Wow, you actually 3d printed some of the components. Very impressive! Way to set the bar. In your "about me" section, you mentioned that you have taken electrical engineering courses. Did you take the NAIT Diploma program or?

Cheers

Hey Shawn, thanks! I was in the EE Nanotech program at the UofA for many years (2004-2011'ish), but I didn't graduate. They didn't do 3D printing or robotics then, but it did give me taste enough in related fields to continue related hobby work.

I survived first year engineering and decided to finish my degree online with comp sci because it seems more flexible :)

Nice work, Tyler. Great data set.  What Excel formula or function yielded the vertical line and the "y=0.146x-2.3843" function?  Or did you enter the data into an Excel chart and automatically yielded the line and function?  Thanks very much.

Hey Susanna. The data was already linear so I just used Excel's 'show trendline' feature in its graphs. Automatic, like you said :)

Jon,

I agree with the points you are raising here.

There is a pervsuasive (and generally inaccurate) notion that learning is the acquisition of simple sets of skills. We seem to believe that out of that acquisition of skill sets that higher order thinking and problem solving simply emerge magically.

If that were the case then the recommender engine model would work great. Students would have the much heralded (of late) playlists of lessons to build those skills and viola!

However, this model excludes the most basic truth about learning which is that it is labor intensive, experience dependent, and therefore not realy programmable in the way that Sal Khan and other ed tech gurus seem to believe. 

As in all things, it ain't what you do, it's the way that you do it.

Having worked on recommender systems, especially adaptive ones, for my PhD and for some years afterwards, I do see that there are many ways they can have a place. But there are also enormous dangers and, as you suggest, having them drive a teacher-determined learning agenda is not smart. Collaborative filters of the sort used by Amazon, Netflix, etc, turn out to be less promising than you might at first think, sadly: either too crude to work (it's not about relatively static preferences, as in books or music, but about evolving learning needs that change as you learn) or too difficult to use (e.g. my PhD systems!).

I find small chunks of stuff to learn from (YouTube videos, StackExchange dialogues, etc) can be immensely useful when used by learners to achieve goals they have set for themselves: I have learned a great many skills that way, which are a necessary part of (but only part of) learning. And there is great value within a small, known community to sharing and ranking stuff that the group uses - the objects the bind, the ideas that connect, the shared cognitive artefacts - which can be greatly enriched with added visualization, analytics, and rich qualitative metadata, as long as these simply support, rather than drive learning, and reflect rather than dominate the group's dynamics.

As an interesting addendum to my post, Amazon's recommendations turn out to be far more benign than the media at first suggested: the recommendations come about as a result of people making backyard fireworks and doing science experiments. Context is everything, and context gets lost in large-scale recommender systems whose purpose is to sell stuff, not to support learning!

Jon,

I think that recommender systems can be good (and I know yours were/are). I was responding to the corporate instantiations in education in particular. 

Mike Caulfield had a nice piece about Netflix recommender engines not really recommended things for you, but rather recommending things you might like that they have rights to.

I also agree that I have sought out YouTube and Stack Overflow and other similar places to support my own learning, and they have been immensely helpful. The difference there is the self-directed piece I think. 

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Tyler,

I just finished the book "Life 3.0 Being Human in the Age of Artificial Intelligence" by Max Tergmark (MIT). Extremely fascinating look at the questions humanity will need to deal with (now) if we are to have any measure of control over the possible future scenarios he presents for the coming AGI and superintelligence. The last chapter deals with the subject of consciousness, how it relates to subjective experience, and why it may be entirely possible for non-biological consciousness. It's exciting to think that consciousness theories such as Giulio Tononi's integrated information framework for neural-network-based consciousness are being tested presently  In this light the current WestWorld is far more fascinating than the original from the 70's. Interesting post. Thanks.

The Life 3.0 book gets 4.26/5 stars on goodreads.com -- pretty impressive. I looked up the integrated information framework ("IIF"). Love how it attempts to deal directly with the hard problem of conciousness. I'll have to take a look -- thanks. (I haven't been able to take a stance on whether conciousness is necessary for general intelligence. There are many apparently solid arguments.)

Season 2 of Westworld is slated for Spring 2018. :)

This post was interesting to me as I consider my use of praise multiple times a day as parent and teacher. Jon Dron's comments bring attention to the differences in praising who a child is vs. what they do, in extrinsic vs. intrinsic motivation.

The value of teaching mindset, our beliefs about success, is becoming more understood in and out of school systems. Fixed mindset is the belief that success is based on innate ability whereas with growth mindset success is based on hard work, learning, and training.

As kids, we tend to grow up with fixed mindsets labelling ourselves  'good' or 'bad' at things. Carol Dweck has contributed to social psychology with theories of intelligence and has devoted her life to studying fixed and growth mindset.

I wonder if we can begin to shift the next generation of thinkers, if, as young parents and educators, we focus our efforts on specific behaviour - directed feedback.

Jon,

I found this really interesting as well, particularly given my recent forays into Self Determination Theory. I also just shared the article, and your comments with my students in a Learning Environments course I am teaching this semester.

Beautiful write-up, Tyler.  Do you know motion parallax?  I've dreamed of designing sensors to detect this phenomenon. 

I noticed you're profilic with blogging for this course.  What background do you bring to the course?  Mechanical?  Industrial Arts?  Sciences?  AutoCAD design?  3D printing?  Curious!  Thanks very much

Hey Susanne. Yeah, I've done a bit of research on parallax, the bulk of it resulting from this xkcd comic: https://xkcd.com/941/ ; and a bit of photo manipulation tricks (3D effects by combining two photos). Sounds difficult to make a sensor for it. Could go with 'blob' or particle tracking over time with a single moving camera, comparing the change of many objects' positions from different vantage points; or binocular vision. Mataric talks about it a bit starting on page 114 (9.3.5 Stereo Vision).

I'm just putting stuff online to complete the course, and I enjoy writing. I've taken a bit of electrical engineering, have tinkered with electronics for over a decade, and armchair philosophy has always been a passion of mine. Nowadays I have a wee lab in my basement where I wish I could spend more time doing nerdy stuff. Mostly I just babble. ;)

Thanks Tyler!   I'll definitely check out the comic.  Great reply on parallax, and congrats on the electrical engineering background.