Explore the basic functionality of the National Student Clearinghouse’s Postsecondary Data Partnership Institution-Level dashboard.
This tutorial explores the basic functionality of the National Student Clearinghouse’s Postsecondary Data Partnership Institution-Level dashboard. Thank you for joining us.
This is the Home Page for the Postsecondary Data Partnership dashboards. The dashboards are divided into three sections:
The first section are the executive summary and enrollment dashboards. The executive summary contains summary metrics from other PDP dashboards. The Enrollment dashboard provides information on the characteristics of the first-year student cohort.
The second set of dashboards report on early momentum metrics like credit accumulation rate, credit completion ration, and Gateway Course Completion.
And the last set of dashboards report on long-term outcomes like credentials earned, completion and transfer rates, and retention/persistence rates.
Let’s select the Credit Accumulation Rate institution-level dashboard, which is the first early momentum metric, to explore its functionality. Most of the PDP dashboards have a similar structure. Clicking on the link takes you to the dashboard.
At the top of the screen are the global filters. Filters allow you to assess the short-term and long-term outcomes for a specific student population. For example, if you would like to understand the outcomes of part-time students, you could filter by Attendance. Or if you were interested in understanding the outcomes of transfer-in students, then you could filter by Enrollment Type.
Looking across these filters, we see metrics like age group, race/ethnicity, gender, and GPA range.
We call these “global filters” because they are available for every PDP dashboard. If you would like to learn more about the impact of filters on dashboard visualizations, please watch the tutorial called “Dimensions and Filters”.
Below the global filters are the dashboard reports.
The top left quadrant is our “framing quadrant” which explains the key performance indicator that is being measured. Here, we see that the Credit Accumulation Rate dashboard measures the proportion of students who are making sufficient progress towards completion.
Above the framing question, you will find the dashboard dimensions and any filters unique to this dashboard. Dimensions can help identify achievement gaps between student populations by disaggregating one or more visualizations by the metric’s categories. If you would like to learn more about the impact of dimensions on dashboard visualizations, please watch the tutorial called “Dimensions and Filters”.
Next to the dimensions, are two filters that are unique to the Credit Accumulation Rate dashboard. Those are Credit Threshold and Academic Year. To learn more about these two filters, please review the tutorial called “Credit Accumulation Rate: Introduction and Basic Functionality”.
In short, the “Credit Threshold” filter sets a minimum number for credits that part-time and full-time students should earn in a year. The second filter, Academic Year, represents the number of years the student has been enrolled. Let’s leave those two filters set on their default values.
For most PDP dashboards, the top right quadrant is a line chart. Hovering over each data point, a tool tip appears which provides details about that data point.
This chart reports the credit accumulation rate for first-year student cohorts. Hovering over the 2018-19 data point, we find that 22.1% of first-year students who enrolled at the institution within that academic year met the credit threshold.
Looking at the prior first-year cohorts, we see that the credit accumulation rate increased between 2013-14 and 2016-17 then declined.
For most PDP dashboards, the lower left visualization is a bar chart. For this dashboard, the height of the bar represents the percentage of first-year students who did not meet the credit threshold.
Like the line chart, if we hover over a bar, a tool tip appears with details about that data point. For the 2018-19 academic year, we find that 77.9 of first-year students did not meet the credit threshold while 22.1% did.
In addition, there is a color overlay. The lighter color translates into cohorts where a higher number of first-year students met the credit threshold while darker bars represent cohorts where fewer students met the threshold.
For most PDP dashboards, the lower right chart offers an overall view of the dataset.
For this dashboard, the length of the blue sections represents the number of first-year students and the percentage of that population who met the credit threshold. The gray bar shows the percentage of first-year students who did not meet the credit threshold.
For 2018-19, we see the same values indicated in the other two charts: 22.1% of first-year students met the credit threshold while 77.9% did not.
Continuing our exploration of the dashboard functionality, we find a breadcrumb below the visualizations which lists the filters that have been applied to the dashboard. This is a useful reminder if you export this dashboard to send to a colleague.
Now, let’s go back to the top of the screen. Here we find three filters: Organizational Grouping, Institution Type, and Select an Institution.
If you are at an individual institution, then these filters won’t be interesting. However, if you are with an organization that works with multiple institutions (for example, a state, system, multi-college district, or a national initiative or organization), then these will allow you to filter your member institutions.
For example, if you are with a system office that includes 2-year and 4-year institutions, you can use the “Institution Type” filter to view the results of your 2-year institutions. Or you can use the “Select an Institution” to view the results of an individual institution.
Please know that the National Student Clearinghouse never shares your institution’s data with a third-party group without your permission.
Above those filters, we find a link called “Help”. Clicking on that link shows a pop-up screen with definitions of metrics used in this dashboard.
Another useful feature is “Download” which is at the top right corner. This allows you to download a copy of the dashboard to share with a colleague.
If you click that link, a pop-up screen appears with download options. We recommend you select either Image, PDF, or PowerPoint.
Now, let’s look at the very top of the screen. Every PDP dashboard has four tabs. The first tab is this dashboard.
The second tab is the Subgroup Gap Analysis which is an additional report available for every PDP dashboard. This report supports our exploration into achievement or equity gaps that may exist between two student populations.
To learn more about this report, please watch the tutorial called “Subgroup Gap Analysis”.
The third tab is called the “Credit Accumulation Rate Details”. This report provides a tabulation of the dashboard data.
Notice that the same list of global filters exist which allows you to select a specific student population. For example, let’s filter this report to first-time, full-time students. To do that, click on Enrollment Type, deselect “All”, click on “First-Time”, then click “Apply”. Then, click on Attendance, deselect “All”, click on “Full-time”, then click “Apply”.
This is the tabulated data showing the Credit Accumulation Rate for first-time, full-time students. These data can be downloaded or copied into an Excel sheet.
The fourth tab is called the “Dashboard Guide” which provides information about the dashboard like its purpose, methodology, and how to interpret the visualizations.
Now, click on the first tab to return to the dashboard. Notice that the two filters that we set earlier in the Details tab carried over to this dashboard. This dashboard shows the results for first-time full-time students.
Let’s use this dashboard to find the percentage of those students who met the credit threshold.
Hovering over the 2018-19 data point in the line chart, we find that 13.4% of full-time first-time students who entered during the 2018-19 academic year met the credit threshold.
Now, let’s determine if there is an achievement gap between first-time, full-time students with a Pell Grant compared to those who do not have a Pell Grant.
To compare student populations, we need to add a dimension. Clicking on the Dimension drop down, select “Pell Grant Recipient”.
The upper right chart now shows a line for each category of the Pell Grant Recipient metric. The blue line is the credit accumulation rate of first-time full-time students who do not have a Pell Grant, the yellow line represents first-time full-time students whose Pell Grant status is unknown, and a red line represents first-time full-time students who have a Pell Grant.
In the lower left chart, we find three sections for our first-time full-time students – one for students without a Pell Grant, one for students with a Pell Grant, and one for students whose Pell Grant status is unknown.
The lower right chart is not impacted by dimensions.
Let’s remove the data for those students whose Pell Grant status is unknown. To do that, click on the “Pell Grant Recipient” global filter, deselect “Unknown”, and click “Apply”.
The line chart now shows the credit accumulation rates for first-time full-time students with, and without, a Pell Grant.
Notice that there is a separation between the two lines. This separation indicates an achievement gap between these two student populations. Hovering over the 2018-19 data point, we find that 21.3% of first-time full-time students without a Pell Grant met the credit threshold while 10.5% of first-time full-time students with a Pell Grant met the threshold. This is an achievement gap of nearly 11 percentage points.
Understanding which student populations have lower levels of achievement gives us information to target interventions to those students. In this case, we could send additional emails or text messages to our first-time full-time students with Pell Grants regarding the importance of accumulating sufficient credit for an on-time completion and offering support to help them accomplish that.
There is one last thing I want to show you. To do that, let’s add two additional filters.
First, let’s click on the “Race/Ethnicity” filter, deselect “All”, click on “American Indian or Alaska Native”, then click “Apply”.
Next, let’s click on “First Generation”, deselect “All”, click on “First Generation”, then click “Apply”.
The data have been filtered to first-time, full-time, first-generation, American Indian or Alaska Native students. Looking at our dashboard, we see that all visualizations are missing. With each filter that we added, we reduced the number of students who met those criteria. The PDP dashboards suppress visualizations if the number of students is 10 or fewer. This protects student anonymity.
This completes our tutorial on the basic functionality of the Postsecondary Data Partnership dashboards. Thank you joining us.