Author: carolineboice_x5gxjq

More Than 80% of the Student BODY At Most COmmunity COlleges are Maryland Residents

According to the Maryland Higher Education Commission, 91.6% of Maryland community college students are Maryland residents. This is not surprising at all as community college students tend to attend their local community college. From looking at a data table I was able to see that more than 80% of the student body at most community colleges are Maryland residents. However, only 55.4% of students at Allegany College of Maryland are Maryland residents. With that information and a bit of curiosity about using Datawrapper for mapping, I decided to build a map showing the location of the main campus of each community college and the percent of students that are Maryland residents.

Building the map was relatively simple. I used google to look up the address for the main campus address for each of the 16 community colleges in the State. I then pasted that information into the program and added typed in the Maryland resident information for each college. From my basic sense of the locations of the colleges, the placements look accurate, but I have not checked them.

I struggled with how to best visualize the data since most of the colleges have more than 80% of the student body being Maryland residents. Reducing the number of color categories to three really helped with this issue. It highlights that Allegany College of Maryland is an outliner and Hagerstown Community College is almost an outliner. I probably should have either rounded the numbers to make Hagerstown Community College 80% and reduced the categories to two colors, but I think it is a little fun to see that Hagerstown Community College does not quite meet the 80% requirement. I could also say something such as all but one community college has 79% or more of its student body as Maryland residents. If I was highlighting Allegany College for some reason I would probably do one of those options. But since I am just playing around with the data to see what jumps out at me, I have not done that this time.

After I mapped the data I saw that Allegany College is very close, less than 3 miles, from the West Virginia border. It is also close to the Pennsylvania border. There are other community colleges that are not far from the State borders, but these colleges seem to draw a smaller percentage of non-Maryland residents. Perhaps if I put the color break at a higher percentage and treated Allegany College less like an outliner that story would become clearer, but I have not yet tried that.

WHat I learned from this Mapping Experience

  • Adding location markers in Datawrapper is easy, but can be time consumming
  • Datawrapper is significantly easier to use than other mapping software such as ArcView, but the data crunching functionality is less
  • I have not yet figured out how to show just one of Maryland's counties in a map
  • When most of the data is in the same range, but there are outliners, the data can be hard to visualize
  • I haven't decided how to handle colleges with mulitple campuses when mapping. Representing the "main" campus seems to be the best way for now.
  • Color breaks can change the data story

Ploting the Data

Yesterday I looked at SAT scores in Maryland by county and income and for the incoming freshman classes of the public four-year institutions. Today I take a brief look at SAT scores by county and race/ethnicity from the same dataset published by the Maryland State Department of Education. I made a dot plot using Datawrapper with all the race/ethnicity categories available in the data set as well as “all students”. To be honest, the chart looks very busy and is rather hard to read. I changed the color scheme to reds and oranges to aid with distinguishing the categories, but it only helped a little. I could choose custom colors for each group, and would if I was intending to show this data to a wider audience, but since this is primarily for my own exploration of the data I decided I did not have the energy to make those choices today. I did decide to highlight the “all student” category to help with readability a bit.

An alternative visualization, and the one I have seen used at State Board of Education meetings, is a grouped bar chart. While I think that would work for smaller numbers of counties or race/ethnicities, I think that it is worst than the dot plot for a large amount of data. However, I may explore this visualization in the future.

SAT SCores by county and Race/Ethnicity

It is hard to draw any conclusions from this data. For one thing, I am unsure if this data represents public school students or all students who took the test from that county. For another thing, not all students take the SAT and different local school systems have different policies about pushing students to take the SAT.

Howard County stands out as having very high scores, for students of all races. From the data, I do not know if Howard County encouraged only high-performing students to take the SAT. I would be surprised if that was the case, but it is a possibility. Since there is information about the number of students that took the test, I might be able to infer the policy from that data or I might look at their website to see if they have a SAT policy.

Note: I likely will not be posting for a while as I will be on vacation.

Context

I have been reading through the Maryland Higher Education Commission’s Annual Data Book 2021. I am interested to see if I can learn any new insights by exploring different visualizations of the data. I am also teaching myself how to use data visualization/graphing software. Today I am taking a look at SAT scores statistics published in the databook as well as additional SAT scores published by the Maryland State Department of Education. The populations for all of these datasets are slightly different.

SAT Scores of Entering for High School Seniors 2020

According to the databook, the mean combined evidence-based reading and writing score and math for Maryland high school seniors in 2020 was 1029, slightly less than the nationwide mean of 1051. I have read in the past this is because a higher percentage of students in Maryland take the SAT than nationally, but I will have to find a citation that is true.

SAT Scores By COunty

On the Maryland Report Card, the Maryland State Department of Education publishes data about students. As part of the "college readiness data", average SAT scores are published. In addition to an average for all students from the local school system, the scores are disaggregated by a number of subpopulations including by low-income students, that is those eligible for free or reduced price meals. Low-income students scored lower on the SAT than all students in every county. However, the average scores were nearly identical for students from Dorchester County. At the SEED school, where almost all students are low-income, low-income students scored higher than all students.

There is a large range of scores between counties and between low-income students and all students in most counties. Howard County has the highest average score for all students and the second-highest average score., after Carroll County, for low-income students. In fact, low-income students from Carroll and Howard counties had a higher average score than the average score for all students from 14 counties, Baltimore City, and the SEED school.

I plan to examine the data for more subpopulations in the future. Including the best way to visualize the data.

SAT SCores of Entering Freshmen

The MHEC databook publishes the average, 25th percentile, and 75th percentile SAT scores of entering freshmen at the public four-year institutions. Institutions submit aggregated data on average SAT scores for all incoming freshmen. Some institutions do not require SAT scores for admission. Institutional score ranges are based upon those scores that were used as a basis for admitting students to the institution.

This chart shows the SAT scores of entering freshmen for the University of Maryland, College Park from the 25th to the 75th percentile

Another Attempt at a Sankey College Pipeline

Another day, another attempt at using amcharts to illustrate the college pipeline of Maryland public school students. This time I used the Maryland Longitudinal Data System Center data. I am still struggling with getting the data to display in the chart in the way I want it to, but I am making progress. I figured out how to fade out categories when I do not know what happened to the students. For example, I faded out students that did not immediately enroll in a college directly after high school. These students may have never enrolled in college, may have enrolled in college at another time, or even enrolled in college in another country.

I still have not figured out how to have the full label on the right-hand side to display. I played around with a few settings, but none of them allowed the full label to show. There is documentation for the am charts software, but I have not yet read through it all and I do not have a ton of experience with writing javascript. I think if I experiment with it more I will continue to learn how to use it.

As I figure out the program you need to click on the actual post or this link to see the chart. I am working on this issue.

What I can learn from this Pipeline data

From this data, I can only learn the ultimate fates of 32% of the students who exited a Maryland public high in 2011 at the end of 12th grade. That is the percentage of high school exiters that graduated college (from the same sector they initially enrolled) or are still enrolled in college at the same sector.

I know nothing about the students who did not immediately start a college degree program in the fall and nothing about students that transferred between higher education sectors. To really understand the pathways I need more information. I would love to be able to illustrate how students flow through the higher education system. I want to know how many students reverse transfer, and how many of those who do get degrees, either an associate’s or a bachelor’s.

Notes About the PipeLine Data

The following are the notes about the data provided by the Maryland Longitudinal Data System Center. The data explore the high school graduation, college-going, and college graduation for 12th grade students who exited Maryland public schools in 2011. The notes are very important because I am trying to deeply understand all of the data I am examining.

community College Notes

To be counted as a community college graduate, the student must have enrolled in any community college and graduated from any community college. Students who start at a community college but graduate from a college in another sector are not counted as graduates. Students who started in another sector but graduate from a community college are also excluded. To be counted as persisting (still in college), the student must have NOT graduated from any community college and be enrolled in any community college in Fall 2019. Some students who enrolled in community college transferred from the college and are enrolled in another four-year public, state-aided independent, or out-of-state institutions.

Public Four-Year NoTes

The four-year public table above evaluates within sector college graduation and persistence independent of college of enrollment. To be counted as a college graduate, the student must have enrolled in any four-year public and graduated from any four-year public. Students who start at a four-year public but graduate from a college in another sector are not counted as graduates. Students who start in another sector but graduate from a four-year public are also excluded. To be counted as persisting (still in college), the student must have NOT graduated from any four-year public and be enrolled in any four-year public in Fall 2019. Some students who enrolled in a four-year public transferred from the college and are enrolled in another community college, state-aided independent institutions, or out-of-state institutions. Those students are not reported here.

State-aided Independent Notes

The state-aided independent table above evaluates within sector college graduation and persistence independent of college of enrollment. To be counted as a college graduate, the student must have enrolled in any state-aided independent institutions and graduated from any state-aided independent institution. Students who start at a state-aided institution but graduate from a college in another sector are not counted as graduates. Students who start in another sector but graduate from a state-aided independent institution are also excluded. To be counted as persisting (still in college), the student must have NOT graduated from any state-aided independent institutions and be enrolled in any state-aided independent institutions in Fall 2019. Some students who enrolled in a state-aided independent institutions transferred from the college and are enrolled in another community college, public, or out-of-state institutions. Those students are not reported here.

Out-of-State Notes

The out-of-state table above evaluates within sector college graduation independent of college of enrollment. To be counted as a college graduate, the student must have enrolled in out-of-state institutions of any type and graduated from an out-of-state institution of any type. Students who start at an out-of-state institution but graduate from a college in Maryland are not counted as graduates. Students who start at a college in Maryland but graduate from
an out-of-state institution are also excluded. Out-of-state institutions may be community colleges, public four-year, or other types of private institutions.

Additional Notes

  1. Exiter is defined as a student who is enrolled in a Maryland public school through the end of 12th grade.
  2. High school graduate is defined as a 12th grade exiter who fulfills the requirements to graduate from a Maryland public high school.
  3. Immediate college enrollment is defined as a high school graduate who entered college as degree-seeking in the fall immediately following high school graduation.
  4. College graduate is defined as a high school graduate who entered college as degree-seeking in the fall following high school graduation and arned any college degree by age 25.
  5. Still Enrolled is defined as a high school graduate who entered college as degree-seeking in the fall following high school graduation, did not graduate from college and is enrolled in college in Fall 2019.
  6. Enrollment in a graduate program is defined as a high school graduate who entered college as degree-seeking in the fall following high school graduation, completed a college degree by age 25 and enrolled in a Master’s degree program.
  7. Graduation from a graduate program is defined a high school graduate who entered college as degree-seeking in the fall following high school graduation, completed a college degree by age 25, enrolled in a Master’s degree program and earned a Master’s degree by June 2019. Students enrolling in PhD programs, or professional degree programs (law, medical, etc.) are excluded from this analysis.

Exploring the AMCharts Funnel

I have not yet figured out how to edit the Sankey chart display for the chart I made yesterday, so I decided to use the same data to make a funnel chart. I copied the code from Amchart’s website and altered it for my data. It was easy to change the label categories and numbers to represent my data. It was also easy to change the number of categories. I haven’t figured out how to delete or change the percentage the code calculates. The percent number is the percentage that category is of all the numbers in the chart, which is misleading and not very useful. This one issue is rather unfortunate as otherwise, I like the funnel chart. I like how it automatically scales for the size of the category. The colors are appealing, but I’m not sure if I am able to change them.

The data is from the Maryland Longitudinal Data System Center. It is grade 12 students who exited Maryland public high schools in 2011.

AMCHARTS for Sankey visualiation

While I have been loving playing with Datawrapper, it does not have a sankey option. So I went in search of a WordPress compatible application that makes sankey visualizations. The first I came across was Amcharts. It is a bit more complicated than Datawrapper, but I think I got it figured out. I still have a ton of options to figure out, but I believe I am able to make a chart appear. Since building the chart took so much time I am going to save the explanation of the data for when I update this post. Right now I just want to see how the visualization works.

Maryland Public School College Pipeline | Caroline Boice

As I figure out the program you need to click on the actual post or this link to see the chart. I am working on this issue.

UnLabeled Category Limits Use of Data

After my last post about community college outcomes using data published by the Maryland Higher Education Commission (MHEC), I went in search of more data to get a better sense of the story. I want to know what happens to students that initially enroll in a Maryland community college. I found some data published by the Maryland Longitudinal Data System (MLDS) Center in 2016. It is almost exactly the data I am looking for, unfortunately, there was an unlabeled category that consisted of 25% of the cohort. With this unknown category being such a large part of the pie, I feel that the data is of limited use for really understanding the outcomes of community college enrollees. Despite my belief that the data may be limited, I am going to take a closer look at the data to see if I can learn anything from it.

It is also unclear the time frame after enrollment that the data covers. I know that the population is community college students that were enrolled during the 2008-2009 academic year. I also know that the data was published in 2016. It is unclear if the data set is all students; first-time, full-time students; or another population. I think that my next step may be to contact MLDS Center to clarify what data is being presented. There may have been additional information in the documentation that I have missed.

When I publish graphs in the future I want to be sure to be clear about the data that is presented. In education public policy I feel that this is especially important as the specifics matter. For example, a cohort of first-time full-time students is not the same as a cohort of all community college students.

If Data is Accurate: Few CC STudents Transfer; Fewer earn a BA

If this data is accurate, few community college students transfer to a Maryland four-year institution, only about 6% of the cohort. This is much less than the 12% of the first-time, full-time cohort in the data reported by MHEC. This leads me to believe that the transfer rate in this data source may be underestimated. I wish that I could trust that this data is fully accurate because, unlike the other data source, this data source reports the percent of students that earned a bachelor's from a Maryland four-year institution after transfer. It shows the specific community college to four-year institution pathway to a bachelor's degree that some experts recommend, especially to lower-income students as a method to save money. But it isn't a method to save money if students do not actually end up earning a degree. According to this data, 1% of the students (421 students) that begin at a community college earn a BA from a Maryland four-year institution.

If only 1% of a community college cohort earn a BA, then that pathway to a BA is broken. However, I need to dig into the data more, because other data sources report a slightly less bleak picture. According to data published by the Aspen Institute, which is shown below, nationwide 14% of community college students earn a BA within six years of transfer. However, like the MLDS data I discussed above, I am not sure of the student population, so I need to do more research into the data. Overall I need to better understand the data sources before I make any conclusions.

A Note About THis Data Presentation

As I have said in previous posts I have really been loving playing with data presentation using datawrapper. However, for the multiple pie charts shown above I ran into a few issues. Firstly, the website was being glitchy, not showing changes I had made to the graph and hiding the graph completely. Second, I was not able to make this type of graph look exactly how I wanted it to look. I did not want to show the percent for the grayed-out space and that was not an option because the categories are not the same. This is not surprising, I think that for that type of functionality I would need a graphic designer and not a web application. So while there are a few issues, I am still extremely happy with datawrapper. I am just trying to document for myself the limits of its functionality.

Commmunity college Graduation and Transfer Data

According to the Career and College Readiness and College Completion Act of 2013, it is the State’s goal that all degree-seeking students enrolled in a public community college earn an associate’s degree before leaving the community college or transferring to a public four-year higher education institution. Therefore the Maryland Higher Education Commission (MHEC) tracks community college students’ graduation and transfer outcomes. MHEC does not have the ability to follow students who transfer to out-of-state institutions. Therefore, the data only reflects transfers to institutions in Maryland.

For this data set, the analysis cohort is all first-time, full-time students entering Maryland community colleges within the fall term of a given year. Three successful outcome measures are tracked.

  • Graduated and transfered: Student graduated with an associate degree or lower-division certificate and transfered to a Maryland four-year college or university.
  • Graduated/Did not transfer: Student graduated from a Maryland community college with an associate degree or lower-division certificate and did not transfer to a Maryland four-year instituion.
  • Transferred to four-year college or univeristy without graduating: Student transferred to a Maryland four-year insitution, withou having completed an associate degree or lower-division certificate.

Now Most Community College Transfer Students Graduate Before Transfering

I decided to display the data as a line chart, but with different visual clues from MHEC’s original line chart. For my chart, I added shading between graduated and transferred and transferred without graduating. I like how it clearly shows that in 2014 the number of students who graduated before transferring surpassed those who transferred before graduating. Although the numbers had been trending in that way, the legislation likely had some impact.

I wish we had the data to analyze the outcome of both types of transfer students at four-year institutions. Past research suggests that the students that graduated community college first prior to transferring be more successful, but I wonder if this is still true. This might be a question to examine in the future.

64% of a Cohort does not transfer or graduate in four years

The line chart above does not capture the approximately 64% of the cohort that do not graduate or transfer within four years, so I decided to make an area chart. The area chart shows that the majority of students that begin as first-time, full-time community college students do not graduate or transfer within four years. Although when the number of first-time, full-time students decreased after peaking during the Great Recession, the percent of students that did not graduate or transfer has dropped to about 60% of the cohort.

This data set does not capture if these students are still persisting or if they dropped out. It is also possible that these students transferred to a two-year or four-year institution out of State. This data set only captures a subset of successful outcomes for community college students: graduation and in-state transfer.

This chart reminds me that while measuring the success of transfer students that did or did not graduate is interesting and important, there are a large number of students that entered community college as full-time students who have not graduated or transferred.

Maryland Community College Student Outcomes

This data only tells part of the story of community college student outcomes, but it is a good place to start. One of the biggest issues in education public policy is the lack of data that tells the whole story. Another issue is knowing what data is important to analyze. I plan to continue to look for data to analyze.

Student Performance On THird Grade Assessments 2019

Yesterday the Maryland State Board of Education presented data about student performance on the third grade Statewide tests in a discussion on learning loss and recovery prior to the COVID-19 pandemic. The new State Superintendent said that Maryland has one of the bigger performance gaps between students eligible for free/reduced meals (FARMS) and students not eligible for FARMS; however, I am not sure how that is measured. I was happy to see that they divided the data by students eligible for free/reduced meals and by race/ethnicity as that is not always done. The graphs show that regardless of race/ethnicity, family income matters when it comes to performance on third-grade assessments.

I would be curious to know if this data is available further divided by local school systems. If any school system is doing better than the others with regards to the performance gaps it might point to policies that are contributing to better student performance that may be able to be replicated in other local school systems.

Third Grade English Language Arts Performance

Student Math Performance

Data PResentation

For these graphs, I generally copied the presentation by the Maryland State Department of Education for this data for their presentation. I did not add axis markers because I did not release that they were missing until I published the graphs. I thought about making a range plot, but I didn't have the data for "all students" in a category, and for this data, I decided I preferred the bar graphs for this data. The bar graphs show that there is a performance gap between FARMS and non-FARMS eligible students for every race/ethnicity category.

I played with using low-income vs. non-low-income rather than FARMs, for the presentation of the data to a general audience, but I did not commit to the choice as you can see. This is a problem that I face frequently as a policy analyst, do I simplify data presentation for a general audience or do I provide all of the specifics so that fellow wonks can understand specifically what I am data presenting? This is a skill I want to work on in the future.

Today I looked at a report on the Pathways in Technology Early College High Schools (P-TECH) program. In six years or less, a P-TECH student can graduate with a high school diploma and a no-cost, two-year Associate of Applied Science (AAS) degree in a specified discipline. Each P-TECH program requires a partnership among a local school system, a local institution of higher education, and a local employer. The partnering company agrees to provide students with mentoring, paid summer internships, and first-in-line consideration for job openings with the school’s partnering company.

Maryland is currently phasing in nine P-TECH programs in six jurisdictions. The first P-TECH programs in Maryland were established at Carver Vocational-Technical High School and Paul Laurence Dunbar High School, both in Baltimore City. They had their first class of students earning graduate from high school in the 2019-2020 school year. According to data reported by the Maryland State Department of Education, 92.6% of Carver P-TECH students and 100% of Dunbar P-TECH students earner their high school degree. In addition, 48.1% of Carver P-TECH students also earned an AAS degree in four years. Additional students may earn their AAS in the fifth and sixth years of the program. According to the chart shown below, 31 P-TECH students at those two schools returned for the fifth year of the program in 2020-2021.

The report included information on Maryland P-TECH program enrollment for the 2020-2021 school year by grade and by school. Overall, there were 1,065 students enrolled in a P-TECH program. Grade 9 enrollment ranged from 25 students to 61 students. Enrollment is anticipated to grow as the programs continue to phase in. All currently operating programs will be fully phased in by the 2025-2026 school year.

The Pathways in Technology Early College High (P-TECH) School Act of 2017, states that it is the intent of the General Assembly that no additional P-TECH school shall be established other than those that receive a P-TECH Planning Grant in fiscal year 2017 or 2018 until the P-TECH Program is shown to be successful in preparing students for the workforce or for further postsecondary education. Thus, no new P-TECH schools funded by the State of Maryland will likely be established until an evaluation of the current programs is completed. However, the statutory language does not require a formal evaluation.

We use cookies to personalize content and ads, to provide social media features and to analyze our traffic. We also share information about your use of our site with our social media, advertising and analytics partners who may combine it with other information that you have provided to them or that they have collected from your use of their services. You consent to our cookies if you continue to use our website.

Preference Allow All Disable All
} catch( err ) { console.log( err ); } try { // Create chart instance var chart = am4core.create("amchart3", am4charts.SankeyDiagram); // Make the chart fade-in on init chart.hiddenState.properties.opacity = 0; // Set data chart.data = [ { from: "HS Exiters", to: "Did not Graduate HS", value: 2405 }, { from: "HS Exiters", to: "HS Graduates", value: 58365 }, { from: "HS Graduates", to: "Did Not Immediately Enroll in College", value: 30054 }, { from: "HS Graduates", to: "Immediately Enrolled in College", value: 28311 }, { from: "Immediately Enrolled in College", to: "Some College", value: 9960 }, { from: "Immediately Enrolled in College", to: "College Graduate", value: 18351 }, ]; chart.dataFields.fromName = "from"; chart.dataFields.toName = "to"; chart.dataFields.value = "value"; // For right-most label to fit chart.paddingRight = 30; // Make nodes draggable var nodeTemplate = chart.nodes.template; nodeTemplate.draggable = true; nodeTemplate.inert = true; nodeTemplate.readerTitle = "Drag to rearrange or click to show&hide"; nodeTemplate.showSystemTooltip = true; nodeTemplate.width = 30; var defaultState = nodeTemplate.background.defaultState; var defaultDropShadow = defaultState.filters.push(new am4core.DropShadowFilter()); defaultDropShadow.opacity = 0; var hoverState = nodeTemplate.background.states.create("hover"); var hoverDropShadow = hoverState.filters.push(new am4core.DropShadowFilter()); hoverDropShadow.dy = 0; } catch( err ) { console.log( err ); }