrating personalized, blended and competency education. https://www.inacol.org

Rienties, B., Herodotou, C., Olney, T., Schencks, M. & Boroowa, A. (2018). Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers. International Review of Research in Open and Distributed Learning, 19(5), 186–202. https://doi.org/10.19173/irrodl.v19i5.3493

Rogers, T., Dawson, S., & Gaevi, D. (2016) Learning analytics and the imperative for theory- driven research. In Haythornthwaite, C., Andrews, R., Fransman, J., & Meyers, E.M. (Eds.), Introduction to the SAGE Handbook of E-learning Research (2nd ed., 232–250). http://dx.doi.org/10.4135/9781473955011.n12

Van Leeuwen, A. (2019). Teachers’ perceptions of the usability of learning analytics reports in a flipped university course: when and how does information become actionable knowledge? Education Tech Research Dev, 67, 1043–1064. https://doi.org/10.1007/s11423-018-09639-y