What if you are looking for an easy, accessible learner analytics that does not only map access and timings, but actually allows trainers and teachers to take a more in-depth look into the actual learning? THAT is what I am looking for. As I am researching learner interactions in a mobile accessible open, online course – I can see that access pushes forward learner interactions, and even meaningful, constructive learner interactions (research snippets will be shared soon, still getting my thesis together). Looking at Tin Can will reveal a bit of the future of learner tracking and open opportunities for future educational research. The most essential challenge that emerged from my research (at this point) was the fact that meaningful mapping of learning was lacking due to a number of reasons.
So this is where Tin Can enters (@tincanapi), and thanks to Danny De Witte (@paravolve ) for getting me back on this topic! Tin Can builds on SCORM, but takes learning/training data to the next level. What Tin Can does is enabling teachers/trainers and as such also researchers to see which type of learning appears within a learner community. Not only the formal learning (access to course content, etcetera), but also the informal learning, not only web-based but offline as well as online, and adding ubiquity to the analysis as well. So in a nutshell Tin Can wants to push learner analytics to the next level.
Tin Can is not yet in a version to provide a widely audience a userfriendly version, but … you should have a look at where they are heading (currently 1.95 version, so near public release). What Tin Can does is getting all the diversity of tools that all of us use to build brick-à-brack solution for learner analytics, and centralizing them so that it becomes more intuitive.
The problems tackled with Tin Can as it is developed:
- Mobile tracking – any device tracking: so allowing focus on the actual learning, no matter what device.
- Simulations: allowing to move away from the browser, into where the actual learning takes place.
- Educational (serious) games: gamification is a great way to learn, but it gives rise to a lot of learning challenges, let alone tracking. Currently a lot of serious games are being played by young children, which can actually show the actual learning going on (algebra, math, language…), but this (informal) learning does not seep through to the actual teacher/trainer… however, this could create a much more personalized and in-depth profile of the actual learning of a particular individual.
- Performance support: point-of-need, just-in-time learning is essential for learning. But the barrier with scorm was the actual ‘login, path….’ Which gave rise to a bit of demotivation. But Tin Can takes tracking outside of the LMS, as such it is much more directly accessible.
- Track real world activities: a webinar, training attendance… any tracking that is GPS enabled and such.
- Offline and long running content: spaced learning enhances all of our learning as we know, but this was tough to track, with Tin Can offline tracking will be enabled. Tin Can will not program spaced learning, but will provide data that will make it easier to develop spaced learning.
- Security and authentication: Tin Can will upgrade the security a bit: secure the security between a learning provider and the actual material. It will not be fully secure, but a bit more secure.
Deeper layers: multi-level tracking: in the cloud and on the device:
- Everything is learning: if we do something that increases our knowledge, this is essential to get an idea of learning: making a mistake, writing a blog, reading up on a specific topic …. The holistic learning environment.
- How it works: bookmarks are gathered (in Tin Can), book scanner (to add the books you read), tapestry api addition…. It is all about capturing learning events.
- Key enablers: tracking does not need to be done inside an LMS, it can be launched from anywhere. The asserter of the learning material, can be different from the learning event => as such everything on the web can be used as a learning object, and can be tracked for its learning, it does not be ‘scorm-enabled’.
- Free the data: LRS: this is a great concept, LRS is a Learning Record Store: the thing that accepts all the Tin Can data and enables other instruments to analyse the data. This is where specialized analysis comes in: improve content delivery, content analytics, point towards learning pathways…
- Training data portability: allow to take along training records.
- Personal data locker: that might give the learner herself/himself to learn more on their learning: ideal meta learning tool!
Major challenge: how can one filter out the signal to noise: what makes up relevant learning, what not?
And of course, if you look at Tin Can and put this next to MOOC’s and take socially intelligent agents into consideration, than you must agree that it will offer an amazing learning advancement for the next decennium. And the effect on educational research is ENORMOUS:
- How do people like to learn?
- Which learner profiles are there?
- Can you deduct a professional profile based on learner profiles?
- Can you filter out skills and consequent opportunities based on learning?
- Can you find topics that arise from learning … from what seems to be missing?
This will shed light on actual learning apart from neuroscience monitoring. Great!
If you want to learn more about Tin Can, have a look at their website here (http://tincanapi.com ), or get a broader idea watching this webinar.
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