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

Since the active cases in Anne Arundel County Public School students have increased since Thanksgiving, I was curious about the percentage of students with active cases by age range. The rates chart below are just estimates because the total number of students is from the prior year (2020-2021) and the case data has extra categories (i.e. charter/contract, other, and specialty centers). As you can see, rates for all school types stayed below 0.4% until mid-December, when all rates increased sharply. The rate for high school students increased the most, up to 0.8% on December 15.

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.

Somethings you think are going to be easy, but then you learn that you still have more to learn. In a class, I learned to make a dynamic calendar in Excel. I then expanded on what I learned to make a calendar that shows the first and last day of the Session, and the day that the 90-day report is finished. I thought it would be easy to upload the Excel sheet to my website to document what I learned. So far that has not been easy. I’ve been able to save the Excel worksheet as an HTML page; however, I can’t get it to display on in an iframe the way I display my tricky data visualizations. It might be because the Excel workbook has multiple sheets. When I try to do that I get a 404 error. I have attempted to upload the page to my file manager, but that has led me to realize I have not yet used that feature, so I don’t know the password for the site.

If I upload the Excel workbook to my site, all that does is make the worksheet download automatically upon loading the website. This is not something I want my website to do. I want users to be able to choose to download information, but I do not want it to automatically download.

So until I figure out how to display a dynamic version of this calendar, here is a pdf of the beginning of 2022. It shows that Session begins on Wednesday, January 22, 2022, which is the second Wednesday of the year. It ends 90 days later on Monday, April 11, 2022. I am finally able to take a break on Friday, April 15, 2022, when the 90-day report is finished.

As this website is primarily a personal learning experience I am documenting my learning experience rather than just perfection.

This is a test of a waterfall chart with negative numbers. This is just a test to see if I can recreate a waterfall chart I saw using Amcharts. I have linked the document with the original waterfall chart in the source below, the chart is on page 16 of the document. Initially had trouble with showing the final two bars because they went into negative numbers, but I was able to play around with the formatting to get it to display properly. As I note I do not have access to the underlying data, I used the displayed figures to produce the chart below. To completely recreate the chart I need to still figure out how to format the Y axis numbers. I need to add a “$” sign and truncate the numbers. I will research this and update once I figure it out.

ARP: American Rescue Plan

COLA: cost-of-living adjustment

IT: information technology

Source: Department of Legislative Services

I have made a few updates to my Sankey diagram that shows college enrollment by dually enrolled students. I really wish I had college enrollment data on non-dually enrolled students so I could compare the two groups.

I am publishing this update because my primary goal of this blog is to document for myself how to make better data visualizations.

Dual Enrollment College Pipeline

Twenty-one percent of public school 12th graders in Maryland during the 2019-2020 school year had a college record, that is they had been enrolled in a college class. The majority of these students participated in a dual enrollment program through their local school system. In fall 2020, the fall after they were slated to graduate from high school 79% of students who had a college record enrolled in college. This was during the height of the COVID-19 pandemic. Of those who enrolled in college, 75% enrolled at an in-State institution and 25% enrolled at an out-of-state institution. A little more than half of the in-State students (55%) enrolled at a community college, the remaining 45% enrolled at a public four-year or State-aided independent institution. Further, approximately half (51%) of students who enrolled in an in-State institution enrolled at their college of dual enrollment.

Source: Maryland Longitudinal Data System Center

Further Questions

I would be curious to know how college enrollment patterns of dual enrollment students compare to all public school 12th graders in Maryland. The Sankey graph above with that information would be much improved. Without that information, I am not really sure what it all means.

I am also interested in whether the dual enrollment credits earned transferred to the college in a meaningful way. According to MLDS data, students that took dual enrollment courses in high school earned an average of 2.14 credits.

Further, I wish that I had information on whether these students took AP or IB courses, which can also lead to college credit. I am curious if students are taking dual enrollment courses in addition to or instead of these courses. I wonder if the courses taken are primarily due to student choice, or due to the courses available at the student’s particular high school.

Finally, I am curious how these students perform in college.