Skill Trees
At the heart of Learn2Mine is the virtual portfolio (the Google App Engine side of Learn2Mine). The virtual portfolio contains many things, but I want to focus on the skill tree. This is located in the profile section of the site and is unique in that it shows the individual progress of a user. When you first go to the site you will have a skill tree that is all gray except for the root of the tree. At the root is a basic intro to data science badge which you get for just joining the site - a slight motivation in order to start you off on your badge-earning journey. The badge directly under the root of the tree is currently an "Uploading" badge. The uploading badge is being scrapped as we are working on a tutorial which will replace the uploading badge - so we will have a "Tutorial" badge in its place. More on the tutorial in a later post, though. So the skill tree branches off in 2 main sections as of right now - and this will definitely be expanding in the coming months. The left side of the tree is all about learning R programming (anything from basic skills, to file i/o, to writing classifiers). The right side of the tree is about using the built-in machine learning algorithms that we have inserted into the Galaxy side of Learn2Mine. A depiction of the R subtree as it remains right now can be seen below. Note that badges that are gray are unearned and badges that are colored in have been earned. Green badges are basic learned badges - typically easier lessons. Blue badges are mastery badges, a step above the green badges. Finally, the gold badges are advanced badges, which are a step up from the blue badges.
Achievements/Badges
So I just mentioned badges. You may be wondering where that came from. Well, badges are a way to incentivize users to complete lessons. These badges are, effectively, achievements for completing certain lessons. When you earn a badge it will take its place on the skill tree and, additionally, on your home page of Learn2Mine you will see a fully-flushed list of your badges. But so what? You can have badges and just show them on Learn2Mine - that's kind of boring. Well, we are in the early stages of building a search feature so you can compare yourself with your friends and try to out-compete them, but, more importantly, we have integrated our badges with Mozilla Open Badges. Mozilla Open Badges is an online standard to recognize and verify learning. You are able to show your badges off on LinkedIn, or put them on a resume. Really, you can do anything with them. What gives them credibility, though, is the JSON that backs the badge images. It is there that proof exists of when you earned the badge, where you earned the badge (for now this is always College of Charleston and Learn2Mine), and all other metadata that would be useful to have. So this has much more credibility than all your friends endorsing you for skills in LinkedIn that you may not even possess. Eventually, we want other institutions and teachers to use Learn2Mine to create their own lessons, so there would be new institutions backing certain badges and it would really just help the site flourish and help in resume processes.
Leaderboards
The leaderboards we have built into Learn2Mine are very primitive as of right now. Currently, we have leaderboards for 3 of the built-in Learn2Mine tool lessons - k-Nearest Neighbors, Neural Networks, and Partial Least Squares Regression. The idea is that users will compete against each other to get better scores on certain lessons. For example, the k-Nearest Neighbors lesson judges a user's score by the amount of classification they get correct when using our pre-built test set. This is something that desperately needs re-working but is not of as much importance as the rest of the site. A reason that re-working has to be done is that if someone is smart enough with R programming, then they can currently manipulate output to get 100% of the test set correct, though that would be a case of either cheating or overfitting. Our k-Nearest Neighbor leaderboard can be seen below (and it can be seen that it really hasn't been used):
Game Over? Instant Feedback
When you submit a lesson to Learn2Mine through Galaxy, you are able to submit as many times as you want. Let's take the standard R lesson for this example. That lesson has 12 questions that you have to answer with R output. On a quiz in a class if you missed 4/12 questions then you would end up with a 66, assuming equal weight, but, on Learn2Mine, we just let you know that you missed whatever 4 questions you missed and then allow you to retry it. As many times as you want. We let you know exactly what you missed and, in certain cases, we may even provide hints for you to take in to mind when coding up your answers.
Music listened to while blogging: Childish Gambino and Tech N9ne
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