__Note added on 2/7/21:__

For some insight into this situation, see this later post.

__Note added on 2/5/21__:

I just received the following email (sent to me and the estimable Carol Wolf) by Michelle Hudacsko, the RPS Chief of Staff:

Hi Carol and John,

There are 4 teachers in RPS that are unlicensed. I’m getting information on why. The four teachers are at the following locations/content areas:

· Oak Grove – Kindergarten

· Miles Jones -2^{nd} grade

· Woodville – 4^{th} grade

· Armstrong – Art

There are 32 teachers whose licensure application is with the State being processed. There are 6 teachers hired in December/January and are getting paperwork in to us to submit to the State.

We have about 2,100 teachers. So if you count just the 4 unlicensed we are about 0% unlicensed (as I expected and as it should be!). If you count the 38 who are just in a licensure processing queue (which is a bit delayed due to COVID), while I would argue they aren’t unlicensed from a qualifications perspective, just a paper perspective, we’d be at 2%.

To the extent these data from the RPS “Talent” office are complete and accurate, this bespeaks a big win for our Superintendent. More to the point, to the extent teacher qualifications are relevant to students’ learning, this indicates a big win for Richmond’s schoolchildren.

As well, if these data had been available on the RPS Web site, the post below would have been much more a celebration than a lamentation.

**End of note.**

There is room to argue whether the licensing system for public school teachers measures teaching quality. The National Comprehensive Center for Teacher Quality says “no”:

The No Child Left Behind (NCLB) Act mandates that all teachers should be highly qualified, and by the federal definition, most teachers now meet this requirement. However, it is increasingly clear that “highly qualified” – having the necessary qualifications and certifications – does not necessarily predict “highly effective” teaching – teaching that improves student learning.

In any case, the system is in effect and the 2018 Civil Rights Data Collection has the counts of full-time teachers who do, or do not, meet all state licensing/certification requirements.

To start, here are the percentages of unlicensed teachers in the Virginia public school divisions.

Richmond is the gold bar at 22.5%. The red bars, from the left, are the peer cities Newport News (barely visible at 0.1%), Norfolk, and Hampton. The blue bar is Galax which, at 3.7%, is closest to the state average of 3.6%.

Richmond is 6.3 times the division average.

Turning to the Richmond schools:

The yellow bars are the Richmond high schools (with Community invisible at 0% and counting Franklin as a high school); the pink are the middle schools; and the green, the elementary. The white bars are specialty schools (five of which are at 0%). Gold is the Richmond average; blue is the state division average.

Here is the list.

Turning to the relationship between the SOL reading pass rate and the percentage of unlicensed teachers:

The fitted curve suggests a seven point decrease in the pass rate per 10% increase in the unlicensed teachers, with just over a fifth of the variance in the pass rates explained by the unlicensed percentage.

The math data show a stronger relationship: 8.6% decrease per 10% and R-squared = 27%.

Of course, these data do not imply causation so they cannot tell us whether larger numbers of unlicensed teachers tend to reduce the pass rates or whether schools with lower pass rates tend to hire larger numbers of unlicensed teachers. But I’ll bet you a #2 lead pencil it’s the latter.

Indeed, it would be an interesting, and perhaps useful, experiment to fully staff up, say, Boushall (48% unlicensed, reading pass rate = 47%) or MLK (39%, 32%) with licensed teachers and see what happens.

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Puzzle question: Can you explain why the Richmond percentage of unlicensed teachers is more strongly associated with the decrease of pass rates of students who are __not__ economically disadvantaged (“Not ED”) than with the rates of those who are (“ED”)?

Your hypothesis needs to accommodate the division-level data that reverse that order, mostly by the weaker relationship with the Not ED pass rates.

Richmond is the enlarged, yellow points. The red are, from the left, Newport News, Norfolk, and Hampton.