For starters, there is not an effective interactive site to learn data science and to perform data science algorithms all in one place. Many people have used programs, such as Weka or RapidMiner, in the past to conduct algorithms and take results away for their own use, but these results are often confounded and require a large amount of computer science expertise to use and understand. Weka's outputs do not contain much information and is not meaningful unless you are an expert at the algorithm in which you are conducting. RapidMiner has a confusing workflow interface that may confuse new users, leading to a very steep learning curve for the software. Both of these informatics platforms, though, do not bother teaching users about the algorithms or how they work - they merely give a basic introduction as to how to use the software. It is in the name, RapidMiner - rapid mine. It really is just used to mine information. The name of my software, however, is Learn2Mine - you can learn to mine data, but you can just strictly mine if you want. The options are open and that is one of the crucial aspects of Learn2Mine - freedom of usability and pedagogical ability.
A lot of programs, though, are pretty effective at actually teaching concepts to students. I used Rosalind in the past to actually learn Bioinformatics concepts and apply my programming knowledge to actually conducting and performing basic bioinformatics algorithms. It is an effective program, but it has pigeonholed itself to only catering to computer scientists whom have a specialized interest in biology. Learn2Mine aims to take this idea and expand it to any and all domains. This has been pioneered to a very small extent. There currently exists 3 case study lessons where students have to fill in missing code in order to finish problems relating to algal bloom classification, stock market investments, and fraudulent transactions. This will be expanded in the coming months as lessons will be rolled out for bioinformatics, artificial intelligence, and data mining. The bioinformatics lessons are listed because it is a specialization that I have adopted at the College of Charleston by taking multiple bioinformatics classes and by having my data science concentration be in molecular biology. The artificial intelligence and data mining lessons will be included as there are classes at the College of Charleston which will utilize those lessons toward the end of the semester, as a way to evaluate students.
Learn2Mine has other parts about it that stand out from other programs. It is not just about being able to learn and perform. Learn2Mine takes the next step and is a completely cloud-based technology. You need not worry about having to install Learn2Mine on any machine or any kind of dependency. If you want to submit a lesson at the library and then do one at home, then you are free to do that because of our cloud-based nature. Below is an image created that shows everything that goes into Learn2Mine:
So Learn2Mine can teach data science and perform related algorithms, but what is going to keep people motivated to use Learn2Mine? Interdisciplinary fields need new ways to approach their teaching. Learn2Mine has coupled its development with gamification. Gamification, not to be confused with "edutainment" which is a video game with a bonus educational goal (e.g. You beat the boss, here's a fact about programming languages), is the manifestation of a lesson that a student completes with motivations stemming from techniques that are inspired by video games. In Learn2Mine, the techniques used are currently: the implementation of skill trees, leaderboards, and achievements (in the form of badges). My next post will focus on the gamification elements of Learn2Mine and how they have been implemented and what is next to implement.
Music listened to while blogging: Ghostland Observatory and Nine Inch Nails
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