Why you should mine your data goldmine: Linking research to practice
Most days an email appears in my inbox with a task report of my students’ performance in their latest Education Perfect (EP) assignment. This summary includes statistics relating to task completion and accuracy as well as conveniently highlighting the questions that the class found most challenging. More detailed information in the form of “late” and “almost” finishers is provided at the click of a button. The visualisations on my teacher dashboard allow me to compare EP use across departments and track student usage from year to year. Put simply, there is a wealth of information with the potential to inform and improve teaching and learning practices at my fingertips.
Often, these emails are relegated to my trash can, with only a cursory glance at the insights offered. While I suspect that I am not alone in this behaviour, it leads me to wonder:
…to what extent are secondary school teachers aware of and engaging with these data?
How can we, as teachers, use system-generated data to stage interventions and implement “just-in-time” changes to our teaching practice?
How can we optimise and personalise the learning environment of our students in light of the evidence?
The discipline of Learning Analytics
In looking for answers, the discipline of Learning Analytics, a subset of the broader field of Technology Enhanced Learning (TEL), offers some helpful insights. The most commonly cited definition for Learning Analytics is that proposed by the first International Conference on Learning Analytics and Knowledge (LAK 2011):
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
Ferguson (2012) distinguishes the field of Learning Analytics from the closely related disciplines of Educational Data Mining (EDM) and Academic Analytics (AA) on account of their primary focus. Whereas EDM has a technical focus and AA a primarily political/economic objective, LA’s challenge is educational, seeking to answer the question; “How can we optimise opportunities for online learning?” (p.311).
Learning analytics benefits matrix
In this regard, Ifenthaler (2017) proposes a helpful LA benefits matrix. From the perspective of a classroom teacher, LA could well be the golden ticket, offering the following exciting possibilities:
- Comparison of results
- Tracking of learning progressions
- Evaluation of teaching practices
- Timely and adaptive interventions
- Modification of content
- Identification of struggling students
- Optimisation of student learning paths
Sound too good to be true? Such is the caution raised by a number of academics who remind us that the data is only as valid as the system which generates it. To this end, many LA systems produce what is referred to as event data (number of logins/time spent on a page etc) or performance data (results/completion rates etc). While these results do have some correlation with student retention rates, particularly at tertiary level (Thille & Zimmaro, 2017), more helpful from a teaching perspective would be a framework that “ground(s) data collection, measurement, analysis, reporting and interpretation processes within the existing research on learning” (Gašević, Dawson & Siemens, 2015).
Other criticisms levelled against existing LA frameworks include the need for algorithms to represent populations accurately, considering biases such as gender, race and class in order to generate meaningful data (Thille & Zimmaro, 2017).
As this exciting field continues to develop over the next few years, delivering on its promises and addressing some of its weaknesses, my thesis seeks to explore how secondary school teachers are making use of this data.
To date, the majority of the research has been conducted on tertiary institutions at a mega (governance) and macro (institutional) level (Schumacher & Ifenthaler, 2018). I’m interested in the conclusions drawn by secondary school teachers at the meso-level (teacher) and the impact of their interventions on the learning outcomes of their students. Having had my eyes opened to the metaphorical LA goldmine, I’m looking forward to doing some more mining of my own!
References
Ferguson, R. (2012). Learning Analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4 (5/6), 304–317.
Gašević, D., Dawson, S. & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71
Ifenthaler, D. (2017). Are Higher Education Institutions Prepared for Learning Analytics? TechTrends, 61, 366–371. doi: 10.1007/s11528–016–0154–0
Schumacher, C. & Ifenthaler, D. (2018) Features students really expect from learning analytics. Computers in Human Behaviour, 78, 397–407. Doi: 10.1016/j.chb.2017.06.030
Thille, C., & Zimmaro, D. (2017). Incorporating Learning Analytics in the Classroom. New Directions for Higher Education, 179, 19–31. doi: 10.1002/he
