Segregation, Teacher Quality, Test Results and Social Mobility

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From the course by University of Illinois at Urbana-Champaign

Negotiating Learner Differences: Towards Productive Diversity in Learning

ratings

University of Illinois at Urbana-Champaign

Negotiating Learner Differences: Towards Productive Diversity in Learning

ratings

An investigation of the dimensions of learner diversity: material (class, locale), corporeal (age, race, sex and sexuality, and physical and mental characteristics) and symbolic (culture, language, gender, family, affinity and persona). Examines social-cultural theories of difference, as well as considering alternative responses to these differences in educational settings - ranging from broad, institutional responses to specific pedagogical responses within classes of students. The course also focuses on the application of learning technologies and new media to meet the needs of diverse populations of learners. Its main practical question is, how do we use educational technologies to create learning environments in which learning experiences can be customized and calibrated to meet the precise needs of particular learners? Topics include: universal design for learning, differentiated instruction systems, and adaptive and personalized learning environments.

From the lesson

Diversity in US Education and Differences in Theory

In this module, we examine some specifics of learner diversity in the US context, then some general concepts in a theory of learner differences.

Segregation: A Persisting Concern4:05

Desegregation vs Intergration6:02

Patterns of Desegregation9:11

Processes of Resegregation5:33

School Segregation and Educational Opportunities8:51

Segregation, Teacher Quality, Test Results and Social Mobility6:03

Meet the Instructors

Dr William Cope
Professor
Department of Education Policy, Organization & Leadership, College of Education
Dr Mary Kalantzis
Dean
College of Education

2001, and what you

see is the green indicates the lowest poverty schools,

and the red and white also indicates.

You can see on the high-poverty schools

the far right column and the high-poverty schools.

The far left column,

the lowest poverty schools.

The schools with the green have the highest TQI, Teacher Quality Index.

The schools with the red and white stripe bars have the lowest TQI,

and the blue and the yellow and solid red in between fall between those two.

So, what you get to see is that students who attend the highest poverty schools have

the lowest likelihood of having

high-teacher quality teachers in any substantial concentration.

The school percent minority looks almost identical and distribution of teacher quality.

When we look at the race of the racial composition of the schools.

The higher the concentration of Black and Latino students in the school,

the higher school percent minority.

The lower the likelihood of having teachers of high-quality.

Having teachers of high-quality has implications for testing.

As we know, we're a test crazy in this country,

and we can show and demonstrate the effect of having both in

elementary schools and in high schools what

the implications are for having teachers in the low TQI quartile,

and what the implications are for students testing.

Students who attend schools with

low TQI are not going to score competitively with their peers.

The highest math courses and college readiness courses are disproportionately

available primarily in those schools with high TQI.

Which we've already shown to be associated with

the racial and poverty concentration in the schools,

but here's another way in which it did negatively impacts minority students.

The highest math courses taken as associated with

the school TQI in which the courses are taken.

So, you get more benefit when you're attending not just a school with a high TQI,

they also have a better range of the courses,

students in high-poverty schools only.

Students attending high-poverty schools by race and by school year,

and you get to see that Hispanic and black students disproportionately

attend high-poverty schools and that was for the year 2013-14.

We use administrative records on the incomes of more than 40 million children and

their parents to describe three features of

intergenerational mobility in the United States.

This is from work done by an economist and

published by the National Bureau on Economic Research.

This is from their working papers.

I think it's 2015.

So, looking at the administrative data, first,

we characterize the joint distribution of parent and child income at the national level.

Look at conditional expectation of child income given parent income in percentile ranks.

On average, a 10 percent increase in parental income is

associated with a 3.4 increase in a child's income.

Second intergenerational mobility vary

substantially across areas within the U.S. For example,

the probability that a child reaches

the top quantile of the national income distribution.

Starting from a family in the bottom quantile is 4.4 percent in Charlotte,

but it's 12.9 percent in San Jose.

Third, if we look at the factors associated with upward mobility, high mobility areas,

geographic areas have less residential segregation,

less income inequality, better primary schools,

greater social capital, and greater family stability.

So this descriptive analysis does not identify

the causal mechanism but determine upward mobility.

The new publicly available statistics on intergenerational mobility by

area developed here can facilitate future research on such mechanisms.

In other words, places that are highly

segregated residentially and in their schools are far more

likely to have negative and ominous consequences

for intergenerational mobility and for upward mobility more generally.

So, while the districts we contact in different areas across

the nation have efforts underway to improve the quality of education for students.

Potential discrimination and disparities across key groups of students,

but it is not routinely analyze its data in a way that

may reveal larger patterns among different types and groups of schools.

So, the agencies may miss

key patterns and trends among schools that could enhance their efforts.

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