Learn2Mine and Skill Trees

This post will not be reflective of my team's ongoing Galaxy Testing project as we have not met since my last post (we will be meeting and working tomorrow morning). So, instead, I will detail my latest work on my own research in the Anderson Lab here at the College of Charleston.

Learn2Mine utilizes gamification in order to help teach students data science. My latest work on the application is on revamping the way in which users view their skill tree.

Fig. 1 - Old Skill Tree

The best way to talk about these concepts is to use the physical images. The initial skill tree implementation is able to be seen in figure 1. This skill tree used a regular block method to represent skills that users have unlocked, learned, or mastered. This method seems a bit too cut and dry, though. For starters, we had 2 skill trees with this current implementation to represent pattern recognition techniques in one tree and R programming skills in a different, second tree. Also, if a skill was 'locked', then users were not able to work with that skill.

Fig. 3 - Classification Subtree

Fig. 2 - New Skill Tree Prototype

Now we have departed from that. Figure 2 shows the entirety of our current skill tree prototype - this prototype is created through a javascript implementation of Cytoscape (a flash-based graphical environment). We give a  hierarchical progression of skills that are represented in tree-form. This is not strict, though; this hierarchy serves more as a guideline for what order we feel users will learn the best. There are many different progressions one can take through the skill tree. For example, you can go down the classification branch in the tree (as seen in figure 3). This will have you learning the basics of the K-Nearest Neighbor algorithm, Partial Least Squares Regression, and Neural Networks. We also have a mastery lesson for K-Nearest Neighbors that requires users to implement optimization techniques in order to improve their KNN classification over a specific threshold.

We make new lessons just about every week for Learn2Mine, as it is going hand-in-hand with Dr Anderson's Data Science 101 class. This is functioning as a replacement for other workflow management systems that implement data science techniques. Typical systems used in DATA 101 include Weka and RapidMiner - these systems have lots of flaws, flaws that we are trying to combat with our system, while also focusing on the educational aspect of this process. This launch in the DATA 101 class is also functioning as our pilot test deployment of the system.

Music listened to while blogging: 50 Cent and J. Cole