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.