Ladder was born out of necessity. We as a team are privileged to attend Cupertino High School, one of the best high schools in the area and in the nation. However, all of us have felt or experienced severe student stress either firsthand or secondhandedly.
 This continuous, omnipresent, pressure to excel has caused repetitive student suicides at schools such as Gunn High School. We spent the first three hours of this hackathon researching causes of student stress in high academically achieving areas, and were not so surprised to see that the number one reason students didn't do as well as they could have in school in these areas was due to constant distraction from social media websites/other attractions on the internet. Ladder was born from this knowledge.
 We set our minds and our hearts against this problem and came up with a solution we are truly proud of. 

What it does

Yes, it updates over realtime, and yes, we already offer it for web, iOS, and Android, but Ladder is much, much more than your classic planning app. Ladder allows you to organize and keep track of your homework assignments with a beautiful user interface powered by delightful animations - but thats only the surface. Beneath the surface is a powerful supervised machine learning algorithm, which we like to call M1. Learn more about how it works in the next section.

Another huge factor that sets our application apart from traditional ones is our companion app for Pebble watch, Scalae. You can think of Scalae as a personal trainer for your grades. She'll make sure you stay on task by vibrating whenever you aren't writing for long enough and will even send you motivational text messages when you seem to be working slowly. In addition to this, all of Scalae's data is integrated with Ladder's and helps power our central machine learning algorithm, M1.

How we built it

The heart and soul of our application is our supervised machine learning algorithm, which we like to call M1.  M1 captures the vast amount of data that mobile devices and computers are so adept at capturing these days, such as the amount of time you are writing vs being distracted (using Pebble), the frequency that you need more time than you scheduled for a certain subject, and your grades in each class (just to name a few), and compiles and uses the basic principles of geometry to analyze and find trends between various data points. These trends are later boiled down into meaningful data which is used by M1 to generate a customized homework schedule for you geared towards working efficiency and maximizing your grades. If you're interested, you can view a complete explanation of the logic behind our learning algorithm (

We integrated a variety of APIs  and technologies into our project. We built the application using Ionic and AngularJS, which allowed us to test and deploy on iOS, web, and Android. M1 pulled and read all of its data from clusterpoint, which is integrated in Ladder. Twilio is used in both Ladder and Scalae (our Pebble companion app) in order to achieve a more personal connection with the user (ex. sending motivational text messages). We integrated Chegg to show users where to find additional resources if they get stuck on a homework assignment, and we're working on integrating some of the technologies in IBM Bluemix to make M1 more robust. We used CloudPebble to develop our Pebble companion app. 

Challenges we ran into

M1: Supervised Machine Learning Algorithm
    Writing M1 was definetely the most head-breaking and time-intensive part of Ladder's development. We spent about 5 hours planning it out, 2 of which were wasted in arguing over what would work and what wouldn't. Some of our members had previous experience in machine learning, however, so they guided and lead this aspect of our app.

      We had trouble dealing with and integrating a lot of the APIs we used, but the hardest one and most time-       intensive one was Twilio. This was because in order to integrate Twilio, we needed to write a server that would carry out the requests that Ladder and Scalae would make. We chose to write this in Node.js, and it is currently deployed on Heroku. We also spent some time working with the Twilio representative at the booth to overcome the credit card restriction, because none of us have credit cards.

    We all wanted to create a companion app for Pebble, but none of us had any experience as Pebble developers. We attended two Pebble workshops, however, and managed to pick up a lot about the Javascript SDK. We opted to use the JS SDK to build our Pebble app, and CloudPebble just worked and was amazing to develop with. The only reason this is in the Challenges section is because of the amount of time it took us to get started, which in retrospect may have been due to late-night tiredness.

Other than these, there were no large, looming, problems we faced as developers. Ionic, Pebble, and Clusterpoint were fun to hack with, and we picked them up fairly well.

Accomplishments that we're proud of

 Building it. We knew we had a large and ambitious project on our hands when we brainstormed this idea on Friday, but we decided to take a risk and choose it instead of other, easier to finish, ideas. This, in retrospect, was because this was the only idea out of all that we had that we were really passionate about. Its still hard for us to believe that we wrote a learning algorithm of the quality that we did in under 36 hours.

What we learned

 We learned quite a bit about teamwork throughout the course of this hackathon. We knew we would each have to stay on top of our game if we wanted to even come close to finishing a project as large as ours. We decided to meet and walk and talk as a team every two hours so that we would all know what we were doing, where we were, and whether we were ahead or behind schedule.

What's next for Ladder

 1) Making M1 more robust. M1 is the core of our app, but it was only built within 36 hours. Our first step will be to  refine and perfect it. 

 2) Finding tutors. We want to roll out a feature which allows students to connect with other students who are doing the same homework at the same time and have better grades (all things which our app takes in already). This would allow a student on one side of the country who is great at math but horrible at writing to engage in a mutually beneficial relationship with a student on the opposite side of the country who is great at writing but horrible at math. We're very excited about this idea and we're eager to begin developing it and putting it into action.

 3)Improving Scalae. As Ladder grows, so should Scalae. We're eager to learn more about the capabilites of the Pebble watch, and make Scalae have a similar UI feel through animations and color as is older sister. 
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