These are chat archives for FreeCodeCamp/DataScience

30th
Mar 2017
Hèlen Grives
@mesmoiron
Mar 30 2017 02:19
@evaristoc thank you; it's one of my fav topics. I'll comment later. I am not only interested in learning analytics but also why we chose a strategy I.e going along the predefined path or deviate. What influences learning path choices even when they might be sub optimal. Formal learning vs tinkered informal learning.
CamperBot
@camperbot
Mar 30 2017 02:19
mesmoiron sends brownie points to @evaristoc :sparkles: :thumbsup: :sparkles:
:cookie: 332 | @evaristoc |http://www.freecodecamp.com/evaristoc
Hèlen Grives
@mesmoiron
Mar 30 2017 05:49
@evaristoc the first few lines are promising; however I am curious to why they choose collaboration as this might be overstated. One can end up with a product one doesn't want and it is not the same as casual help. A nod is different than a real hand when be pulled from a pit. Even teens interact often like lose particles bumping into help.
Hèlen Grives
@mesmoiron
Mar 30 2017 06:20
One interesting line is network residue; I want to use that concept in economic performance and usefulness as metaphorically seeds can lie dormant and contribute in unexpected form later. A form of social capital spread. A way to calculate voluntary impact on large scales. One side note. Collaboration efforts should largly fail as success needs huge input space. There's a difference between personal go along success steps and the monumental. Much the same like matter and dark space. It can also be a gauge of difficulty level of ideas. Most ideas are more difficult then at first glance. So data might not capture these aspects.
evaristoc
@evaristoc
Mar 30 2017 07:39
@mesmoiron nice you enjoyed reading! :)
Hèlen Grives
@mesmoiron
Mar 30 2017 11:27
In the MOOC-study that I'm currently reading one of the first things that stroke me - building on my last comment - that the design of the environments can limit research. Of course it is lower hanging fruit to get data from say Linkedin and combine this with the course outcome. However it is a narrow way of thinking about skill building as we like to have those skills pay off in a narrow job related content. The data would be more interesting (as I would have done the study) to combine the outcome with online social data. In order to do this one needs more account linking. Say my course on forensic accounting increased the amount of tweets, linkage in this topic. To see how people put their skill to use one should not only confine themselves to the job environment. It might be that people take up tasks like cheat sheets, blogs, community work etc. These are all off springs of the course and create a notice-able but hard to combine data stream. In order for MOOCs to present this kind of work it also needs a portfolio space not only for papers but for such things as thoughts and comments. Because like cases and open questions they produce answers where the gained knowledge is presented. Only for now the structure obscures the quality of the comments. We can count how many, but not the quality in a comprehensive overview. This is a feature I would want to have created myself in order to capture what I see that is mostly hidden.