The right way to do personalization is to prove you're useful first. Personalization is just a tool. If a new tool doesn't work better than the old tool, it's useless. There's no reason to use personalized education unless it works better than unpersonalized education. A tool needs to be useful.
Teachers are already overworked and, after having been burned too many times on supposedly exciting new technologies that fail to help, correctly are cynical about tech startups coming in and demanding something of them. If some tech startup isn't helping a teacher get something done they need to get done, it's a bad tool and it's useless.
Parents are leery of companies who say they only want to help and what corporations are doing with the data they have on their children, correctly so given all the marketing abuses that have happened in the past.
Kids don't want more boring busywork to do -- they get enough of that already -- and don't see why anything this company is talking about helps them or is useful to them.
If a company wants to succeed in personalized education, it should:
- Be useful, noticeably raise test scores
- Not require additional busy work
- Be optional
- Have no marketing whatsoever, only use data to help
I would like to see a company use the existing standardized tests required by several states, analyze the incorrect answers to identify concepts a student is not understanding, and then print short worksheets targeting only those missed concepts for teachers to hand out to each student. The worksheets would be free and arrive in teachers' mailboxes. If the teacher doesn't want to hand them out, that's not a problem, but test scores go up for the classrooms where the teachers do hand them out. So, even if most teachers don't hand them out at first and most students throw them away at first, over time, more and more teachers will start handing them out and more and more students will do them, as only helps those who do.
In both of these examples, a startup could set up from the beginning to run large scale experiments, showing different problems to different students, and learning what raises test scores, what designs and lesson lengths cause students to stop, what concepts are important and which matter less, what can be taught easily through this and what cannot, what people enjoy, and what works.
When a company comes in and says, "Give us your data, teachers, parents, and kids, and do all this work. Maybe we'll boost your test scores for you later," they're being arrogant and tone-deaf. Everyone responds, "I don't believe you. How about you prove you're useful first? I'm busy. Do something for me or go away." And they're right to do so.
There likely is a way to do personalized education that everyone would embrace. But that way probably requires proving you're useful first. After all, personalization is just a tool.
4 comments:
Luis von Ahn, when presenting at the Collective Intelligence conference this year, mentioned that they will start teaching math relatively soon in the future.
Thanks, Panos, that's excellent news! Love Luis' work, can't wait to see what he comes up with!
I appreciate the sentiment, but also think you vastly underestimate the amount of effort required to:
- access the existing standardized state test data
- analyze the incorrect answers
- identify concepts a student is not understanding
- print short worksheets targeting only those missed concepts
- deliver them to teacher's mailboxes
The analysis component is actually the simplest one to solve. Getting at the data, cleaning it up, and logistics of distribution are the stuff nightmares are made of.
Every district is different. This is precisely what the Gates initiative was hoping to address, btw.
And all of this needs to be absolutely free. And absolutely reliable, right? And based on solid, large-scale data science. And no sullying anything with "marketing".
I agree that education is rife with useless crap, but egads, the world doesn't work quite the way you think it does.
Anonymous, I agree that, in the second example, the statistical analysis is a small part of the problem. Setting up for continuous experimentation on the worksheets is important but, once that's done, the ongoing analysis would only require a small team. Much bigger would be getting the initial grant funding (very hard), striking the initial deal to get access to the state test data (very hard), building a team of teachers who would be mapping incorrect answers to missed concepts and picking candidate problems to help (time consuming and expensive), and shipping the worksheets. But the task is made easier by not requiring heavy coordination with teachers and schools -- use the worksheets or throw them out, whatever you want -- which avoids the lengthy and difficult purchase cycle at most schools and in most districts.
No, it doesn't have to be absolutely reliable, just has to be piloted, raise test scores at least a little at first, and get better over time. You are right that it is precisely what the Gates initiative was hoping to address, but they botched it badly by coming in heavy-handed, making work for teachers, and not improving test scores from the beginning.
There is a reason I am not working on these ideas myself. Building these requires expertise I don't have. In particular, they require substantial initial funding, contracts to access existing data and problem sets, and a staff of teachers. The data science piece, while easy to mess up from the beginning, is not a huge part of the work once it is properly set up. Even if this isn't something I am in a position to build, there are people out there who could do this or something like it. There is a lot of money being thrown at trying to improve test scores. It would be good if that money actually raised test scores.
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