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·      Make sure you check arithmetic and typography of any study

·      The example used was an article written by Education Week that claimed students scoring at least 650 on the verbal and or math section of the S.A.T. between 1982 and 1992 had fallen.

·      The article sought to discredit education reform over the ten-year period (Neurotic Need to Belief the Worst)

·      When more closely studied, using the profiles of “College Bound Students” by the College Board showed the numbers to be significantly different.  They showed that student who scored over 650 increased in both the verbal and math sections of the S.A.T.

·      The original data omitted students who actually scored over 700, and only took into account those students who scored between 650-690.

·      In conclusion, numbers showing are easier to accept according to the article, and numbers showing education in a positive light are checked two to three times.

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The author coined the term capta to better reflect what we truly do with data. Data means given, but the conclusions drawn from data are really takeaways. The Latin sentiment for that is captiva—taken.  Capta, mirroring data phonologically, is the author's way to suggest that data is anything but a logical given.

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For some reason, data has a negative connotation for many educators.  Brady points out that many principals are hesitant to discuss data, fearing that the data will make them look bad instead of using data to help illuminate problems that need to be solved.

Brady specifically references using data to raise graduation rates. This is an important question right now because of a perceived raise in dropouts and a lower number of sustainable jobs for unskilled workers.  In 2005, Bill Gates gave 15 million dollars to study graduation rates.  In order to really understand how to raise graduation rates requires  a lot of data.  What is the graduation rate?  Who are dropouts?  Why are they dropping out?

At the time this article was published, NCLB was the program that was creating and using the most data. Most of this data dealt with standardized test scores.

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This section has a lot of conflicting information.
- Today we focus on Growth Models and Value Added Models to look at outcomes. The Cohort Method focuses on AYP . Ex) 3rd grade student growth from year to year. This is unrealistic as you are not measuring the true growth of the individual student.
- Finding ways to track students growth within state (if student moves)
* TVASS is the focus of this section:  It is a computer generated system that CAN tell which teachers are effective vs. ineffective.  (Not)
Belief: Teachers are the most important variable in determining the level of achievement. / Student who has effective teacher 3 years in a row WILL show huge growth. Students who have an ineffective teacher 3 years in a row WILL be stunned ruined for life.
FLAWED!!! Unrealistic Assumption
There are so many other factors: family, income, community, genes....
ALSO- the definition of an "effective" teacher is one who shows increases in test scores.
New Test:  Northwest Evaluation Association is coming soon. This will have no attempt to link test score gains to individual teachers.

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- All science is theory
- Some theories are well supported by data and some are not
- Point is to find evidence to support a theory not to prove it
-Science deals with what happens and how not the why
- When wanting to evaluate a program, you need to begin gathering data before the program actually begins
- Need to have a good program evaluation in place and make sure your outcome variables work for that program.

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Pg. 5 Reading Data --Pop Quiz (3-5)    Matt

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Pg. 5 Reading Data --Pop Quiz (3-5)    Matt

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Reading Data Pop Quiz: 3-5

3. “‘On virtually every measure, schools are performing more poorly today than forty years ago.’ (Martin Gross, same speech). Any comments?”

-”Virtually every measure” is not specific.

-Which measurements are used?

-NAEP wasn’t around for the time period mentioned

-SAT was, but few people took it.

-The Iowa Test of Basic Skills (gr. 3-8), ITBS,  or Iowa Tests of Educational

Development (gr. 9-12), ITED, were around and are based on original test so they can be used for comparison. However, these tests show that scores were lower in 1959 than in 1999.

-Since this is the only available, reliable measure available, Gross’ claims

are invalid.

4. “Among the 29 industrialized countries, the United States scored below 20 nations and above 5 in math. The U.S. performance was about the same as those of Poland, Hungary, and Spain…’If we want to be competitive, we have some mountains to climb,’ Deputy Secretary of Education Eugene Hickok said at a news conference Monday” (Associated Press wire story, December 7, 2004). Any comments?

-Ranks show place, but not performance.

- a runner can be really fast, but still finish last.

- nations that had close scores, still finished far apart from one another.

-Common logic is that we can’t compete in the world economy if students don’t get better at math. However, the U.S. is ranked the most economically competitive nation in the world.

-No clear connection between economic health and  “thirteen-year-olds’ skills at bubbling

in answer sheets.”

5. “‘When I compare our high schools to what I see when I am traveling, I am terrified for our workforce of tomorrow” (Bill Gates, 2005). Any critical remarks?

-What are the chances that when Bill Gates goes to a foreign country, they show him the bad schools?

-Japanese schools do not allow foreign researchers into bad schools.

-Schools are ranked 1-27, with 1 being the best. Foreign researchers might get

into level 12 or so. Not even Japanese researchers are allowed in levels 26 and 27.

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P. 22 Deliberate Distortions- Georgina Mayorga

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P. 22 Deliberate Distortions- Georgina Mayorga

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Bracey shares a claim from the Washington Post from former Secretary of Education, William Bennett, "Half of all high school graduates have not mastered 7th grade math." He presents that while this is interesting this statement lacks two very important questions, 1) What is mastery? 2) Show me the data! This statements lacks a lot of specificity

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For quite some time, educational consultant Denis Doyle tried to prove that public school teachers were hypocrites, teaching in public schools but sending their own children to private schools.  He called teachers both "connoisseurs" (positive) and "Benedict Arnolds." (obviously negative) The research that was done disproved his original thoughts.  In 2000, data showed the general public and public school teachers sending students to private schools decreased.  The general public tends to make, on average, more than $20,000 less than a public school teacher.  A higher percentage of the general public actually sends their kids to private schools than teachers do.  So, BOOM/take that, Doyle.

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Program for International Student Assessment in 2000 and 2003, assessed 15 year old boys in over 30 counties with the same test (as similar as can be given language translation and cultural context differences.

Usually, Asian nations are among the top, but in fact, Finland ranked top in the world in reading and math, and tied for top with Japan in science.

Looking for what reasonable candidates, Fins begin teaching at age 7, well developed comprehensive early childhood. Genuine commitment to no child left behind. Higher engagement in reading, and the check out more library books. Math curriculum emphasizes real-life problems. Hands-on science.

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Bracey shares the belief that hypothesis testing is the principal activity in educational research  The article says:
-Hypotheses should be specific; unambiguous.
-Tools for testing hypotheses should be appropriate measures the predicted outcome.

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Bracey talks about the difference between data-"driven" decision making and data-"assisted" decision making in this section. He insists that all too often, teachers are focused on doing whatever they can to get their test scores up, sometimes with unsupported interventions.  The difference between the two is utilizing the data to solve problems and evaluate results.
One way it can be used is to test individual perceptions of issues within a school. If an issue is determined, data can be used to support or reject the claim. This provides proof, which could diffuse any false perceptions of happenings within the school.
The reading, finally, provided numerous intrinsic and extrinsic motivations for using the data for decision making.

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Principle of Data Interpretation

"For every complex problem there is an answer that is clear, simple and wrong."
This section of the reading discussed the decade of declining standardized test scores (1964-74). It brought to light to all of the social turmoil/change in the US, included the Vietnam War, assassinations (JFK & MLK), social movements, drugs, political instability (Watergate), and how these issues affected the performance of students during that time. In short, the author makes the point that it would've been a miracle if test scores hadn't fallen given the context in history, and that complex problems require solutions that address all of the causes. Design Thinking is in order here.

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In this portion of the article, Bracey is saying that you really can't compare an individual state's scores to national scores. He talks about how Iowa's scores are only looking at Iowa, where national scores are looking at the entire nation. Iowa is going to be higher because it is looking at a smaller population. Iowa has had a set test since the 1920's, however they did such a massive overhaul of their test in 1955 that they actually consider 1955 to be year zero for their test.

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*If a limited range of data is presented when it could be expanded to other data, find out what the other data is and its results.
*The proportion of students at the given level of achievement is not a score.
*Test scores can't determine the students level of achievement. They are not comparable.
*Selecting the "right" data is akin to selecting the "right" words in a movie review

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pg. 10 Exploring Group Differences (The Education Trust's study...) 

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pg. 10 Exploring Group Differences (The Education Trust's study...) 

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This study looked at high-poverty/high minority schools.

"Dispelling the Myth" (Poor kids can't learn)

Looked at one grade level-one subject -one year.
The data was put in online in an interactive database that anyone could manipulate.

Looked at schools in California and how these schools that were high flyers for one year or less with a high school poverty rate.

Result: As poverty increases, achievement decreases.

So -NCLB is supposed to address this but it has a lack of resources, knowledge, and motivation. We need to focus on improving the performance of these students.

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Principle of Data Interpretation-p.31 Tracy

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Principle of Data Interpretation-p.31 Tracy

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When comparing groups (with data), make sure the groups are comparable.
Example: Percentage of public school teachers who send their own children to public schools vs. sending them to private schools.

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* Readers should critically consider statistics.
* Don't passively assume that everything stated is what it seems. (For example, statistic on American children gunned down.)
* Misinterpretations can stem from a variety of sources:
1) Lack of specificity
2) Poorly defined info
3) Consider motivations and biases of authors
* Bracey advises to be on guard and watch for distortions and misrepresentations
* Also advises to check the arithmetic for yourself - if it doesn't make sense when you read it, reconsider the math (For example, Education Week omission)

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Basically says that you make statistics say whatever you want them to say. " The average SAT score for students is 1014, 964 for those going to major in education."
So this means the average student is 50 points smarter than their teacher.
Only 43% of students take SAT, not all students who start in EDU finish there and visa versa many teachers come to EDU from other fields. Further investigation finds that only elementary teachers have lower scores than everyone else, but does that really matter? Elementary teachers don't need extremely high levels of content knowledge, they need to be better versed in other skills.

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Only using some of the data.
Choosing data that only reveals part of the picture.
Using the data that supports the author's agenda.
Example: Commissioners reviewed NAEP science achievement test scores taken by 17 year-old students. They only used items that the 1973 and 1969 tests had in common. They excluded data from the 1977 test, excluded data from 9 and 13 year-old students, and excluded math and reading test data.

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In this section of the text, the author illustrated the difference between data and other forms of observations. He explicitly states that examples and stories are often not representative and cannot be generalized to specific data sets. He takes a decided point of view that examples and observations are compelling on a personal level, but often untrue or unrepresentative of actual hard data. There is a strong implication that he does not beleive that personal experience, examples, and non-systematic observations, have any place in presenting meaningful data.

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- "Average achievement of high school students on most standardized tests is now lower than 26 yrs. ago when Sputnik was launched."
--- inaccurate, only one showed data (ITED)
--- lack of specificity is a common flaw