Tag: Maryland

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 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.

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