What if unified enrollment platforms were 10x better?

An emerging group of cities – including Washington D.C., Newark, Camden, New Orleans, and Denver – have adopted unified enrollment systems. With these systems, families can enroll in schools across the city via an online application system.

This is a huge step forward. For too long, parents have not had enough information or access to the public schools in their cities.

However, the new enrollment systems are still in their infancy. The best version of these systems could radically improve public education. Unfortunately, we’re very far from this endgame.

I. Early Wins: Access, Equity, and Ranking

Access: With the best open enrollment systems, families who can’t afford a house in a fancy neighborhood can now finally transparently apply to a school in a more wealthy neighborhood.

As a result of increase in access, a recent study in Washington D.C. found that the new enrollment regime would likely reduce segregation over time:

Screen Shot 2017-03-17 at 9.23.22 AM

Equity: In most cities, traditional and charter schools play a ton of games with enrollment. Traditional magnet schools use opaque entry requirements. Bad apple charters don’t take in kids with special needs. There is no equity.

With online enrollment platforms, these problems go away, as schools are no longer in control of their enrollment.

Quite simply: the algorithm is fairer than the enrollment clerk.

Ranking: These new enrollment systems also allow parents to rank their top schools. This is extremely important.

First, a family’s high desire to enroll their child in a school can now  be translated into an increased chance that they actually get into this school.

Previously, high desire meant little unless you were connected, wealthy, or dogged.

Second, ranking allows  parents to publicly signal to government which schools are most and least in demand (which will ideally affect opening, expansion, and closure decisions). It also signals to school operators what attributes make a school in high demand.

By analyzing ranking preferences, researchers in New Orleans were able to correlate school characteristics with parent preference:

Screen Shot 2017-03-17 at 9.44.39 AMRanking transforms family desire into actionable information.

II. Unified Enrollment Systems are Mediocre Platforms

In preparation for writing this blog, I spend an hour on unified enrollment system websites. It was not a great experience.

Here is the school finder homepage from Washington D.C. – I couldn’t even find a way to filter schools by academic performance!

Screen Shot 2017-03-17 at 9.50.14 AM

Here is the search function for Newark’s enrollment system – you have to download a pdf!

Screen Shot 2017-03-17 at 9.55.36 AM

By comparison, here’s the search page from Zillow:

Screen Shot 2017-03-17 at 9.53.10 AM.png

On Zillow, you can easily search for homes based on the most useful search criteria. Yes, Zillow is surely better capitalized than your average enrollment system, but even with modest funds a city should be able to do better than a downloadable pdf.

III. Moving From Equity and Ranking to Matching and Prediction

More sophisticated uniform enrollment could offer two extraordinary improvements: they could better match families with schools, and they could better predict how any given student would do at a school.

Matching: Right now families mostly use enrollment systems for ranking: they know the schools they want and they use enrollment systems to express this desire.

What is not really happening (as far as I can tell) is sophisticated algorithms actually helping families match with schools.

For example, on Camden’s enrollment site (where you can thankfully filter by academic performance!), I found three schools that all met the “on track” performance criteria, and pulled up the comparison page:

Screen Shot 2017-03-17 at 10.11.14 AM

This does very little to help me choose between these schools. My preference variables are limited to very broad categories such as “art classes” and “after school programs.”

After playing around on these websites, I get very little indication that that the platform knows me or the schools very well. Compare this to Netflix, Amazon, or dating websites (previous life) – platforms where I feel like the algorithms know me better than myself.

Unified enrollment systems need to more deeply understand children and schools in order to make better matches.

Prediction: Right now, government accountability systems are a basket case of poor design (generally don’t weight growth enough), brutal politics (what politician wants to tell a bunch of communities they only have “F” schools?), and awful transparency (good luck trying to navigate your average state department of education website).

Most importantly, government accountability systems evaluate schools rather than make predictions.

As a parent, it’s one thing to tell you that a school is a “C+” – it’s another thing to give you a prediction of what will happen to your child if she attends the school.

With current date, we could probably gather basic information on your child’s age, gender, current academic performance, personality type, etc.,  and make a reasonably accurate prediction that if she attends school X she will have a Y% chance of graduating from high schools and a Z% chance of earning a post-secondary degree.

Good enrollment systems, over time, should become better and better predictive agents, and, perhaps, can end up augmenting (displacing?) government accountability systems.

IV. Root Causes and Potential Solutions 

I don’t yet have strong beliefs about the root causes of why these enrollment products aren’t getting better faster. But here’s some guesses:

Non-profits > government operated: Most of the enrollment systems are run by governments, which are not good at running tech products and have bad incentives around giving parents accurate information about schools. Non-profits would likely be better operationally and have better incentives, and avoid the privacy concerns associated with for-profits.

Lack of scale: Matching and predication can better with bigger data sets, and if all these systems are structured as isolated city based data silos, the algorithms will be dumber than they should be.

Weak Customer Demand -> Bad Economics: SchoolMint, from what I understand, is the most successful player in the market. For reasons I don’t underhand, this company has not developed a better product. Perhaps it’s because their government customers don’t actually want it. Or perhaps the economics don’t work (which might suggest philanthropy is needed).

If the above is true, a national non-profit should be backed to scale to enough size to create smart algorithms, and it should be financially structured in a manner that gets it out of the perverse incentives of being beholden to government or individual schools rather than families.

A philanthropic foundation with a great tech backbone could be well situated to support this endeavor.

V. Expectations

Better matching and prediction would probably not make the average student’s education experience 10x better, just as dating websites don’t inevitably lead to great marriages.

But I do think better matching and predication could increase the probabilities that millions of families could find a better fit for their children.

At scale, that’s a better world.

5 thoughts on “What if unified enrollment platforms were 10x better?

  1. Mike

    Good thoughtful post.

    Amazon, Netflix is 2 things, though, right?

    1. You buy lots of stuff. You make many choices. You “behave.” They keep track.

    2. Then, and only then, does Amazon algorithm help. Gets better each time you choose.

    But any school matching algorithm struggles with #1, no? Parent typically buys elementary school only “once.” You leave only if you move or dissatisfied. Same with high school, college.

    So Algorithm needs to substitute a questionnaire for actual behavior. How much does that degrade finding “better fit”?

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    1. nkingsl Post author

      Thanks. Zillow perhaps a better example where you use wide data sets to make predictions on one time behavior of an individual. People aren’t that different! My hunch (haven’t talked to Reed) but is that Netflix customers probably fall into smallish number of buckets (10-15?)…. so you just need to figure out what these buckets are and what bucket a family falls into – questionnaire one way, but perhaps other ways to use public data to get better…

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  2. Steve Glazerman

    Perhaps you are thinking about this wrong, putting too much on the algorithm. The websites really shouldn’t be a way to choose schools. They should be a way to narrow down the list of schools that you visit and gather other information about, like meeting the staff, observing the learning environment, and talking to parents of current students.

    That being said, you and readers of this blog might be interested in an experimental study (randomized trial) that we’re finishing up now to understand how the way that information is presented to parents online affects their choices as well as their understanding of the information and their user experience. https://www.socialscienceregistry.org/trials/1190

    Some folks in NYC (Sarah Cohodes, Sean Corcoran, Carolyn Sattin-Bajaj) are also doing research on this topic too. I don’t have the link handy but they have a project website.

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    1. nkingsl Post author

      Thanks, Steve. Given that no parent is going to visit a 100 schools, an algorithm could do a lot of work to narrow it down; akin to narrowing down your pool of “first dates.” Will read study! Eager for more great research to come out of UE systems.

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