Tag: Datawrapper

In November 2023, the U.S. Department of Education released new data about bachelor’s degree completion rates for transfer students. I have been interested in transfer students for years and it is a topic that I want to better understand. I use this blog to remember the data I read about and capture my initial thoughts. It is also a place for me to explore using data visualization tools.

DYAD

The researchers defined a dyad as a pair of institutions consisting of a public community college and a public or private four-year institution. The data only includes pairs where at least 30 students enrolled in the community college in 2014 and at least 30 students transferred and graduated from the four-year institution in at least 8 years. Nationally they identified 385 dyads, of which 8 were in Maryland.

Success of DYADS

Montgomery College belongs to four of the eight dyads in Maryland, showing that Montgomery College has a robust transfer program.

Maryland’s most successful dyad is students who transferred from Wor-Wic Community College to Salisbury University; 10% of students who transferred using that pathway graduated with a bachelor’s degree. Nationally, there were only 18 dyads with completion rates of more than 10%. The most successful dyad, Tri-County Technical College X Clemson University, had a completion rate of 20%. Kapiolani Community College X University of Hawaii at Manoa had a completion rate of 16%. Followed by four dyads with a completion rate of 13%, four dyads with a completion rate of 12%, and eight dyads with a completion rate of 11%.

Size of Transfer Programs

In addition to the completion rate of students transferring in a dyad, it is interesting to examine the size of a dyad program because in my mind a truly successful dyad will have both a high completion rate and be the "right" size. What the "right" size is I'm not sure of yet, but of a size that shows that is sufficient to support the continued transfer of students and meets the needs of the students.

The largest number of students transferred from the Community College of Baltimore County to Towson University (2,282). It looks like all transfer students from Montgomery College who transferred to a four-year institution are counted in all four Montgomery College dyads, since the number of transfer students in the denominator is 1,856 for all four dyads. This is not something I fully understood when first looking at the data. If this is true, Montgomery College has a very high total completion rate of nearly 17%.

I experimented to make a chart showing both the number of students who transferred to a four-year institution and the number of students who completed a bachelor's degree. I sorted it by percentage of students who completed a bachelor's degree. I believe the chart works fairly well at illustrating all the data, and it helped me see the potential issue with the Montgomery College data at the same time.

I have been curious about the teacher pipeline in Maryland. So I did what I do when I have a question, I started looking for the available data.

So, I looked at the data published by the Maryland State Department of Education on the prior experience of new hires. According to the data, 58% (2,513) of newly hired Maryland teachers are new to teaching, 19% worked in another state (or the District of Columbia or Porta Rico) just prior, 13% worked in another Maryland county, 9% worked in a Maryland nonpublic school, and the remaining worked in another county or at the SEED School. This data set does not have information about the preparation of new teachers, so I do not know if they received their teaching training at a Maryland institution of higher education or in another state or country. When I have time I will look at other sources of data.

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 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.

I am exploring using a map to display college enrollment data for Anne Arundel County. Unfortunately, I only have a shapefile that includes Crofton HS, which is a new school, so the boundaries do not reflect the boundaries at the time. The are other specialized high schools in the county that are not reflected in the data. Since I haven’t done mapping in a while I had to remember how to upload the data, but I figured it out pretty quickly.

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

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.

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