Learning Analytics for 21st Century Competencies?

safariscreensnapz484We’re delighted to announce a Special Section of the Journal of Learning Analytics, published this week, focusing on the challenge of Learning Analytics for 21st Century Competencies. In our editorial we introduce the nature of the challenge, and after summarising the different researcher and practitioner papers, propose a complex systems approach which takes seriously the ‘layers, loops and processes’ of learning infrastructures and the iterative relationship between the human and the digital, where people learn at the nodes of networked flows of information.neural_network_by_rajasegar-d2xx3w9

Learning analytics is an emerging field powered by the paradigm shifts of the information age. Pedagogy and learning that produce students capable of thriving in conditions of complexity, risk, and challenge by taking responsibility for their own learning journeys, and using technology and analytics to scaffold this process is  at the heart of the challenge. It is an emergent field, still struggling to find its way. These papers represent a unique ‘window’ into this programme from the viewpoint of both users and researchers.

You can enjoy full access to all the articles, since JLA is an open access journal.

I gave an overview of the topic and some of the papers in the above volume in this talk to the Asian Learning Analytics Summer Institute, with thanks to Yong-Sang Cho and the LASI-Asia team for the kind invitation…

SPECIAL SECTION: LEARNING ANALYTICS FOR 21ST CENTURY COMPETENCIES

Learning Analytics for 21st Century Competencies

Simon Buckingham Shum, Ruth Deakin Crick

Towards the Discovery of Learner Metacognition From Reflective Writing

Andrew Gibson, Kirsty Kitto, Peter Bruza

An Approach to Using Log Data to Understand and Support 21st Century Learning Activity in K-12 Blended Learning Environments

Caitlin K. Martin, Denise Nacu, Nichole Pinkard

Understanding Learning and Learning Design in MOOCs: A Measurement-Based Interpretation

Sandra Kaye Milligan, Patrick Griffin

Practical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement

Andrew Edward Krumm, Rachel Beattie, Sola Takahashi, Cynthia D’Angelo, Mingyu Feng, Britte Cheng

Analytics for Knowledge Creation: Towards Epistemic Agency and Design-Mode Thinking

Bodong Chen, Jianwei Zhang

Tracking and Visualising Student Effort: Evolution of a Practical Analytics Tool for Staff and Student Engagement

Robin Paul Nagy

Marks Should Not Be the Focus of Assessment – But How Can Change Be Achieved?

Darrall G Thompson

Scaffolding deep reflection with automated feedback?

We’ve all got used to the idea that computers can understand writing and speech to some degree — Google adverts that match your search queries… asking Siri simple questions… IBM Watson winning Jeopardy! But how does natural language processing fit into learning that goes beyond getting the right answer to a focused question, or matching some key concepts?

Language is clearly front and centre in the way that we learn from others, share our understanding, and narrate to ourselves. However, the idea that computers have any substantive contribution to make to the teaching and assessment of writing elicits strong reactions from educators, and understandably so (learn more from this workshop).

In recent work that we’ve been doing at University of Technology Sydney, we’re exploring to what extent text analytics could support the integrative practices of writing that many are now using to help students reflect on their learning. This is particularly germane to future-oriented pedagogy that seeks to help the learner narrate their own learning journey in relation to their identity (how does my learning change who I think I am becoming?), place them in authentic (often professional) learning contexts (on the ward; in the classroom; in a company), and so forth.

As we explain in this article, there are many pedagogical reasons for valuing deep, academic reflective writing, but it poses significant challenges to educators to teach this well, and to students for whom this is often a new genre of writing. Below is the presentation we gave earlier this year. Current the Academic Writing Analytics (AWA) web tool is available only within UTS as we pilot it, but in the future this may become available to other universities, and beyond that, to schools interested in collaborative research.

Buckingham Shum, S., Á. Sándor, R. Goldsmith, X. Wang, R. Bass and M. McWilliams (2016). Reflecting on Reflective Writing Analytics: Assessment Challenges and Iterative Evaluation of a Prototype Tool. 6th International Learning Analytics & Knowledge Conference (LAK16), Edinburgh, UK, April 25 – 29 2016, ACM, New York, NY. http://dx.doi.org/10.1145/2883851.2883955 Preprint: http://bit.ly/LAK16paper

Customer Journeys: Learning Journeys – beyond ‘nudge’ to digital decisioning at scale

The Learning Emergence team hosted an International Centre for Infrastructure Futures workshop at the Systems Centre  yesterday to explore the synergy between ‘customer journeys’ as developed in the digital architecture of retail banking – and ‘learning journeys’ as developed by the Bristol team to capture the personal and social processes which contribute to the development of ‘resilient agency’.

the achievable goal of true ‘customer at a time’ value management with many millions of customers

Dec Blue Partner  Tim Crick, also a Learning Emergence Partner, showed how advanced customer decisioning technology can help organisations deliver agile and adaptive ‘customer at a time’ value management strategies across digital, assisted and face-to-face  channels. This transformation in approach to customer management is radical – from a product/campaign centred approach to a ‘customer at a time’ Next Best Action Journey, from siloed channels to ‘joined up’ channels, and from  ‘old data’ to ‘real time insight’ – its all about how to engage the individual in becoming resilient agents of their own financial journeys, rather than ‘telling them what to do’ and ‘selling them products they don’t really want’.  Dec Blue is partnering with the Learning Emergence Partnership to explore this ‘joined up thinking’ through research and development. Untitled

Lets just suspend our suspicion of the banks for a minute and explore these ideas.

Ok – this is ‘commercial speak’ but we are Analogical Scavengers....and if we look at this from a different viewpoint,then it has many of the characteristics of learning journeys.  The ‘person’ has a desire or a purpose. That purpose gives fuels their learning power.  They figure out how to go about achieving that purpose, to persevere and explore all the relevant data and options available to them, in their unique context. They accumulate all that information, no doubt feel overwhelmed, and challenged as they figure out how best to achieve their goal. They get the help they need (online and offline) and finally settle on their preferred outcome. Once they move in they’ve achieved their purpose…they got what they wanted and needed which was a home of their own (not a mortgage!).   This is what we call a ‘single loop’ learning journey – just getting the job done. But if we use the same digital decisioning capability to ‘make the journey visible’  so that the individual learns how to go about navigating the journey itself more reflexively, asking more questions, challenging more assumptions and exploring more alternatives, then we’re actually enabling people to strengthen their learning power and become more discerning and effective in creating value. 

customer journey

Just willingly suspend your disbelief’ about the banks for a moment. The focus here is on enabling the individual to identify a purpose, to creatively explore what their options are, to find people and resources to help them, to collect the data they need to explore options, to make a decision and to implement it. Maybe the outcome is a NO. Still it’s a job done and its a learning journey. The journey has an architecture – stages, steps, transitions, interactions, triggers, beginnings and endings. The point is we have the technology and the know how to build this sort of digital decisioning infrastructure at scale.

what if we tuned this technology to the challenge of CO2 reduction?

customer journey fuelled by learning

double loop learningThe ‘customer needs cycle’ matches the ‘authentic enquiry cycle’ ….. what we know about learning power and resilient agency adds value to this by locating the ‘customer’  (aka ‘learner’ or  ‘individual’) as the primary agent of purpose and thus the driver of the process. Resilience is about mindfully navigating the journey between purpose and performance fuelled by learning power, which is the way in which we regulate the flow of energy and information over time in the service of a purpose of value’.   The dimensions of learning power simply provide a language and a focus for how we can go about this and get better at navigating learning journeys when we don’t know what the outcome is in advance. Learning Emergence Partner Steven Barr took us through the first steps of designing a customer/learning journey focused on ‘cycle to work’ as a job to be done which, if done at scale would have an impact on the overall outcome of CO2 reduction.

linking the the knowledge and know how about digital customer journeys with learning journeys and applying this at scale to an important social  ‘job to be done’ is the next big research and development challenge