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December 8, 2016

What is career and technical education? New CPE paper answers this question and more

10901-4727 CTE Pathways CPE SliderWhat exactly does career technical education encompass?

It’s one of the many basic questions that bubbled up after we ended our Path Least Taken series, the original analysis of Class of 2004 high school graduates which, to refresh your memory, examined the outcomes of non-college goers (who were fewer than we anticipated) against college-goers.

Among the key findings of that study was the outsized impact career and technical education programs had on the relative success of all students, especially the non-college goer.

Once we made that discovery, it seemed only natural to look at the ins-and-outs of CTE programs … which we present to you in this handy FAQ. It attempts to answer all your burning questions about career and technical education, including what it looks like in school communities and what successful programs have in common.

Filed under: 21st century education,Career Readiness — Tags: , — NDillon @ 11:08 am





December 7, 2016

PISA scores remain stagnant for U.S. students

The results of the latest PISA or the Program for International Student Assessment are in and as usual, we have an interpretation of the highlights for you.

If you recall, PISA is designed to assess not just students’ academic knowledge but their application of that knowledge and is administered to 15-year-olds across the globe every three years by the U.S. Department of Education’s National Center for Education Statistics (NCES) in coordination with the Paris-based Organisation for Economic Cooperation and Development (OECD). Each iteration of the PISA has a different focus and the 2015 version honed in on science, though it also tested math and reading proficiency among the roughly half-million teens who participated in this round. So, how did American students stack up?

In short, our performance was average in reading and science and below average in math, compared to the 35 other OECD member countries.  Specifically, the U.S. ranked 19th in science, 20th in reading and 31st in math. But PISA was administered in countries beyond OECD members and among that total group of 70 countries and education systems (some regions of China are assessed as separate systems), U.S. teens ranked 25th in science, 22nd in reading, and 40th in math.  Since 2012, scores were basically the same in science and reading, but dropped 11 points in math.

PISA Science

Before you get too upset over our less-than-stellar performance, though, there are a few things to take into account.  First, scores overall have fluctuated in all three subjects.  Some of the top performers such as South Korea and Finland have seen 20-30 point drops in math test scores from 2003 to 2015 at the same time that the U.S. saw a 13 point drop.  Are half of the countries really declining in performance, or could it be a change in the test, or a change in how the test corresponds with what and how material is taught in schools?

Second, the U.S. has seen a large set of reforms over the last several years, which have disrupted the education system.  Like many systems, a disruption may cause a temporary drop in performance, but eventually stabilize.  Many teachers are still adjusting to teaching the Common Core Standards and/or Next Generation Science Standards; the 2008 recession caused shocks in funding levels that we’re still recovering from; many school systems received waivers from No Child Left Behind which substantially change state- and school-level policies.  And, in case you want to blame Common Core for lower math scores, keep in mind that not all test-takers live in states that have adopted the Common Core, and even if they do, some have only learned under the new standards for a year or two.  Andreas Schleicher, who oversees the PISA test for the OECD, predicts that the Common Core Standards will eventually yield positive results for the U.S., but that we must be patient.

Demographics

Student scores are correlated to some degree with student poverty and the concentration of poverty in some schools.  Students from disadvantaged backgrounds are 2.5 times more likely to perform poorly than advantaged students.  Schools with fewer than 25 percent of students who are eligible for free or reduced price lunch (about half of all students nationwide are eligible) would be 2nd in science, 1st in reading, and 11th in math out of all 70 countries.  At the other end of the spectrum, schools with at least 75 percent of students who are eligible for free or reduced price lunch, 44th in science, 42nd in reading, and 47th in math.  Compared only to OECD countries, high-poverty schools would only beat four countries in science, four countries in reading, and five in math.

Score differences for different races in the U.S. show similar disparities.

How individual student groups would rank compared to the 70 education systems tested:

Science Reading Math
White 5th 4th 20th
Black 49th 44th 51st
Hispanic 40th 37th 44th
Asian 8th 2nd 20th
Mixed Race 19th 20th 38th

 

Equity

Despite the disparities in opportunity for low-income students, the number of low-income students who performed better than expected increased by 12 percentage points since 2006, to 32 percent.  The amount of variation attributable to poverty decreased from 17 percent in 2006 to 11 percent in 2015, meaning that poverty became less of a determining factor in how a student performed.

Funding

America is one of the largest spenders on education, as we should be, given our high per capita income.  Many have bemoaned that we should be outscoring other nations based on our higher spending levels, but the reality is that high levels of childhood poverty and inequitable spending often counteract the amount of money put into the system.  For more info on this, see our previous blogpost.






Internship Opportunity

The Center for Public Education (CPE) at the National School Boards Association (NSBA) seeks policy research interns for the spring and summer 2017 semesters to work closely with CPE’s research analyst in conducting education policy research.

CPE is a national resource for accurate, timely, and credible information about public education and its importance to the well-being of our nation. CPE provides up-to-date research, data, and analysis on current education issues and explores ways to improve student achievement and engage public support for public schools.  The Center serves as America’s one-stop shop for clear, concise, and trusted information about public education, leading to more understanding about our schools, more community-wide involvement, and better decision-making by school leaders on behalf of all students in their classrooms.  Approximately 80,000 people visit the CPE website monthly (centerforpubliceducation.org), providing the opportunity for wide visibility.

Primary duties include:

  • Contribute to a major CPE research report to be published on CPE website. Your contribution will be acknowledged on the report, and may also include an article under your name for NSBA’s flagship publication, “American School Board Journal.” Current projects focus on early elementary education and school principals.
  • Summarize findings of significant education reports on CPE’s blog
  • Analyze statistical data from the National Center for Education Statistics
  • Update CPE’s previous data reports
  • Attend briefings/conferences in the Washington, DC area

Job qualifications: A graduate student studying education policy, public policy, statistics, economics, early childhood education, or a related field. The student should also have a strong interest in education policy and research.

Internship dates and hours are flexible, but should align with the academic calendar for the semester.  The internship offers a $1,500 stipend, to be paid at the mid-point and termination of the internship.  CPE will also work with your school to satisfy any requirements for you to receive course credit.

Send a cover letter, resume, and writing sample by January 15 (for the spring internship) or April 1 (for the summer internship) to: Chandi Wagner at cwagner@nsba.org with the subject line “Policy Research Intern.” Please contact Chandi Wagner at cwagner@nsba.org with any questions about the internship.  NSBA is an Equal Opportunity Employer.

Filed under: CPE — Chandi Wagner @ 10:48 am





November 17, 2016

What does “evidence-based” mean?

The Every Student Succeeds Act requires schools to use “evidence-based interventions” to improve schools.  The law also includes definitions of what evidence means, and recent guidance from the Department of Education has provided additional clarification on what passes as “evidence-based.”  Mathematica has also put out a brief guide on different types of data that have similar categories as the Department of Education, but also provide explanations for data we may see in the media or from academic researchers that do not qualify as hard data but can still help us understand policies and programs.

ESSA Evidence

What follows is a brief summary of what qualifies as “evidence-based” starting with the strongest first:

Experimental Studies:  These are purposefully created experiments, similar to medical trials, that randomly assign students to treatment or control groups, and then determine the difference in achievement after the treatment period.  Researchers also check to make sure that the two groups are similar in demographics.  This is considered to be causal evidence because there is little reason to believe the two similar groups would have had different outcomes except for the effect of the treatment.  Studies must involve at least 350 students, or 14 classrooms (assuming 25 students per class) and include multiple sites.

Quasi-experimental Studies:  These still have some form of comparison group, which may be between students, schools, or districts that have similar demographic characteristics.  However, even groups that seem similar on paper may still have systematic differences, which makes evidence from quasi-experimental studies slightly less reliable than randomized studies.  Evidence from these studies are often (but not always) considered to be causal, though experiment design and fidelity can greatly affect how reliable these conclusions are across other student groups.  Studies must involve at least 350 students, or 14 classrooms (assuming 25 students per class) and include multiple sites.

Correlational Studies: Studies that result in correlational effects can’t necessarily prove that a specific intervention caused students in a particular program to have a positive/negative effect.  For example, if Middle School X requires all teachers to participate in Professional Learning Communities (PLCs), and they end up with greater student improvement than Middle School Y, we can say that their improved performance was correlated with PLC participation.  However, there could have also been other changes at the school that truly caused the improvement, such as greater parental participation, so we cannot say that the improvement was caused by PLCs, but that further study should be done to see if there is a causal relationship.  Researchers still have to control for demographic factors; in this example, Middle School X and Middle School Y would have to be similar in both their teacher and student groups.

With all studies, we also have to consider who was involved and how the program was implemented.  A good example of this is the class-size experiment performed in Tennessee in the 1980s.  While their randomized control trial found positive effects of reducing class size by an average of seven students per class, when California reduced class sizes in the 1990s they didn’t see as strong of effects.  Part of this was implementation – reducing class sizes means hiring more teachers, and many inexperienced, uncertified teachers had to be placed in classrooms to fill the gap, which could have reduced the positive effect of smaller classes.  Also, students in California may be different than students in Tennessee; while this seems less likely for something like class size, it could be true for more specific programs or interventions.

An additional consideration when looking at evidence is not only statistical significance (whether or not we can be certain that the effect of a program wasn’t actually zero, using probability), but the effect size.  If an intervention has an effect size of 0.01 standard deviations* (or other units), it may only translate to the average student score changing a fraction of a percentage point.  We also have to consider if that effect is really meaningful, and if it’s worth our time, money, and effort to implement, or if we should look for a different intervention with greater effects.  Some researchers would say that an effect size of 0.2 standard deviations is the gold standard for really making meaningful changes for students.  However, I would also argue that it depends on the cost, both of time and money, of the program.  If making a small schedule tweak could garner 0.05 standard deviations of positive effect, and cost virtually nothing, then we should do it.  In conjunction with other effective programs, we can truly move the needle for student achievement.

School administrators should also consider the variation in test scores.  While most experimental studies report on the mean effect size, it is also important to consider how high- and low-performing students fared in the study.

Evidence is important and should guide policy decisions.  However, we have to keep in mind its limitations and be cautious consumers of data to make sure that we’re truly understanding how the study was done to see if its results are valid and can translate to other contexts.

 

*Standard deviations are standardized units used to help us compare programs, considering that most states and school districts use different tests.  The assumption is that most student achievement scores follow a bell curve, with the average score being at the top of the curve.  In a standard bell curve, a change of one standard deviation for a student at the 50th percentile would bump him/her up to 85th percentile, or down to the 15th percentile, depending on the direction of the change.  A report of the effect size of a program typically indicates how much the mean of the students who participated in the program changed from the previous mean or changed from the group of students who didn’t receive the program.

Filed under: CPE,Data,ESSA — Tags: , — Chandi Wagner @ 3:39 pm





November 2, 2016

Thoughts on nuance and variance

As we approach the 2016 general election, I’ve heard public officials, family, and friends make very clear statements regarding which side of the aisle they support.  Yet, I find it hard to believe that the average American falls in line 100% with either political party, or supports every word and tenet of a particular public policy.  We are nuanced people.  Very few issues are as black-and-white as we’d like them to be.  Here’s a guide for things to consider when considering your stance on a particular issue, candidate, or political party, put in the context of educational issues.

  1. Most issues have an “it depends” clause.

With the onslaught of information available today, it makes sense that we want answers that are black-and-white.  The reality, though, is that there’s gray area for most policies and practices.  We also have to balance our ideological values with evidence.  Charter school proponents may believe in free-market values and choice to improve public schools through vouchers and charter schools, but I haven’t seen widespread evidence that choice in and of itself actually improves academic achievement or long-term outcomes in significant ways.  Yes, there are individual students who have benefited, but there are also individual students who have lost out.  Charter school opponents claim that taking away publicly-elected oversight through school boards is detrimental to the public’s ability to provide free and quality education to all.  Yet, the reality is that some public schools have dismal records, and charter or private schools have sometimes had success with the same students.  We have to acknowledge that we all want good things for our kids, and then use the evidence to figure out what that looks like without demonizing the other side.

  1. Most policies rely heavily on the quality of their implementation to be successful.

Common Core seems to be a prime example of this.  Two-thirds of Americans are in support of some sort of common standards across the country.  Yet, barely half of Americans are in support of Common Core.  Support for both questions have dwindled significantly from about 90% of public support in 2012.  Even presidential candidate Hillary Clinton has called the roll-out of Common Core “disastrous,” despite supporting them overall.

CommonCore

Source: http://educationnext.org/ten-year-trends-in-public-opinion-from-ednext-poll-2016-survey/

They were implemented quickly in many states, often without the curriculum materials or professional development to help teachers succeed in teaching the new standards.  While support for Common Core seems to be leveling off with teachers, who are most familiar with them, several states have repealed or are considering repealing the Common Core.  The new state standards that have been written in South Carolina and Indiana are extremely similar to the Common Core, which means that it may not be the concept or content that people disagree with so much as how they were implemented and the ensuing political backlash.

 

  1. Statistics usually tell us about an average (the typical student) but variance is also important.

Charter schools are a prime example of this.  On average, they have similar student achievement outcomes as traditional public schools.  But, there are schools that outperform their counterparts and schools that woefully underperform.  We have to think about those schools, too.

This is also clear in school segregation.  The average black student in the U.S. attends a school that is 49% black, 28% white, 17% Latino, 4% Asian, and 3% “Other,” but that doesn’t mean that every black student has this experience.  At the edges of the spectrum, however, 13% of U.S. public schools are over 90% black and Latino, while 33% of schools are less than 10% black and Latino.  To understand the reality, we need to look at the variety of students’ experiences (known in statistic-speak as “variance”) not just the average.

  1. There’s always room for improvement. “Fixing” a policy may mean making adjustments, not abandoning it altogether.

Student assessments under No Child Left Behind (2001) resulted in the narrowing of curriculum.  But, we also learned more about disadvantaged student groups and have continued closing the achievement gap for students of color.  Should we throw out testing altogether? Some would say yes, but most Americans say no.  Graduation rates, college enrollment, and achievement scores have all increased since NCLB passed in 2001.  What we can do is improve on student assessments.  Adjusting consequences for students, teachers, and schools could result in less narrowing of curriculum and subjects taught.  Involving more well-rounded tests that encourage creative and critical thinking would help teachers emphasize these skills in class.  Continued improvement in data use can help teachers and school administrators adjust their practices and policies to see continued student growth.  States have the power to make some of these changes under the new Every Student Succeeds Act without dismantling gains made under No Child Left Behind.






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