Enhancements to the SOLE Tookit – now version 3.5

May 31, 2015

I have no idea what the protocol is for naming versions of things. I imagine, like me, someone has an idea of what the stages are going to look like, when a truly fresh new is going to happen. For me I have a sense that version 4.0 of the SOLE Toolkit will incorporate what I am currently learning about assessment and ‘badges’, self-certification and team marking. But for now I’m not there yet and am building on what I have learnt about student digital literacies so I will settle for Version 3.5.

This version of the SOLE Toolkit, 3.5, remains a completely free, unprotected and macro-free Excel workbook with rich functionality to serve the learning designer. In version 3.0 I added more opportunities for the student to use the toolkit as an advanced organiser offering ways to record their engagement with their learning. It also added in some ability to sequence learning so that students could plan better their learning although I maintained this was guidance only and should allow students to determine their own pathways for learning.

Version 3.5 has two significant enhancements. Firstly, it introduces a new dimension, providing a rich visualization of the learning spaces and tools that students are to engage with in their learning. This provides an alternative, fine-grain, view of the students modes of engagement in their learning. It permits the designer to plan not only for a balance of learning engagement but also a balance of environments and tools. This should allow designers to identify where ‘tool-boredom’ or ‘tool-weariness’ is possibly a danger to learner motivation and to ensure that a range of tools and environments allow students to develop based on their own learning preferences.

Secondly, it allows for a greater degree of estimation of staff workload, part of the original purpose of the SOLE Model and Toolkit project back in 2009. This faculty-time calculations in design and facilitating are based on the learning spaces and tools to be used. This function allows programme designers and administrators, as well as designers themselves, to calculate the amount of time they are likely to need to design materials and facilitate learning around those materials.

I invite you to explore the SOLE Toolkit on the dedicated website for the project and would welcome any comments of feedback you might have.


Why ‘learning analytics’ is like a sewer

May 2, 2015

Back in the late northern hemisphere summer of 2013 I drafted a background paper on the differences between Educational Data Mining, Academic Analytics and Learning Analytics. Entitled ‘Adaptive Learning and Learning Analytics: a new design paradigm‘, It was intended to ‘get everyone on the same page‘ as many people at my University, from very different roles, responsibilities and perspectives, had something to say about ‘analytics’. Unfortunately for me I then had nearly a years absence through ill-health and I came back to an equally obfuscated landscape of debate and deliberation. So I opted to finish the paper.

I don’t claim to be an expert on learning analytics, but I do know something about learning design, about teaching on-line and about adapting learning delivery and contexts to suit different individual needs. The paper outlines some of the social implications of big data collection. It looks to find useful definitions for the various fields of enquiry concerned with collecting and making something useful with learner data to enrich the learning process. It then suggest some of the challenges that such data collection involves (decontextualisation and privacy) and the opportunity it represents (self-directed learning and the SOLE Model). Finally it explores the impact of learning analytics on learning design and suggests why we need to re-examine the granularity of our learning designs.

I conclude;

Learning Analytics Cover“The influences on the learner that lay beyond the control of the learning provider, employer or indeed the individual themselves, are extremely diverse. Behaviours in social media may not be reflected in work contexts, and patterns of learning in one discipline or field of experience may not be effective in another. The only possible solution to the fragmentation and intricacy of our identities is to have more, and more interconnected, data and that poses a significant problem.

Privacy issues are likely to provide a natural break on the innovation of learning analytics. Individuals may not feel that there is sufficient value to them personally to reveal significant information about themselves to data collectors outside the immediate learning experience and that information may simply be inadequate to make effective adaptive decisions. Indeed, the value of the personal data associated with the learning analytics platforms emerging may soon see a two tier pricing arrangement whereby a student pays a lower fee if they engage fully in the data gathering process, providing the learning provider with social and personal data, as well as their learning activity, and higher fees for those that wish to opt-out of the ‘data immersion’.

However sophisticated the learning analytics platforms, algorithms and user interfaces become in the next few years, it is the fundamentals of the learning design process which will ensure that learning providers do not need to ‘re-tool’ every 12 months as technology advances and that the optimum benefit for the learner is achieved. Much of the current commercial effort, informed by ‘big data’ and ‘every-click-counts’ models of Internet application development, is largely devoid of any educational understanding. There are rich veins of academic traditional and practice in anthropology, sociology and psychology, in particular, that can usefully inform enquiries into discourse analysis, social network analysis, motivation, empathy and sentiment study, predictive modelling and visualisation and engagement and adaptive uses of semantic content (Siemens, 2012). It is the scholarship and research informed learning design itself, grounded in meaningful pedagogical and andragogical theories of learning that will ensure that technology solutions deliver significant and sustainable benefits.

To consciously misparaphrase American satirist Tom Lehrer, learning analytics and adaptive learning platforms are “like sewers, you only get out of them, what you put into them’.”

Download the paper here, at AcademiaEdu or ResearchGate

Siemens, G. (2012). Learning analytics: envisioning a research discipline and a domain of practice. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 4–8). New York, NY, USA: ACM. doi:10.1145/2330601.2330605


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