Week 11 builds on the previous week’s theme of big data, providing a spotlight on the use of data specifically for learning. Unsurprisingly, there are many links and reference points from topics throughout the rest of IDEL.
The core readings seem to indicate that the field of learning analytics is still very immature, and when compared with the use of other technologies within education, could be considered to be lagging.
It seems, on the whole, learning analytics operate at surface level at present. Gasevic et al (2015) highlight the complexities around learner agency and pedagogical approaches that can provoke glaring holes in the interpretation of any data. Ultimately in any scenario, educational or not, data needs context to have any meaning (and therefore actionable value), and it seems to be falling short in this area at present.
I enjoyed reading about Purdue University’s Course Signals project in the core readings. The intention behind this project seems to empower the teacher, rather than simply ‘measure’ the student. While the positivity around the results should be taken with a pinch of salt in Clow (2013) (indeed Gasevic et al (2015) does proffer further critique of this), it would seem that involving the teacher in the choice of interactions recognises the absence of relationship and emotion that perhaps these analytics struggle to encompass. However, it does appear that the aggregation and understanding of quantitative data that could bridge this gap is improving (Gasevic et al, 2015).
I particularly liked Clow’s (2013) description of learning analytics as a “‘jackdaw’ field of enquiry”, in that it uses a fragmented set of tools and methodologies – it could be seen to be using a rather less cohesive approach that would be required. This is certainly one of Gasevic et al’s (2015) key points – that the field needs to be underpinned with a more robust foundation to really allow it to develop in a useful way.
I wonder if the lack of maturity in this field is an implication of the nature of the field. The learning analytics cycle used by Clow (2012) identified that learning analytics are gathered and used after a learning activity or intervention has taken place. As has become even more apparent to me throughout this course, the pace of technological change is significant and rapid and the impacts on education are quite far-reaching.
If technology and tools are being developed, trialled, integrated, ditched and recycled so rapidly, inevitably it must be a challenge to assess with any rigour. Indeed Gasevic et al (2015) highlight the lack of empirical studies available to attempt to interpret this area. It’s interesting to hear in Clow (2013) that the use of proprietary systems impedes’ this too, with the lack of data available. This is particularly pertinent given their prevalence across the educational sector, and in turn impacts the assessments that can be made across domains and contexts (Gasevic et al, 2015).
A pervading theme across IDEL has been the discourse around the educational ‘voice’ in the development and use of technology, for example, Bayne (2015). Quite rightly this academic world wants to scrutinise, assess and challenge, but it seems the pace of change makes this less and less possible to take place.
For me, the spectre of the private sector is raised in Perrotta & Williamson (2016). It argues that that the use of analytics and interventions are “partially involved in the creation of the realities they claim to measure”. The challenge here is the increasing commercial influence taking place in the field of learning analytics. It cites Pearson as an example, as they have the large-scale distribution of their products to gather learner data, the resources to interpret and mine this, as well as the size to influence wider policy-making. Given the rhizomatic nature of the development of learning analytics, it seems to be that there are many reasons to be fearful of this development, particularly as it looks to be self-perpetuating.
Of course, I’m keen to keep in mind that this is one side of the argument, and I’m sure the likes of Pearson see themselves as helping to push things forward. Certainly, there are areas that the commercial world can help ‘lift the lid’ on learner behaviour, and empower teachers to make interventions – I guess the issue is how much those outside the corporation are at the discussion table. The stark truth is that Pearson’s core responsibility, above all, is to its shareholders, not it’s students.
My own personal experience has been at a ‘macro’ level, or what Clow (2015) refers to as ‘Business Intelligence’. As a commercial training provider, we used learner analytics (at a rather shallow level) to understand learner behaviour, and help us understand the product performance. Given the commercial nature of the people around me, however, there was probably an unhealthy bias or interest in how these can be used to improve commercial metrics. I certainly recognised some of the observations raised by Gasevic et al (2015) around data visualisation, and the pitfalls these can cause.
I think given this week’s reading I’ll certainly be more aware of some of the challenges in this area, particularly around providing metrics without some context. There almost needs to be some ‘priming’ of the viewer before they gain access, just to reduce the risk of mis-interpretation. I think I’ll also be keen to trial the use of analytics data to empower tutors, rather than simply automating prompts which has been the norm in the past. Alongside this, providing students with their own ‘performance’ data would be something I’d be keen to explore.
Last week’s discussions on big data raised concern about the skills needed within institutions to use big data, and I would suggest these are not solely limited to the educational world. The same issues occur in the commercial world, and can oft have quite dramatic implications if not used with care and forethought. It seems like if you are a data analyst with an understanding of educational methodologies you would be able to choose your job!
References:
- Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6) pp.683–695.
- Gasevic, D, Dawson, S & Siemens, G 2015, ‘Let’s not forget: Learning analytics are about learning’ TechTrends, vol 59, no. 1, pp. 64. DOI: 10.1007/s11528-014-0822
- Perrotta C, Williamson B, 2016. The social life of Learning Analytics: cluster analysis and the ‘performance’ of algorithmic education, Learning, Media and Technology. pp. 1-14 DOI: 10.1080/17439884.2016.1182927.
- Bayne, S. (2015). What’s the matter with ‘technology-enhanced learning’? Learning, Media and Technology, 40(1), 5-20, DOI: 10.1080/17439884.2014.91585