Learning analytics is an evidence-based approach to understand and evaluate relevant data to support and improve learning and teaching
While evidence-informed practice using data is not new, the incorporation of digital data and analytics for learning is relatively new. As an emerging field of research and practice, learning analytics has its roots in related fields such as educational data mining, academic analytics, intelligent tutoring systems however in its current form now encompasses wider components of other disciplines, such as the learning science, psychology, cognitive science, and human factors, to name a few.
The most cited definition of "Learning Analytics" comes from the very first Learning Analytics and Knowledge Conference in 2011, where George Siemens and colleagues defined learning analytics as:
...the measurement, collection, analysis and reporting of data for learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs
Erik Duval (2012) echoes this more simply in highlighting the new aspect associated with learning analytics:
Collecting traces that learners leave behind and using those traces to improve learning
Understanding learning towards optimising learning
This focus on "understanding and optimising learning" is where learning analytics departs from educational data mining and more data-driven practice, to that of a practice with more critical links to learning theory, design for learning, and a more scholarly, evidence-informed practice. This process of incorporating data into learning and teaching practice inherently requires a critical, ethical mindset.
The imperative to focus learning analytics on learning as central to its use and application marked a critical turn for the field (Gašević , et al., 2015). Since then, more have focused research and practice in the domain of learning and teaching, increasing our understanding of student learning strategies and performance, impact and redesign of design for learning, research and application methodologies, as well as an increasingly critical discourse in the use and application of learning analytics in complex learning environments.
Learning analytics resources
Learning analytics is an emerging, rapidly evolving field. It is critical to bear this in mind as we engage in and with learning analytics. The more the we (and the sector) learns, the better we are able to improve our practices in evidence-based ways. These resources are intended as part of professional learning for learning analytics for those who may be new to the field.
The following resources are by no means comprehensive, and are not intended as prescriptive of learning analytics - a critical scholarly lens when reading these are always beneficial. Given the rapid evolution of the field, bear in mind the chronological order of these papers when synthesising to understand the current state of the field.
|Title and link||Description|
Lang, C. Siemens, G., Wise, A., & Gašević, D. (2017). Handbook of Learning Analytics. https://doi.org/10.18608/hla17
The Lang et al reference is a comprehensive handbook for learning analytics, developed by the Society for Learning Analytics Research. Covers a wide span of topics, and open access.
Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, https://doi.org/10.1177/0002764213498851
|This (Siemens, 2013) is an account of the development of the discipline of learning analytics. A more recent paper talks about where it has further evolved into (see Gašević, Kovanović & Joksimović, 2017, http://www.tandfonline.com/doi/abs/10.1080/23735082.2017.1286142)|
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
|As a result of some of the earlier emergent practice in learning analytics, leaders in the field put this paper out to remind the field of the key focus of analysis and improvement: for learning.|
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439–1459. https://doi.org/10.1177/0002764213479367
|An earlier paper in the field, but one of the clearer links to practice of design for learning.|
D. West; H. Huijser, A. Lizzio, D. Toohey, C. Miles, B. Searle, J. Bronnimann, (2015) Learning Analytics: Assisting Universities with Student Retention, Case studies - Project outcome of Final Report prepared for the Australian Government Office for Learning and Teaching. https://ltr.edu.au/resources/SP13-3268_Casestudybook_2015.pdf#page=38
Alhadad, S. (2017). Subject level learning analytics. https://app.secure.griffith.edu.au/exlnt/entry/4365/view
Louis, W., Chapman C. (2017). The seven deadly sins of statistical misinterpretation, and how to avoid them. Retrieved from https://theconversation.com/the-seven-deadly-sins-of-statistical-misinterpretation-and-how-to-avoid-them-74306
|And one for interpreting data that others have previously found helpful (particularly as we have dynamic y-axes to accommodate the large data range/courses).|
|Title and link||Description|
Kahu, E. (2013). Framing student engagement in higher education. Studies in Higher Education, 28(5), 758-773. https://doi.org/10.1080/03075079.2011.598505
Ashwin, P., & McVitty, D. (2015). The meanings of student engagement: Implications for policies and practices. In A. Curaj, L. Matei, R. Pricopie, J. Salmi, & P. Scott (eds.) The European Higher Education Area. Dordrecht: Springer, (pp. 343–359). https://doi.org/10.1007/978-3-319-20877-0_23
Title and link
From the Australasian Society for Computers in Learning in Tertiary Education
Inter-disciplinary network of leading international researchers exploring the role and impact of analytics on teaching and learning
A paper exploring the ethical issues related to Learning Analytics in the context of the Australian Higher Education sector