Places where learning takes place (#moocmooc day 2)

So I survived day 1 of #moocmooc, and the collaborative task of creating a 1,000 word essay explaining the history, context and potential and potential pitfalls of MOOCs, worked surprisingly well. Thanks to the people in the group I was assigned to, who took charge and got things done. I’m curating the resources/ recommended readings from the course on Pearltrees and you can see links to all the group submissions in my #moocmooc pearl.

Today’s theme is “places where learning takes place”. Participants have been asked to create a video sharing their views, experiences and share them on Youtube. There there are some really great contributions which have been collated in this Storify.

Now, today has been not quite an average Tuesday for me. We had our almost annual CETIS Scotland meet up at the Edinburgh festival. This was a bit of a family affair too and so we went to see Horrible Histories – which is a very fine live learning experience in itself. Those in the UK will know what I mean – if you’re not from here, check it out for probably the best guide to British history. Anyway I was thinking about where I learn at various points during the day. For #moocmooc, it is primarily online – at the office, at home, on the train – anywhere with a decent 3G/wifi connection. But I do need quite space to contemplate too. This tends to be when I’m walking to or from work, sometimes in my favourite chair with “a nice cup of tea”.

However the level of contribution and activity in this (and any online, never mind “massive” online course) can be overwhelming. In the twitter chat last night a few of us agreed that boundaries were important to help stay focused, and also the ability to not feel overwhelmed by the sheer level of activity is a key strategy which learners need to develop.

It sometimes feels that you’re trying to juggle all sorts of mismatching things, whilst trying to doing three other things at the same time and speak to 200 people you’ve never met before.

This afternoon we stopped off to watch a street performer who ended up ten feet up a ladder, took his kilt off and then juggled three very large (and sharp) knives. All this on cobblestones! Sometimes being in a mooc feels a bit like that. Slight crazy, a bit dangerous, but great fun – particularly when you get good feedback and connect with others.

Anyway here is my little video (I’ve bent the rules slightly but using animoto and not posting to youtube ).

To MOOC or not to MOOC?

Is the one of the underlying questions of the week long MOOC being run this week by Hybrid Pedagogy. Like many others working education I am interested in MOOCs, and there has been a flurry of activity over recent months with a number of big guns joining, or perhaps taking over, the party.

The #moocmooc course is running over a week, and today’s themes centre around “What are MOOCs? What do we think they are? What do we fear they may be? What potential lies under their surface?”. There’s a group task to complete – a 1,000 word essay on “What is a MOOC? What does it do, and what does it not do?”, and a twitter conversation tonight to share experiences.

However, I think that these questions need to be underpinned by a couple of “whys”? Why are you interested in MOOCs? Why are you thinking about taking the MOOC route? Sian Bayne and her colleagues in the MSc E-Learning course at the University Edinburgh have done exactly this in their recent ALT Article “MOOC pedagogy: the challenges of developing for Coursera“.

And by way of not answering the assignment question, I’m trying to reflect on my experiences of MOOCs to date. So far it looks like the majority of participants seem to be from North America, although there are a few UK faces in there too. I’m particularly interested seeing if there are any major differences in implementation/drivers between North America and the UK. Not everyone is going to be able to go down a full blown MOOC route, but what are the key elements that are really practical for the majority of institutions? The open-ness, experimenting and extending notions of connected learning? Potential to get big enrollment numbers? It’s probably far too early to tell, and as most of the participants probably fall into the early adopters category their motivations may not reflect general practice or readiness.

Although I have a professional interest in MOOCs, it’s probably their potential for me as a learner that really excites me. I’m not particularly motivated to do any more “formal” education – for a number of reasons, but time is probably the main one. I’m also very fortunate to have a job where I really do learn something new everyday, and I feel that my peers do keep my brain more than stimulated.

Being able to participate in open courses around topics that interest me, without financial risk to me personally or my employer (which adds pressure for me) is very appealing. I’ve tried MOOCs before (LAK11) which I enjoyed – particularly the synchronous elements such as the live presentations and chat. But if I’m being honest, I didn’t spend as much time on the course as I probably should have. On the plus side, I did get a feel for being a student on a MOOC and some useful insights to learning analytics.

Although I probably tick the right boxes to be a self motivated, engaged and directed learner, sometimes life just gets in the way and it turns out that I’m a bit rubbish at maintaining engagement, direction and motivation. But that hasn’t put me off MOOCs. Like tens of thousands of others I signed up for the Stanford NPL course, and very quickly realised that I was being a tad optimistic about my coding capabilities and that I just didn’t have the time I would need to get anything out of the course, so like tens of thousands of others I silently dropped out. I did think the traditional design of that course worked well for that subject matter.

But #moocmooc is only a week, no programme required, and also a week in August when things at work are a bit quieter than normal. Surely despite the twitter conversations talking place from 11pm my time I’ll be able to cope with that? Well we’ll see. Already it has got me thinking, given me the opportunity to try the Canvas VLE and back into blogging after a brief holiday lull.

*Day 2 Places where learning takes place
*Day 3 Massive Participation but no-one to talk to
*Day 4 Moocmooc day 4
*Day 5 Designing a MOOC – moocmooc day 5
* Analytics and #moocmooc

Learning Analytics, where do you stand?

For? Against? Not bovvered? Don’t understand the question?

The term learning analytics is certainly trending in all the right ways on all the horizons scans. As with many “new” terms there are still some mis-conceptions about what it actually is or perhaps more accurately what it actually encompasses. For example, whilst talking with colleagues from the SURF Foundation earlier this week, they mentioned the “issues around using data to improve student retention” session at the CETIS conference. SURF have just funded a learning analytics programme of work which closely matches many of the examples and issues shared and discussed there. They were quite surprised that the session hadn’t be called “learning analytics”. Student retention is indeed a part of learning analytics, but not the only part.

However, back to my original question and the prompt for it. I’ve just caught up with the presentation Gardner Campbell gave to the LAK12 MOOC last week titled “Here I Stand” in which he presents a very compelling argument against some of the trends which are beginning to emerge in field of learning analytics.

Gardner is concerned that there is a danger of that the more reductive models of analytics may actually force us backwards in our models of teaching and learning. Drawing an analogy between M theory – in particular Stephen Hawkins description of there being not being one M theory but a “family of theories” – and how knowledge and learning actually occur. He is concerned that current learning analytics systems are based too much on “the math” and don’t actually show the human side of learning and the bigger picture of human interaction and knowledge transfer. As he pointed out “student success is not the same as success as a student”.

Some of the rubrics we might be tempted to use to (and in cases already are) build learning analytics systems reduce the educational experience to a simplistic management model. Typically systems are looking for signs pointing to failure, and not for the key moments of success in learning. What we should be working towards are system(s) that are adaptive, allow for reflection and can learn themselves.

This did make me think of the presentation at FOFE11 from IBM about their learning analytics system, which certainly scared the life out of me and many other’s I’ve spoken too. It also raised a lot of questions from the audience (and the twitter backchannel) about the educational value of the experience of failure. At the same time I was reflecting on the whole terminology issue again. Common understandings – why are they so difficult in education? When learning design was the “in thing”, I think it was John Casey who pointed out that what we were actually talking about most of the time was actually “teaching design”. Are we in danger of the same thing happening to the learning side of learning analytics being hi-jacked by narrower, or perhaps to be fairer, more tightly defined management and accountability driven analytics ?

To try and mitigate this we need to ensure that all key stakeholders are starting to ask (and answering) the questions Gardner raised in his presentation. What are the really useful “analytics” which can help me as a learner, teacher, administrator, etc? Which systems provide that data just now ? How can/do these stakeholders access and share the data in meaningful ways? How can we improve and build on these systems in ways which take into account the complexity of learning? Or as Gardner said, how can we start framing systems and questions around wisdom? But before we can do any of that we need to make sure that our stakeholders are informed enough to take a stand, and not just have to accept whatever system they are given.

At CETIS we are about to embark on an analytics landscape study, which we are calling an Analytics Reconnoitre. We are going to look at the field of learning analytics from a holistic perspective, review recent work and (hopefully) produce some pragmatic briefings on the who, where, why, what and when’s of learning analytics and point to useful resources and real world examples. This will build and complement work already funded by JISC such as the Relationship Management Programme, the Business Intelligence Infokit and the Activity Data Programme synthesis. We’ll also be looking to emerging communities of practice, both here in the UK and internationally to join up on thinking and future developments. Hopefully this work will contribute to the growing body of knowledge and experience in the field of learning analytics and well as raising some key questions (and hopefully some answers) around around its many facets.

Thoughts so far on LAK11

Along with about 400 or so others world-wide, I’ve signed up for the LAK11 (Learning and Knowledge Analytics) MOOC run by George Siemens and colleagues at the Technology Enhanced Knowledge Research Institute (TEKRI) at Athabasca University. We’re now into week 2, and I think I’m just about getting into the swing of things.

When George was in the UK late last year, I managed to catch his presentation at Glasgow Caledonian, and I was intrigued with the concept of learning analytics, and in particular how we can start to use data in meaningful ways for teaching and learning. I wanted to know more about what learning analytics are and so signed up for the course. I’ve also been intrigued by the concept of MOOCs so this seemed liked the ideal opportunity to try one out for myself.

In her overview paper, Tanya Elias provides a useful description: ” Learning analytics is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study including business intelligence, web analytics, academic analytics, educational data mining, and action analytics.” (Elias, T. (2011) Learning Analytics: Definitions, Processes, Potential)

The course outcomes are:
*Define learning and knowledge analytics
*Map the developments of technologies and practices that influence learning and knowledge analytics as well as developments and trends peripheral to the field.
*Evaluate prominent analytics methods and tools and determine appropriate contexts where the methods would be most effective.
*Describe how “big data” and data-driven decision making differ from traditional decision making and the potential future implications of this transition.
*Design a learning analytics implementation plan at a course level. 
*Evaluate the potential impact of the semantic web and linked data on learning resources and curriculum.
*Detail various elements organizational leaders need to consider to roll out an integrated knowledge and learning analytics model in an organizational setting.
*Describe and evaluate developing trends in learning and knowledge analytics and develop models for their potential impact on teaching, learning, and organizational knowledge

You can check out the full course syllablus here .

The fact that the course is open and non-accredited really appealed to me as, to be honest, I am a bit lazy and not sure if I wanted to commit to to a formal course. The mix of online resources, use of tags, aggregation etc fits right in with my working practices. I blog, I tweet, I’m always picking up bits of useful (and useless) information from my streams – so having a bit of focus for some activity sounded perfect – I’m a self motivated kind of a person aren’t I?

But it’s never that simple is it? Old habits die hard – particularly that nagging feeling of guilt about signing up for a course and not reading all the suggested texts, reading all the forum messages, doing all the suggested activities. Is it just me that suffers from the tensions of trying to be an engaged, self motivated learner and everyday distractions and procrastination? I’ve had some vey circular discussion about myself about why I’m not actually looking at the course material at times.

However, George and the team have been particularly good at reassuring people and emphasising that we need to “let go of traditional boundaries”. With a cohort this large it’s pretty near impossible to keep up with everything so they actively encourage people only to do what they can, and concentrate on what what really interests you. They actively encourage “skim and dive” techniques -skim the all the resources and dive into what catches your eye/interest. If you’ve being thinking about doing one of the MOOCs then I would recommend having a listen to the introductory elluminate session (another great thing about open courses is that all the resources are available to everyone, anytime).

I’ve found the eliminate sessions the most interesting so far. Not because the other resources provided aren’t as engaging – far from it. I think it’s more to do with the synchronous element and actually feeling part of a community. All the speakers so far have been very engaging, as has the chat from participants.

Last week as introduction to Learning Analytics, John Fritz, UMBC gave an overview of the work he’s leading in trying to help students improve performance by giving them access to data about their online activity. They built a BlackBoard building block called Check My Activity (CMA), you can read more about it here. John and colleagues are also now active in trying to use data from their LMS to help teachers design more effective online actives.

This week’s topic is “The Rise of Big Data” and on Tuesday, Ryan Baker from Worcester Polytechnic Institute was in the eliminate hot seat, giving us an introduction to Educational Data Mining (EDM). EDM draws heavily on data mining methodologies, but in the context of educational data. Ryan explained it as a distillation of data for human judgement. In other words making complex data understandable and useful for non information scientists. EDM and Learning Analytics are both growing research areas, and the there are a number of parallels between them. We did have quite a bit of discussion about what the differences were exactly, which boiled down to the fact that both are concerned with the same deep issues, but learning analytics is maybe broader in scope and using more qualitative approaches to data and not so dedicated to data mining methodology as EDM. Ryan gave an overview of the work he has been doing around behaviour modelling from data generated by some of Carnegie Mellon Cognitive Tutor programmes, and how they are using the data to redesign actives to reduce for example students going “off task”. Again you can access the talk from the course moodle site.

Next week I’m hoping to be doing a bit more diving as the topic is Sematinc Web, Linked Data and Intelligent Curriculum. Despite the promise, there really isn’t that much evidence of linked data approaches being used in teaching and learning contexts as we found with the JISC funded SemTech report and more recently when Lorna Campbell and I produced our briefing paper on The Semantic Web, Linked and Open Data. I think that there are many opportunities for using linked data approaches. The Dynamic Learning Maps project at the University of Newcastle is probably the best example I can think of. However, linking data within many institutions is a key problem. The data is invariable not in a standard form, and even when it is there’s a fair bit of house keeping to be done. So finding linkable data to use is still a key challenge and I’m looking forward to finding out what others are doing in this area.