Tradeoffs

Today was a day that made me think about the tradeoffs we often have to make working in development, or in a startup.

Today, for the first time since we started, I taught my class alone. We have three 1.5 hour blocks in the day, and for the first two, I taught my class alone.

It was wonderful. I co-taught with another teacher for the first half-year of my fellowship, and it was pretty disastrous. I had soured on the whole idea of co-teaching, and the final year and a half of my fellowship with Teach for India was definitely more successful as a result of having an independent class.

It was nice to do things just my way today, to manage, be me, etc. I missed it a bit. Then I reflected a bit, on how lucky I am to have wonderful teachers, Rohita Kilachand, Nikhat Aga, Lara Velho, and Aditi Agarwal to teach with. It eases a lot of pressure, it’s less tiring. While teaching independently was nice today, when I consider the progress we’ve made co-teaching  versus what I achieved independently, I’m quite lucky to be teaching in the system we are.

So I’ve traded some independence, some comfort, but gotten a lot less stress, and much better results.

We had an interesting conversation about tradeoffs after school, when talking about how we plan to use data going forward to inform our instruction. Our principal, Gaurav, queried over the past year or two, how much of our time using student data went into inputting, and how much into analysis. I answered that by the end of my last year, I was spending about 20% inputting, and 80% analyzing. Then the others answered, and I was amazed to hear that it was the complete opposite for them, including two who spent 100% inputting and 0% analyzing.

These are all teachers who are totally invested in the idea of data-driven instruction. But the level of data they needed to collect and input was so high and minute, that by the end, they had burnt out and just couldn’t spend the time to analyze it.

We came to a decision that we would be better off gathering less intensive data, so that we could actually analyze it. Another tradeoff. Surely, our data might not be quite as specific and granular as it was previously, but the fact that we’ll actually analyze it and use it to inform instruction will put us in a much stronger place.
So we have to make tradeoffs. One reason this work can be so hard is the absence of perfect solutions. But that’s another reason why it’s so rewarding to succeed.

Today was another great day.