Today I decided to take a quick look at the percentage of all students who score proficient on the Statewide science assessment.

I also did the same map for economically disadvantaged students.

After looking at the maps I put them on the same color scale.

I believe this shows all test takers, both first-time and retakers, I wish they would separate them.

The first map below shows the percent of all test takers who were proficient (level 4 or 5) on the Grade 10 English/Language Arts Exam for the 2020-2021 academic year. The second map shows the percent of economically disadvantaged students who were proficient on the same exam during the same period. The all-student map includes the economically disadvantaged students. The definition of economically disadvantaged is not immediately clear from the data. The definition was not included in the “definitions” section of the website. There is a separate measure, which includes more students, for “FARMS”-(free-and-reduced priced meal.” I assume that it is students “eligible” for free-and-reduced priced meals, but that is not specified either. The third map shows the percentage of non-economically disadvantaged students who were proficient on the exam.

I put these maps together to see if there were schools that had high test scores for the full student body but were less successful for economically disadvantaged students. The problem I ran into, which isn’t really shown in the maps, is that the schools with really high test scores overall, like Severa Park, have only a few economically disadvantaged students overall.

All Students

Economically Disadvantaged Students

Non-economically Disadvantaged

I found a new dataset today. It shows the number and percentage of students that are promoted in high school every year.

The map shows the percentage of 2020-2021 grade 9 students that were not promoted to grade 10.

Played around with showing Non-FARMS High School graduates who earn a college degree by age 25. The Maryland Longitudinal Data System Center publishes the data as a percentage of high school graduates that enroll in college. I used their published numbers to see the total high school graduates. I was originally interested in FARMs students, but the data was repressed for most of the schools.

As always this is just me exploring the data that is available. I am trying to make sense of the data and be able to remember the information.

Immediate college enrollment decreased by 5 percentage points for both low-income and non-low-income HS graduates of the class of 2022.

I am a sucker for outcome data by state. I like to take the data from these reports and graph the Maryland data.

This is primarily a blog about me exploring data visualization. I am having trouble flipping the order of the categories, I would like “completed at starting institution” to be on the bottom. I think that being able to easily control the order of the categories is very important. The order shown hides the percentage of students that have graduated from any institution.

I figured it out, but I had to reenter the data. I would also like to add national data on the same chart, but that does not seem to be an option anymore.

Apparently, I can add national data if I make a stacked bar chart, but not for a stacked column chart.

I have been busy with the 2022 legislative session and new work responsibilities, but today I had some time to graph some enrollment data I read about today.

Maryland Estimated Enrollment by Sector 2020 to 2022

Nationwide Estimated Change in Enrollment By Major

This is a look at students enrolled in Maryland community colleges in fall 2019. In fall 2019, there were 113,288 students enrolled in Maryland Community College. Of those students, 35,905 (31.7%) were enrolled full-time and 77,383 (68.3%) were enrolled part-time. I decided to dig deeper into this information by combining attendance status with student age. The Maryland Higher Education Commission (MHEC) reports enrollment of students aged 25 and older by attendance status. Approximately one-third of community college students are aged 25 or older. About 85% of community college students aged 25 and older attend part-time, while approximately 60% of community college students under age 25 attend part-time.

I made a Sankey diagram to show both age and attendance status of community college students at the same time in the same diagram. This is a different use of a Sankey diagram than I have made in the past. In the past, I have used a Sankey diagram to show individuals flowing through the education system. This diagram shows two variables related to the same population of students. I am happy with the result and find it easy to understand and read; however, I don’t know if people unfamiliar with Sankey diagrams will find it easy to read and understand. My next step will be to ask my friends and family about what they think about the visualization. I also wonder if there are other data sets that I use that would be better understood using this formate. Another way to show this information would be with a single bar graph with four segments: 1) aged 25 and over and part-time; 2) aged 25 and older full-time; 3) under 25 and part-time; and 4) under 25 and full-time.

Sankey-Community College Fall 2019 Enrollment by age and attendance Status

Source: Maryland Higher Education Commission, Databook 2021

Bar Graph-community College Fall 2019 Enrollment by age and attendance Status

The bar graph shows the same information as the Sankey diagram above, but it is another formate. The purpose of this post is to show this data in different forms on the web and be able to test them on various platforms and to remember what I learned about the data. From the bar graph visualization, it is easy to see that the largest group are students who are under 25 and attending part-time and the smallest group (by far) are students who are 25 and older who are attending full-time.

AmCharts has updated its library to version 5. AmCharts5 includes updates to their Venn diagram library. Since I love Venn diagrams I spent the morning figuring it out. I created the Venn diagram below to show HS completers. It took me a while to figure out the syntax. Overall I think I was mostly able to make it do what I wanted to do. However, I since haven’t figured out how to put a category entirely in another category. For example, I would like a big circle with all HS completers that includes HS graduates and HS certificate students.

The Venn diagram below is of Maryland public school students that completed high school. It shows the overlap of how the students that: (1) earned a completion certificate (for completing a special education program); (2) met the University System of Maryland (USM) requirements); (3) met the Career and Technical Education (CTE) requirements; and/or (4) met the regular diploma requirements. “Normal diploma” is just to indicate students that did not earn either a USM or CTE credentials in addition to their high school diploma. This is primarily for my own understanding of the data and to learn web-based data visualization techniques.

Now that I have made this visualization I am not sure if using a Venn diagram is better than a Sankey diagram for this data. I would also like to add additional information to the chart such as a title and to have the actual numbers displayed on the chart. As far as the data goes, I wish that I had information about the post-high school behavior of these different groups of students. According to this data, about 60.5% of high school completers met the USM requirements.

The colors used below are not intended to mean anything beyond looking nice. It took me a while to figure out the colors. Once I figured it out I just used a rainbow with my only intention to combine blue and yellow to make green.

StateWide High School CompLetion Venn Diagram

Source: Maryland State Department of Education, 2019 High School Completion

I have been busy with other work, so I have not had much time to post. I have been using the information from my past posts in my other work, so I think this is a valuable use of my time. I am learning how to better visualize data and better able to remember what data I have already examined. The other day I got asked a question about dual enrollment, and the first place I looked to answer the question was at an old blog post I had written earlier in the year.

Today, I am taking a brief look at educator qualifications. I have not looked at this data before, and I saw it was posted on the Maryland State Department of Education’s website.

Types of Educator Qualifications

First I looked at the types of data that they publish. They publish the count and percent of inexperienced educators, inexperienced teachers, out-of-field teachers, and teachers with emergency or provisional credentials. The data has two files, one by poverty level and the other by students of color. I love that I can download this data in an Excel file easily, but a weakness of the data presentation is that I’m not always sure what the definitions mean and there isn’t any easily accessible documentation. I could probably get additional information if I asked, but it isn’t worth it for my purposes which are learning data visualization techniques, getting a better idea about the data available, and remembering what I have read.

For this chart, I kept it in the order that the data is published, which is mostly alphabetical with Baltimore City, SEED, School, and statewide at the bottom. For a more formal chart, I would move Baltimore City up into alphabetical order and decide what to do about SEED and statewide. That level of effort didn’t seem reasonable for this exploratory chart.

For this split bar chart, I think that it is interesting that the grayed-out area does not equal 100%, rather I think it is the largest value in the column. I’m not sure what I think about it, but I do think it makes it easier to compare some of the larger values.

I wonder why some local school systems have more educators and teachers that are inexperienced, teaching out-of-field, or on an emergency or provisional credential. I will have to do more research into this area, but it is good to know this data exists for my future work. Next, I plan to dig deeper into the out-of-field teachers poverty level.