Module 7 assignement1
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School
Walden University *
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Course
8823
Subject
Mathematics
Date
Apr 3, 2024
Type
docx
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2
Uploaded by ctankersley2005
Representation and Analysis of Data Module 7
During the 8815 course, I looked at various types of data. I have been able to determine and evaluate the types of data collected. I have also been able to look at graphs and the way that the graphs were created using SPSS software. For this assignment, I was asked to find relevant data for my school. For this assignment, I have chosen to look at attendance for my school over the last three years and look at iReady scores in Reading and Math from the beginning to the middle of the year. The attendance data is broken down into many domains or categories. I can see the attendance for 2021, 2022, and 2023. I can also see grade levels: prekindergarten, Kindergarten, first grade, and second grade. I can see data collected on males and females, ethnicity, and whether they are gifted, ELL, migrant, or retained students. A lot of data is collected on attendance in different domains. With this data, I can determine which students have the best attendance and which have bad attendance and compare them. The data on attendance is collected daily by the teachers in Infinite Campus. Then, the data is sent to the board office, where it is put into graphs to be evaluated. This is public information that anyone can obtain. When looking at the data, I can see that in the year 2022, we had the highest absent rate. 54% in pre-kindergarten miss more than 10 days, 59% in Kindergarten to miss more than 10 days, 44% of first graders to miss more than 10 days, and 48% of second graders to miss more than 10 days. Looking at race, students with more than one race had the most absence, while black and non-Hispanic had the least absence. The students that
are labeled gifted, Ell and migrant also had the least amount of absence. The data that I have obtained does not show me the mean score or how the mean was calculated. It does give me
graphs with percentages with each category. One thing that I would do differently if I were collecting data, is to give a total of students I am collecting data on. The next set of data that I collected was on iReady scores. These scores on Reading and math for Kindergarten, first and second grade. They are also broken down into domains of each area in each subject. I was also able to obtain the information for diagnostic 1 and diagnostic 2. One is given at the beginning of the year to give a base line of student’s performance whereas the
other is given in the middle of the year to see growth. Then another will be given at the end of the year to see how students performed by the end of the year. The data that was collected is in the form of a bar graph with domains of midlevel, early on grade level, one grade level below, two grade levels below and more than three grade levels below. When looking at the data for reading on the first diagnostics, it shows that out of 412 students there were 54 students that are on or above grade level. Then on diagnostic 2 it shows that there are 115 students that are on or above grade level in reading. This tells me that there was improvement. Looking at Math data for
iReady, on Diagnostic 1 26 students out of 412 scored on or above grade level and diagnostic 2 shows 184 students on or above grade level. When looking at this data, it does not tell me how the information was calculated other than domains for each subject. If I were collecting the data, I would like to see how each student performed on each question. How many students missed number 5 for example.
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