Sentences with phrase «level test score data»

Plank said that a group of districts including Los Angeles and San Francisco with their own tracking systems have shared student - level test score data with researchers.

Not exact matches

Using longitudinally linked, student - level data collected from two urban school districts, New York City and Washington, DC, Mathematica estimated the impacts of five EL middle schools on students» reading and math test scores.

Using student - level data from two states, Harvard Professor Martin West and I found that 40 to 60 percent of schools serving mostly low - income or underrepresented minority students would fall into the bottom 15 percent of schools statewide based on their average test scores, but only 15 to 25 percent of these same schools would be classified as low performing based on their test - score growth.

The second set of data includes school - level information on test scores for certain grades and subjects, collected since the early 1990s as part of Illinois» ongoing accountability program.

Among the districts in the data, one standard deviation in percent free / reduced lunch is 21 percentage points and one standard deviation in average test scores is 0.35 student - level standard deviations.

The data needed to best answer these questions are the student - level test - score and demographic information collected by the New York State Department of Education.

One must have data on school type (charter or public) and test scores of individual students prior to high school, individual - level high school attendance records and exit information, and college attendance after high school.

Using 2015 test - score data and comparing schools with similar percentages of low - income kids, charters outperform DPS - operated schools at the middle and high school level but not at the elementary level, where there are only 10 charters.

The test - score data were adjusted statistically to account for any observed differences between the two groups, such as level of family income - an important predictor of academic performance - that might have biased the results.

As we struggle with how to improve student outcomes, we need to triangulate Level 1 «satellite» data — test scores, D / F rates, attendance rates — with Level 2 «map» data — reading inventories, teacher - created common assessments, student surveys — and Level 3 «street» data, which can only be gathered through listening and close observation.

For purposes of this analysis, we constructed a data set that contained pupil - level test scores for about 220,000 students.

To rule out this possibility, we rely on school - level data on the percentage of students achieving level 4 in Key Stage 2 English, as the more detailed student - level test scores examined above are not available before 1996.

The analysis extends previous work (see «Johnny Can Read... in Some States,» features, Summer 2005, and «Keeping an Eye on State Standards,» features, Summer 2006) that used 2003 and 2005 test - score data and finds in the new data a noticeable decline, especially at the 8th - grade level.

At the individual school level, with a few exceptions such as the large HCZ, there are less data on school test score effects and attainment effects.

Our analysis is based on statewide, student - level longitudinal data obtained from the Arizona Department of Education (AZDOE) that contains information on test scores, school enrollment, and student characteristics for the 2005 - 06 through 2011 - 12 school years.

This study presents evidence on whether NCLB has influenced student achievement based on an analysis of state - level panel data on student test scores from the National Assessment of Educational Progress (NAEP).

District - level data from New York suggest that relatively affluent districts tend to have higher opt - out rates, and that districts with lower test scores have higher opt - out rates after taking socioeconomic status into account

School - level data on student proficiency were drawn from SchoolDataDirect.org for the 2007 — 08 school year, the most recent year for which test - score data would have been publicly available when the survey was conducted.

In a few districts, district and school leaders reported that analysis of trend data by district and / or state assessment specialists had led to the identification of early indicators of students academically at risk, based on test scores or other factors (e.g., family circumstances), in lower grade levels.

While the State has released average scores on the test by some student groups (though not all), disaggregated data about whether or not students are reaching grade - level expectations has not been released.

Many school systems have gotten the message that they need to be more data driven, and they are now awash in data - not just yearly student test scores, but figures on how different groups of students are doing in particular subjects or grade levels, how successful a school is at attracting and retaining teachers or closing the achievement gap among disadvantaged students, or how equitable funding is from school to school.

Other limitations included small data sets (Kruger, 2005) and the inability to disaggregate test scores that had been compiled at the school level by individual teacher or students (Isenberg et al., 2009).

Those figures came from the New York City Department of Education, which did its own analysis of state testing data using 2010 proficiency levels for 2006 test scores.

Using data sources from the state assessment, college - level entrance testing, SAT reading and writing scores, and course exams, your high school staff identifies a writing goal.

If there are enough years of test - score data, «including individual - level race and income... in the model doesn't matter very much,» he said.

The Brown Center report used state - level data from the NAEP to describe a positive association between tracking in eighth grade and larger percentages of high - scoring AP test takers.

But many researchers argue that value - added models don't need to control for demographic factors like poverty, race, English - learner or special - education status at the individual student level, as long as enough test score data (at least three years) are included in the formula.

Critics point to a report released last week showing how school districts in San Mateo and Santa Clara counties ignore objective data like test scores and grades, and they often place black and Latino ninth - graders in math classes below their level.

The year we changed our focus from textbooks, programs, supplies, schedules, buildings, grades, test scores, etc. to focus on the heart of the matter at the root level (culture, identity, will, beliefs, thoughts, emotions, empathy, relationships, etc.), we transformed our performance data.

Henderson and others point to data in the test results to argue that the answer is yes: generally higher test scores at the lower grade levels than in high school.

Grade 4 test data, absentee students scored an average 12 points lower on the reading assessment than those with no absences — more than a full grade level on the NAEP achievement scale.

California hasn't done away with data altogether — school level test scores are publicly reported and several large districts together known as CORE have worked to create more robust data systems — but several researchers and advocates say they can't fully judge the education policies of the most populous state in the country because of a lack of accessible data.

Still, many say the database, which tracks a variety of student - level data including demographics and test scores, could be better.

He writes: «In this dystopian story, teachers are evaluated by standardized test scores and branded with color - coded levels of effectiveness, students are abstracted into inhuman measures of data, and educational value is assessed by how well forecasted «growth» levels are met.

We shifted towards standardized forms of inquiry, looking at student achievement data for the handful of Mam - speaking students who had been at Bridges for more than a few years; looking at achievement data for the 15 Mam - speaking kindergartners; looking at the entry level of Spanish proficiency (as measured by the IPT Language Proficiency Test) for native Spanish speaking students in our bilingual program and comparing the IPT scores of Mam - speaking children.

The fact of the matter is is that all states have essentially the same school level data (i.e., very similar test scores by students over time, links to teachers, and series of typically dichotomous / binary variables meant to capture things like special education status, English language status, free - and - reduced lunch eligibility, etc.).

Using modest criteria that were overly dependent on TAAS scores — enrollment of 5,000 or more students; high poverty levels; and 50 percent of the high - poverty schools in the district categorized as Recognized or Exemplary on the basis of their state test scores — they studied data from all Texas districts.

A rich, complex picture of a school emerges from the intersection of all four categories of data, such as a comparison of state test scores — disaggregated by program, gender, and grade level — with questionnaire results for students — also disaggregated by program, gender, and grade level.

Student - level data can be test scores and survey data about any number of topics (e.g., learning strategies, student mindsets, etc.).

they compared the levels of depressive symptoms or the frequency of depression diagnoses between children and adolescents with chronic physical illness and their healthy peers or test norms, or they provided sufficient information for a comparison with established normative data (e.g., by reporting standardized T - scores),

She analyzed data on four variables for the children: reading and math test scores; a measure of behavioral problems; and a measure of home environment, which looked at levels of cognitive stimulation and emotional support.