There is a bit of a debate going on about ‘frictionless sharing’ – see (Martin Weller and Brian Kelly).
I’m not sure I understand the issue…
In my view, frictionless sharing is a Good Thing, because it means sharing without effort. It is a Good Thing, because
- you may explicitly want to share and can avoid the effort this used to take in The Old Days (like the way I share my thoughts here, or tweet things that strike me); or
- you may not care too much about what you share, but others can benefit from it anyway (like how I share what I listen to on last.fm or spotify, or when and where and how long I run on runkeeper).
In fact, I would argue that you often benefit in unexpected serendipitous ways in the second case as well: I sometimes get music pointers from people based on what they knew I listen to…
It can become a Bad Thing when you don’t want to share but do anyway because you forgot about it as there is no effort involved. The examples Martin gives of “Bad Sharing” are not so convincing for me: rather, they illustrate that capturing what you do could be more sophisticated (making the distinction between you and your daughter listening to music) or capturing your intent could be more precise (so that people know you were collecting those articles as examples of arguments you don’t buy.)
Better examples of Bad Sharing occur when you actually do something you are ashamed of, or disappointed by: you didn’t want to read that silly gossip story, or you really wish you hadn’t eaten that junk food, or … (I’ll happily leave it to your imagination -experience?- to fill in the blanks.) But you did. And you shared it. And now you hope nobody noticed…
I am a bit in doubt about how big that problem of Bad Sharing really is. It relates to privacy – obviously. I also have questions and doubts about the role of privacy – it is, after all, the right to lie, or to not answer questions…
One way to live in a world of frictionless sharing is to live your life so that there is nothing you feel you need to hide about what you do – maybe I just helped you to find that New Year Intention your were trying to come up with? And, if things go wrong, then at least for now, as long as sharing is not truly frictionless, you can just say that it wasn’t you, but your daughter or son, father or mother, cat or dog…
… and you will get this reply:
It’s 2012… You may have made some new year resolutions… I have: focus on my Family is one of them…
So, I’m off to London with my partner, kids and dogs! We’ll be back on 9 January.
You can contact ariadne@cs.kuleuven.be if you want to get in touch with my team before my return.
Then again, you may want to focus on YOUR new year resolutions
Thanks again to all who sent suggestions for things to do, places to go, etc. via Facebook, twitter and google+.
VERY best to all of you!
No, this post is not a review of 2011 – rather it’s about the reviewing work I do…
I just finished one of my last reviews of 2011. The last of many: I probably do about 80-100 paper reviews per year.
There are some nice rewards to doing reviews: it helps with maintaining a healthy dose of reading – something many of my colleagues find challenging, and one of the reasons we developed TinyARM. You do get the occasional gem to review: a paper that really helps you to understand something, or look at it in a different way, or just a nice paper – and reading people who cite you is always nice too.
But there are many things that I find rather frustrating about reviewing papers… It is actually quite a bit of work. It is rather odd in my view how people sometimes expect you to just be happy to be asked to review. The message sometimes reads more like an order than a request. (Don’t do that if you want me to review for you!)
Also, there is something a bit dissatisfying about the process: you do the review and then the editor takes over. It can be weeks or months before you hear about the decision that was made regarding the paper. Rarely will the editor get back to you for further clarifications. Often, you don’t get to see what other reviewers thought of the paper. Or what was decided.
Probably 80% of my reviews are very negative (reject or requires major revisions and a new review round). Still, my main reasons for rejection are really simple to avoid:
- language issues: English is not my mother tongue either. It is not fair that those who master it well have less difficulty to get their results published. But that is not an excuse for sloppy writing. It keeps amazing me how often I read, re-read and re-re-read sections in papers and then still don’t understand the line of reasoning. Frankly, at a certain moment, I decide that it is not my problem but the author’s.
- evaluation: It’s wonderful that people think they’ve invented the best thing ever that solves all the problems in the world. But if you want me to be impressed, then your idea should be really, really, really convincing, or I’d rather see an evaluation that tries to honestly assess your results.
- related work: even if your idea is really great and you’ve evaluated it and it actually does seem to work, it is really important that you indicate how it advances the field and how it relates to what others have done – otherwise, it just reads like a nice solution rather than a scientific contribution.
- analysis: many papers I review just describe a piece of work – no analysis of pros and cons, of when the solution works well and when it doesn’t, of the trade-offs made in the design, of other approaches considered, etc. That leaves a bit too much work to the reader in my opinion…
I do worry a bit that I may be getting old and grumpy – an 80% rejection rate and maybe less than 5% accept-as-is rate is rather harsh. I’ve actually asked my post-docs to tell me when they think I’ve become so negative that I should stop doing any reviews at all!
But I also think that, if we all were a bit more strict about the reviews we do, our lives as readers would be so much more enjoyable – just think about it: there would be nothing published but well written interesting papers…
The coming week, I will be ‘facilitating’ a #change 11 session on ‘learning in time of abundance’…
I put ‘facilitating’ between quotes, because I am not completely sure what will happen or what I’m supposed to do – kind of like what often happens to me in Real Life too … As in most courses I ‘teach’, I expect that I will be the one who learns most…
What I mean with the term abundance is that, for most of us, we now live in a world of plenty, mostly as a consequence of Moore’s law and its many variations regarding processing power, memory capacity, network connections, etc.
- One result is that we are now more connected than ever before.
- We are connected to the digital networks, with bandwidth available virtually everywhere.
- We are connected to information, to the extent that it has become as much a problem as an affordance in the form of information overload or filter failure.
- We are connected to one another, through email (40 years old), mobile messaging, and of course social media.
- We are connected to all sorts of things, sensors that can track much of what we do, in a quantified self kind of way (see also the Copenhagen Wheel, the jawbone up, runkeeper, etc.).
- Secondly, these connections offer us opportunities to be much more open and transparent in how we live and work and learn. This has obvious consequences for how we do research: think open access, but also much more radical open science or science2.0 tools and initiatives.
- The third effect that I see is that it has turned our world into one which is ‘always on’. Think of the #change 11 experience itself: there is no obvious start or end the way that used to be the case when we had ‘classes’. Learning happens all the time, kind of everywhere (including, of course, twitter and facebook, if you want to start exploring).
I had the distinct pleasure and rather humbling honor of doing the opening keynote at De Onderwijsdagen (education days?) in Utrecht, NL. My talk was on learning analytics, a topic that is really booming, especially in The Netherlands…
What struck me this morning is how technology has sped up how we interact and debate nowadays.
On twitter, #owd11 became a trending topic within two hours after the start of the conference. There are hundreds of tweets by now, and if you think twitter is all empty talk, then you should really read this stream! As a presenter, this is a wonderful way to get Much More feedback on what you presented than you could ever hope to get in coffee breaks!
My slides are on slideshare:
The recording of my talk is available on youtube:
Trendmatcher did a follow-up interview, which is posted on cinch:
We are fours hours into the conference, and there are 5 posts on the conference blog.
And all this ‘just happens’ while we still talk and have lunch and go to presentations, etc.
I am no doubt a bit of an techno-optimist, but it seems to me it is hard to not see the Added Value of technology in this context. Only 5 years ago, creating this kind of web presence would have been a major undertaking - now it ‘just happens’… These are rather nice times to live in!
Doing a talk on innovation at Agfa on Wednesday…
Think I will structure it along three themes:
- connected (social media, linked open data, …),
- open (data, learning, science, publishing, …) and
- always on (ubiquitous, mobile, …).
Suggestions? Examples?
(Will share back… Duh!)
Wolfgang Greller made some interesting comments on his blog about an interview with me on Learning Analytics.
I’m a bit puzzled by his remark that
Duval being a computer scientist strongly believes in the power of data and the revelations it holds.
Actually, I am not sure what would be the alternative to ‘believing in data’ – not believing in data? Isn’t confronting theories with data one of the core activities of any science?
For me, there lies one of the most important promises of learning analytics: as a research domain, technology enhanced learning is too much a field of opinions – maybe learning analytics can help to turn the field into more of a collection of experimentally validated theories? Into more of a science?
I’m not sure I understand the problem that Wolfgang seems to have with data. Of course, a real issue is selecting what kind of data are relevant: we may not need to know shoe size, or hair color, but do we need heart rate? blood pressure? emotional state? what a learner has eaten? where she is? with whom? etc. Making that selection of what data to consider is really tricky. And recording it correctly is no panacea either. Nor is analyzing it and interpreting patterns. But that doesn’t mean that data ‘is full of bias and opinionated thinking’, as he writes?! What is the bias and opinionated thinking in the speed of light?
Rather tellingly, Wolfgang also seems to believe that algorithms will never be able to factor in the influence of ‘problems at home’. Frankly, I think he underestimates somewhat how algorithms can be rather sophisticated…
I guess I agree much more with Wolfgang when he writes about how important human communication is. Of course. Otherwise, I wouldn’t be blogging, giving interviews and doing keynotes!
Anyway, looks like we will have something to discuss next week at De Onderwijsdagen !
No, this is not a post about the Greek crisis. Or at least not about the crisis most people talk about most of the time. Rather, I wanted to share a few reflections after the EDEN Open Classroom conference. As I tweeted a few days ago:
Crisis strong theme in opening session of EDEN conference this morning. May be due to location (Athens, Greece). Or to situation in schools?—
Erik Duval (@ErikDuval) October 28, 2011
I always enjoy meeting my Greek friends, and am quite impressed by the many nice local initiatives: Ariadne now has a Greek president, some of the projects we are involved in are coordinated by our Greek friends, etc.
The EDEN conference ran under the heading of ‘never waste a crisis’ – sounds a bit like management speak to me, but I do admire the way that my Greek friends try to think of the current situation as an opportunity for change…
I was quite impressed by the presentation of Kirsti Lonka on engaging learning environments and by Stephen Harris’s talk on ‘Factories No More‘. They both talked about innovative physical spaces that enable learning in ways that most current school or university buildings prevent rather than promote…
And I was much intrigued by Kirsti’s reference to Mihaly Csikszentmihalyi, a man noted ‘for his notoriously difficult name‘, but also for the concept of flow, ‘the mental state of operation in which a person in an activity is fully immersed in a feeling of energized focus, full involvement, and success in the process of the activity’ – not surprisingly, a state you can achieve with students when they feel competent and deal with challenging tasks… Sounds a lot like what I aim for with my students….
As I mentioned over at Google Plus a week ago,
Family will visit relatives in Mali in 2 wks from now. Looking for good laptop deals to help local kids. (2nd hand is fine!) Re-sharing=awesomness!
I’ve thought of project gutenberg. Maybe openoffice. Any other suggestions?
Thanks to Beki’s blog, I came across the CHI11 article on Fit4life – which I somehow missed before, a nice illustration of how social media help to discover relevant research…
The article makes some critical remarks that are relevant for our work on learning analytics and research2.0…
One important point is that you have to be careful what you measure: an optimal Body Mass Index (BMI) is not the same as perfect health. In fact, this is more problematic even in learning or research, where it seems to me that there is less consensus on what are relevant criteria… Do higher assessment scores always indicate better learning? Are papers with higher numbers of citations always more relevant? Are researchers with a higher h-index always better researchers?
Another important point is to question when persuasion becomes coercion. This is of course highly relevant in a learning context: I guess most of us agree that it is preferable to discuss with students what they can or should do, rather than forcing them to do it. Yet, after I explain to my students why I use twitter in class, I do tell them they have to – or what they do just doesn’t count for the course. And this is just as relevant in research: as a mentor to my team, I try to discuss with my PhD students what they can do for their research – I don’t just order or force them to do it. But if they don’t follow up on what we agree to together, then it won’t be too long before our collaboration is over.
These are really two good points: people and health (and learning and research!) cannot be reduced to numbers; we shouldn’t create Big Brother applications just to become more successful at learning and research.
All true. But I also kept wondering while reading the paper… So what? These issues don’t imply that it is not helpful to have tools that make suggestions? Or to have tools that track behavior and visualize it? To stay in the Fit4Life context: do not nikeplus or runkeeper help people to be active and track their progress on self imposed goals? Maybe it isn’t always clear what to measure? Maybe it is a thin and careful line between coercion and persuasion? Maybe support can be enslaving rather than empowering at some stage – but that doesn’t mean that we should leave people without any support?
This is worth exploring further… Do you have examples of support that turned into coercion? Of (self) tracking applications that cross the Big Brother line? Or maybe you’ve been using or developing such applications that make others uncomfortable? I’d love to hear about your experiences…
