Exploring Tuva's Embed Feature

At Tuva, we've heard from users they want to use Tuva's tool for analyzing their own data. We also noticed that occasionally, people analyze a dataset that they want to share in an interactive format. So, we explored this feature, and I'm creating this blog post as an example of how Tuva's tool appears within a blog/article format.

I'm going to explore this feature in the context of data about dogs.

Let's say we would really like to know the average height of different dog breeds. Fortunately, I had already found this data and uploaded it my personal account. Then, via Tuva's tool, I can embed the dataset here and show the results.

From this graph, we can hover over the red horizontal lines to read the different mean values. We can conclude working dogs are taller on average.

The key to using any data analysis tool is to ask a guiding question, and assuming we have the right data, we can begin kneading the graph until we arrive at a visualization that answers our question.

Let's try asking a question that's a little more interesting: Based on this data, what seems to be the greatest influence on a dog's maximum life span?

After trying several different graphs to see if it would answer my question, I settled on two different graphs that seemed to answer it best:

The first one is maximum weight vs maximum life span.

The second one is maximum height vs maximum life span.

We will notice a downward trend in both of these graphs. At the same time, one of the variables, maximum height and maximum weight, seems to be more strongly correlated with maximum life span. Maximum weight is more strongly correlated to maximum life span than maximum height. So, from this data we can conclude that the maximum weight of a dog seems to have a greater influence on the maximum life span of a dog than maximum height.

What else do you think you can ask? What might be interesting to learn about dogs that we think this data can reveal? Play around with the graphs above and see what else you can discover.

Board Games: Competitive vs Cooperative

I'm feeling a general sense of frustration with competitive board games, like Settlers of Catan, Monopoly, Risk, and the like. When I play these games I end up in last place, or near to last place, and I'm left with nothing to do during the game. The rest of the players continue the fun as I then sit by myself, sip a drink and ruminate over the rule book to see if there was something I could of done differently. Was it my luck? My inability to strategize? Maybe a bit of both.

Enter cooperative board games: a few years ago I played a cooperative board game called Defenders of the Realm. At first I was drawn in by this game's attention to detail, visually stunning renderings and beautifully crafted backstory setting the stage for the players. Then, I was blown away by being able to get feedback on my strategy as a newbie from other players and also coordinate my moves with other players. I was hooked, and inspired.

Ever since I learned about cooperative games as a genre, I've been itching to design my own cooperative game. About four months ago I began designing and testing it out with friends.

Stay tuned for updates as I continue development! And in the mean time, let me know what you think of competitive and cooperative games in the comments section below. What do you like about them? What do you hate about them? How well does it hold your attention throughout the course of the game?

A prototype of the game I'm working on.

What are animals thinking and feeling?

Of all the TED talks I've seen, and I've seen pretty much every single one, I found this one especially moving. Carl Safina passionately holds up a mirror to who we are and the effect we have on the animals we live with. If you have 20 minutes to watch something today, watch this.

Balance Bike: A Learning Game

Over the past several months I've been hard at work learning to develop android apps. I just published my first app on to the Google Play Store. The app is called Balance Bike: A Learning Game.

Balance Bike is an educational game for young children to learn words, letters, and numbers through touch, sound, and speaking. The app currently contains six categories: farm animals, wild animals, birds, alphabet, numbers, and colors.

Balance Bike starts up with a home screen that allows kids to pick from different categories.

Once a kid picks a category, they're presented with a wheel of pictures. Here, they can spin the wheel.

The wheel eventually stops spinning and an animal is selected. On this screen, the first sound we hear is the name of the animal. The second sound we hear is the animal's sound. Clicking on the left and right musical notes repeats the animal name and animal sound, respectively.

Underneath the picture of the animal is a third button. Pressing the microphone, the app focuses on the word, asking the child to speak what they see. At this point, speech recognition is used to see if the word is said correctly.

TuvaLabs at the TechStars+Kaplan EdTech Accelerator

It's been a while since I've had time to write an update. The past month has been hectic. One piece of amazing news I'd like to share with friends and family is our acceptance into the TechStars+Kaplan EdTech Accelerator. You can read about the press release here.
We're working in their office space in New York City with eleven other amazing educational companies. The program started about a week and a half ago, and we've been learning a lot through their workshops and meetings they've planned out for us. As a team, we know our best has gotten us here, and with the feedback and criticism from the experts in this program we'll be able to take our process and skills to the next level.

I feel very fortunate to be working on new educational technologies at such a pivotal time in the history of education. Hopefully TuvaLabs will be able to create a positive and lasting impact in the analytical and critical thinking of students around the world. And in the long run, encourage more students to become data literate by engaging them with data that's from the real world.

And finally, a view from my seat:

Making Open Data Useful for Teaching and Learning

TuvaLabs makes open data useful by curating it into easy to use data sets and then developing rich activities around each data set. This enables teachers to bring data-based inquiry into their classrooms. These classrooms can range from civics, health, and history to science, statistics, and economics.

The way we enable inquiry into real world data is by focusing on three areas. First, we make sure students are able to ask their own questions. Then, we provide opportunities for students to explore, visualize, and analyze real data. And finally, we empower students to communicate their own findings. This means Tuvalabs is a space where students learn from local and global data, affording them to become aware and active members of their local and global communities.

In collaboration with Teachers, we’ve discovered that third graders are learning the basics of the statistical language from data about movies. Then, there are fifth grade students creating visual and verbal arguments from data around the income inequalities between men and women. Eighth graders from New Jersey are becoming aware of their community through local data on population and energy consumption data. And one more brilliant example is of ninth grade students in the Bronx learning data literacy from their local Bronx population.

Some of these students have gotten back to us about their learning experiences. One student said, "It is challenging. But we get to learn about Gender inequality and how it is affecting us." Another commented that "this was a very interesting topic to explore because I love to go to theme parks."

Occasionally, students and teachers want to explore a topic that's not yet covered on TuvaLabs. When this happens, they request data. For example, learners requested data about the demographics of the current US Congress and about the impact of Barbie's proportions on society.

The impact we've observed both inside and outside the classroom continues to motivate us. Every teacher that's part of the this community has contributed to making TuvaLabs the way it is today.

TuvaLabs Places First at the LinkedUp Vidi Competition

We went to Crete this past month to show our work at the LinkedUp Vidi Competition, which was held at the ESWC conference. We presented how TuvaLabs is making open data useful for teaching and learning. The competitors' presentations were very impressive and polished, and included Rhizi, Konnektid, DBLPXplorer, LODStories, eDL mobile app, Solvonauts, and agINFRA.

Making Open Data Useful for Teaching and Learning

Curated Data Sets around a Variety of Topics
After presenting to conference attendees, we received some great feedback and had the opportunity to connect to and build relationships with the open data community and several other startups. Several meetings included people from the Open Knowledge Foundation, Vrije Universiteit Amsterdam, Mozilla Science Lab, software carpentry, and many more.

The Winners from the Vidi Competition

We feel very fortunate to have placed first, and are very pleased with the 3000 Euros we’ve been awarded. After the competition, the various LinkedUp successes were interviewed about their experiences while being part of the competition. Below, you can see a compiled video of these interviews. Enjoy!

Giving Human Form to Audio Visualizers, Version 1

I'll upload a video of this audio visualization when I have some time. In the meantime, try the code out yourself. I'm still tweaking it to figure out what might look best.

Some requirements to run this code:
1. Download OpenCV for Processing from here: https://github.com/atduskgreg/opencv-processing/releases
2. And download and install from here: http://opencv.org/
2.b. Probably need v2.4.5 of opencv from opencv.org, but I was able to get it to work with v2.4.9

I'm also using processing 2.1.2.

And finally, copy this gist into processing: