Student Observation Paper

Pima County is “better than 42% of countries” in upward mobility for poor girls (“The Best and Worst Places to Grow Up”, 2015). Living in Pima County as a poor boy is worse than living as a poor girl (“The Best and Worst Places to Grow Up”, 2015). Living in Pima County takes away only $10 from a poor girls income while it takes $5,650 from a poor boys income (“The Best and Worst Places to Grow Up”, 2015). The article states that “Across the country, the researchers found five factors associated with strong upward mobility: less segregation by income and race, lower levels of income inequality, better schools, lower rates of violent crime, and a larger share of two-parent households” (“The Best and Worst Places to Grow Up”, 2015). In my area Santa Cruz County is the best county to grow up in for upward mobility but Pima County is the best county to grow up in for upward mobility for girls (“The Best and Worst Places to Grow Up”, 2015). The tool was used with the income of the 25th percentile and the study used the incomes of children between the 1980s and 1990s (“The Best and Worst Places to Grow Up”, 2015). I learned that “Manhattan ranks among the worst counties in the country for girls from lower-income families” (“The Best and Worst Places to Grow Up”,

Jones felt the best approach would be a cohort analysis focusing in on the current 4th graders, specifically focused on Mathematics. Mr. Jones needs to seek the input of the others in his building. There has been no mention of others involvement. Focusing only on one cohort of students does not give the other grades and subject in some ways makes the other areas feel less accountable for the success of the school as a whole. Mr. Jones did look at the released version of the state test, he made sure his books were aligned to the curriculum and he did have a test to be used as a

With this information, a region of schools can compare their results and figure out how well a region is doing in comparison to other regions. However, this is not completely valid due to the difference in assessment in each region. Therefore, they cannot be accurately compared to other regions because of this difference. (Costa and Kallick, 1995)

2. After reviewing data table 2, the populations that I chose to compare are black, Latino/Hispanic, and IEP. The black population has a mathematics proficiency of 69% and a reading proficiency of 77%, both are below target goal. The Latino/Hispanic population has a mathematics proficiency of 79% and a reading proficiency of 83%. Both are 1% and 2% above the target goal, respectively. The IEP population has a mathematics proficiency of 54% and a reading proficiency of 55%, which are very much below the target goal. I would be the most concerned with the IEP population because it has the largest number of students are below target in both mathematics and reading. There were 432 students that took the mathematics test and 47% or 199

The statistics for the district and state are similar. 3.1% of district students dropped out, comparable to the 2.1% of Texas students who dropped out. The district has 84.4% of their students graduate in time while Texas has 89% graduate. The average state SAT score is 1394 and Arlington ISD’s average is 3 points higher. The state average score for ACT on the other hand is 20.6 and while lower, the district’s average of 20.1 is still very close. Test scores and the rate of graduating students reflect the academic success of a school district because these are two things colleges and employers look

After gathering all the information from the DESE website about Warren Elementary and the Francis Howell School District, I also looked at some of the information from the state of Missouri. The data that I found on Warren Elementary for the enrollment is that the majority of students are white (85.2%), the next was African Americans (7.2%), the school didn’t count the other ethnicities (Asian, Hispanic and Indian) in the total because the percent was too low to be factored in. According to DESE the attendance rate for Warren Elementary was 95.8% this is a good percent considering this is just for one school. I did look up the percent for the school district as a whole and it is 96.2%. When looking up some of the information on this particular school, I wanted to compare the numbers to the school districts numbers and these next few categories look to be on the higher side for one school compared to the whole district. The percent for free and reduced lunches for Warren Elementary was 14.9%, for the Francis Howell School District it was 19.9%. As far as the student/teacher ratio its 18 students: 1 teacher, the Francis Howell school district its 15 students: 1 teacher. Obviously, these are just average numbers and are going to fluctuate from each school and the district. The next section of data that I collected was on the AYP (Adequate Yearly Progress) status for NCLB, the data on the DESE website was a little outdated it had information from 2009-2011 (no date for

Are there significant implications between negative family environment and math scores of 5th grade students on KPrep?

McCracken, J. D., & T. Barcinas2, J. D. (2001). Differences Between Rural and Urban Schools, Student ... Retrieved from http://www.bing.com/cr?IG=48CE59FFB178484396E518C99D35AF6D&CID=22115C54F69067CD3DD85056F76D665F&rd=1&h=g8pSj71rFlTKBN6m19EtLd9ZpLbVelc7-zRGkIZhAi8&v=1&r=http://jrre.vmhost.psu.edu/wp-content/uploads/2014/02/7-2_3.pdf&p=DevEx.LB.1,5069.1

If children who have moved to a new school in the same state had to choose their favorite school, then some mixed reviews will occur. Some of these children may prefer their previous school while the others may prefer the new one. If asked to develop a list of similarities and differences between the schools, then, as a result, various children will say that the two schools are similar. Now apply this same scenario to a group of children who have moved to schools in a different state, the children would say that the two schools are like night and day. This is true in the case of Frank W. Cox High School in Virginia Beach, Virginia, and Ridgeview High School in Orange Park, Florida. A handful of people who believe this to be untrue. These people believe that regardless of location, two schools

Controls used in the statistical model other than just the charter status are student background, grade cohort in school effects, and school exact effects which are likely to affect student achievement. In the model, i, j, k and t represent individual schools, students, grades, and years; s is the test score for each student for all of his/her school years, α a represents an disregarded school-specific characteristic that does not get any modification over time,δ is an disregarded grade-cohort influence within each school, γ is an disregarded restriction reflecting the potential effect of charter school attendance on s; C is an gauge variable that equals one if the school is a charter school and zero otherwise, x is a 1 x K vector of K observable factors affecting s, β is a K x 1 vector of disregarded parameters, and v is a random error term (Buddin, Richard, and Ron Zimmer.

Since this study is strictly quantitative, it faces the limitations of not being suited to answer how and why questions. The data analyzed may not be robust enough to explain complex issues, and makes it difficult to understand the context of a phenomenon (Mills and Gay, 2016). The data in this study indicate the number of opportunities at a given school, but gives little information of the quality about these opportunities. For example, multiple schools may offer Advanced Placement (AP) courses, but the quality of the instruction and implementation of these courses could vary significantly across schools. Additionally, the data do not take into account local school policies and practices that differ between cities, districts, and schools. Moreover, accessing secondary data may be difficult – although the data are readily available online on the school level, it may not be possible to gather all the data into one comprehensive file. Also, since the datasets vary in the types of schools included (e.g. some datasets may exclude some charter schools), the process of merging the files may not result in a one hundred

The extent of student mobility: Rumberger states that mobility is hard to measure because there is no federal mandate to report this data. As a result, the data is collected from national education studies, state generated data, and from local school districts. The last time data was collected nationally was by the U. S. Department of Education (DOE) in 2000. The policy brief summarizes the DOE data and other data from national studies, state agencies, and local agencies or districts.

After collecting and organizing the data from the district report card for Yankton School District, one of the most interesting trends I found was the difference in scores between 2013 and 2014. For the years 2012 and 2013, scores were reported on four levels: Below Basic, Basic, Proficient, and Advanced. Then in 2014, the scores were reported on a new leveling system including the four levels: Level 1, Level 2, Level 3, and Level 4. When reviewing the different levels, I compared Level 1 to Below Basic. In 2013 for the 6th-grade class, out of 190 students, 0.53% of those students scored at the Below Basic level in math. However, in 2014 for the 6th-grade class, out of 166 students, 32.32% of those students scored at Level 1. Even when evaluating the same group of students, the 7th grade class for 2014, I noticed a rather large increase as well, with 23.30% of 209 students scoring at the Level 1.

First, the author assumes that the difference in performance can be directly attributed to the difference in the amount of homework given. Perhaps the quality of the teachers in Marlee is better than that of the high schools in Sanlee. Or, it could be that the schools have different standards for their students. Is there standardized testing in Attra? Perhaps students in Sanlee are failing in other subjects, not science or math. How are they doing in these other classes? Without the answers to the questions, the author can not assume that the homework makes a difference in student success.

This study will focus on one school in a suburban area in Pennsylvania. While the results of this study will provide insight to the usefulness of the CDT in this setting, future researchers should use caution when considering the generalization of these results with larger populations.