Two weeks ago, Rob Stellar wrote about the low rate of college completion among Hartford Public School graduates on TrendCT. More importantly, he raised some important questions that may better explain what’s going on here:
“What is preventing so many students who graduate high school from enrolling in college? Is it for financial reasons or lack of access to other resources/college prep programs? How well do Hartford high schools prepare students for college? For the students that do complete college, what type of degree did they obtain? What sets these students apart from their peers who do not finish college?”
Rob doesn’t presume to know the answers to these questions - he suggests that more data is necessary to understand the complexities of this problem. A reasonable suggestion to help identify specific ways to help students in Hartford complete college.
A recent CT News Junkie op-ed intended to counter Rob’s CT Trends piece by claiming that we need “no more data” on the subject, pointing to examples of how proficiency rates data and graduation standards may have been compromised in Hartford.
However, the answer to incomplete or flawed data shouldn’t be no data - it needs to be better data.
A recent article in the New York Times emphasizes the need for analysts to ask “What did I miss?” when evaluating easy-to-measure data. At the end of the article, the author notes an under-reported aspect of Bill James’ Moneyball approach to baseball analytics:
“The numerical revolution he spearheaded was never about putting traditional experts out of business. It was about acknowledging our ignorance, then gathering and testing data, whatever its source.”
This is exactly how we should be trying to understand how well our students, teachers, and schools are doing and what we can do to improve them. Let’s collect more data, test it, and use it to improve what adults are doing to improve student outcomes.
Many schools are already implementing a smart use of data to improve instructions and operations, but there’s still room to improve and include more sources of data. Student surveys, growth models, and more accessible data for researchers are just a few ways we could achieve a more robust educational dataset in Connecticut.
As we work towards building more robust datasets in the field of education, we also need to make sure we’re engaging in rigorous, transparent analysis. We also need to acknowledge the limits of what data can reveal to us. As noted statistician John Tukey observed:1
“The data may not contain the answer. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.”
Let’s collect more educational data and apply it appropriately - it’s the best way to answer how we can improve the way our public schools serve students.