Complete documentation in the Campus Community at the link below (you will need to log in to the Campus Community first … here is how).

Campus Early Warning

How to Access

Campus Early Warning uses powerful statistical algorithms to measure how attendance, behavior, academics, home and school stability interact to predict graduation. GRAD Scores help Student Services respond with interventions targeted at students who would benefit the most. Privacy is our highest priority, so the system saves risk correlations that cannot be connected back to individual past students.

Overview video:

What is Early Warning?

Early Warning is a tool that automatically discovers statistical relationships between educational records and enrollment outcomes. Early Warning uses these evidence-based risk factors to estimate each student's likelihood of a positive enrollment outcome (matriculation or graduation) versus a negative enrollment outcome, such as dropping out of school. See the full documentation below.

Early Warning provides school administrators, counselors, principals, etc., with a dashboard to quickly see a student's persistence toward graduation (successful completion of high school) and the likelihood of being promoted to the next grade level. School personnel can compare one student's likelihood to their peers and more effectively intervene on behalf of a student to increase the probability of promotion. Early Warning can be used with students in grades K-12.

Early Warning data is displayed for the current school year only, as determined by the year marked Active in the School Years editor. That year must be selected in the Campus toolbar. When a future or past school year is selected, a message indicates data cannot be displayed.

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The following videos provide additional information on Early Warning:

• Understand Campus Early Warning

• Understand GRAD Scores

• Analyze Student Data Points Using Early Warning
  
  

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The predictive analysis that Campus employs to generate a student's GRAD score is ever-evolving and constantly updated as new data is entered into Campus. A student's GRAD score includes (but is not limited to) the following categories of data:

Early Warning learns statistical relationships between educational records and enrollment outcomes. Machine learning uses computers to find evidence-based predictors of student outcomes using a large amount of anonymous data. To estimate a student’s likelihood of a particular enrollment outcome – such as dropping out of school – the computer system compares her record to past records of similar students.

The advantages of a machine learning system for Early Warning include:

• The machine-learned statistical model has better predictive power than heuristics published in dropout prevention literature.

• The student information system can detect changes in educational data faster than staff members can and update GRAD scores accordingly.

Early Warning Definitions and Calculations

Historical data for all students is used to calculate the scores. The application of that score to different categories and different types of scores varies. The following table lists the definitions and calculations of the terms used in Early Warning.

Score Distribution

The Score Distribution displays the selected school's score distribution for all students on a grid, with students on the left (Y-Axis) and scores across the bottom (X-Axis). The student distribution changes based on the entered tier definitions, selected schools, selected grade levels, and selected Score types.

Score Types

The score types include. but are not limited to the following definitions. Only one score type can be selected at a time. When a specific score type is selected, the list of students refreshes and displays results and color-coding based on that selected type.

Changing the values for the score, student count, percentage, and risk levels changes the list of students.

Tier Definitions

Enter the desired values for Score, Student Count, and Percentile for each High, Medium, and Low threshold. You can modify the percentile values by typing in the fields or using the up/down arrows. The tier settings return to the default values when the tool is refreshed.

• A value closer to 50 indicates the student is at a higher risk for non-promotion and is displayed in red.

• A value closer to 150 is at a lower risk of non-promotion and is displayed in green.

• A value that falls between 50 and 150 tier definition boundary lines indicates the student is at medium risk for non-promotion and is displayed in yellow.

Or, use the default entries, which are a maximum percentile of 5% of the student population (high, more risk), 21% of the student population (medium, moderate risk), and 100% of the student population (low, less risk).

Intervention in some areas may benefit some students who have a high-risk score in a certain category, while other students may not benefit from such programs. Certain events may have caused a change in a student's score, and an intervention may not be the best way to affect change for that student (natural disasters, etc.).

When one score is changed for one of the tier definitions, every value adjusts accordingly. For example, when a counselor is viewing the 12th-grade students at the high school and changes the Score values from 50, 100, and 150 to 76, 110, and 150, the student count changes to match the entered Score values, as does the Percentile values.

If the entered values do not match the results, data is overridden to list the students that do match the entered values. For example, if ten students have a GRAD score of 50, and 20 students have a GRAD score of 55, and the Student Count is set at 15, the ten students with a GRAD score of 50 are listed.

Early Warning Student Detail

For more information on the values returned for each student, select that student from the list by clicking anywhere in any of the columns (except the Watchlist). This displays basic student information (student number, birth date, GRAD Score, etc.), a GRAD Score History Graph, and separate cards that provide details for the category scores.

GRAD Score History

The GRAD Score History graph plots the student's GRAD Score and Category scores for a selected range—annually, last 12 months, last 30 days, and last 7 days. Users can hover over a plot point in the graph to see the student's score for that score type. Score types can also be removed from the graph by clicking on the legend on the right.

Monthly and daily history begins calculating when the Early Warning tool is available for a district.

The Range is set to Annually in the example below because the highest degree of change is visible.

The example below sets the Range to the Last 12 Months. Notice there is a more significant dip in the Attendance and GRAD Score from August to September, and a larger change in the student's Behavior score. But, since the student's Stability is increasing slightly, that item has been removed from the graph (in the legend, that category is grayed out). Also, Show Category Impact Weighting has been turned on, changing the size of the plot points relative to the Category Impact Score at that time. 

Click on any data point for any of the selected graph plots to display a list of events that may have contributed to a change in a student's score in the historical side panel.

The following example shows a change in the student's Curriculum score from 2016 to 2018 (127 in 2015 to 84 in 2018). By clicking on the data point for 2015, a Category Detail panel opens on the side that lists the student's curriculum information; clicking the data point for 2018 shows changes in the student's GPA, perhaps contributing to the student's overall Curriculum score.

Opportunity for Change Graph

The Opportunity for Change graph lists in bar chart format what category impacts the student's GRAD score most. The categories are represented by the ABCS (Attendance, Behavior, Curriculum, Stability) and are color-coded to represent the student's risk level. When hovering over the bar, the total number of points by which the GRAD Score may be affected, the risk level of the score (high, medium, low), and the complimenting category detail are highlighted. 

In the example below, the student's highest areas of opportunity are for Curriculum and Stability. Hovering over the stability bar shows the risk level, the point value and the Stability card is illuminated.