Skills Analysis 

The Risk Score Analysis shows the risk areas and how the student compares to students who successfully completed the major. Looking at risk area performance will also indicate why the student is or is not at high risk of not completing the major. The first skill area usually rises to the top because the student’s performance has a high importance to major completion and the student’s comparative performance is below average compared to the historical norm. In addition, the risk score analysis will make suggestions about what the student can do to improve chances of succeeding in each risk area. 

2.5 Risk Model: Purpose 

The risk model is used to help prioritize advisor and staff interaction with students based on their likelihood to graduate and support advisors in quickly identify at risk students The model estimates the likelihood that current students will graduate by comparing them to historical students at your institution with similar academic and demographic characteristics.

Creation of the Predictive Model Using Historical Data 

Our data scientists use the academic and demographic data of your historical students to create a predictive model specific to your campus. Then current students are ‘scored’ or applied against your campus’s predictive model on a scale from 0 - unlikely to graduate - to 1 - most likely to graduate. We work with your institutional leadership team to translate ranges of those scores to red, yellow, and green colors. As our Data Science team creates a model, they test many different academic and demographic data factors for their predictive quality. Some of these data points are available directly in your SIS, others are derived by our Data Science team using proprietary methods. The data points used in your model include courses student’s took, grades earned, majors declared, the way they earned credit over time, in addition to more static facts, like whether they were a transfer student or their high school GPA. These data points have different weight in the model, and the weight vary over the progress of the student through their degree. 

Skills Scores 

Our analytic research and work with member information has shown that students tend to receive similar grades in particular groups of courses, and we call the reasons behind these grade patterns "skills". Individual courses can be associated with multiple skills, and courses most associated with a particular skill won't necessarily belong to the same or similar academic discipline. Skills provide the predictive model information about students' aggregate coursework not available through individual course grades or GPAs. The risk model analysis displays an individual current student's estimated skills as a percentile compared to all students who graduated in that student’s current major(s). We have created the following lists of courses most associated with each skill to help us identify names for the skills, as well as optional descriptions, for display in the platform. 

Model Quality

The data science team looks at both the accuracy and calibration of your institutional model by comparing your customized model to a baseline model. The baseline model is based purely on accumulated credits and cumulative GPA. Your institutional model adds more data points, sophistication and nuance of how it evaluates those data points, and therefore performs better than the baseline in terms of two metrics that we use to evaluate model quality: Kappa and AUC. We also look at both accuracy and calibration. Your institutional model is more predictive because there is a bigger spread between the predicted grad rates between the bottom and top quartile (looking from top to bottom), and it is better calibrated because there is less difference between the predicted and actual grad rate. 

Conclusion 

Our data science team is confident that this model will help your advisors accurately prioritize interaction with students based on their likelihood to graduate and identify students who are at risk.

The last column of the Classes This Term grid will allow the advisor several actions per course. The options available to you are dependent on the permissions applied to you.

Course Assignments -- Depending on your advisor permissions, you may be able to click on the Course Assignments link to view all of the assignments that have been created (most likely by the professor) for that course. Again, depending on your permissions, you may be able to create new assignments.

Request Tutor Appt -- If you see this option, you can request for a tutor to schedule a tutor appointment with the student for that specific course. For example, when I click on the Request Tutor Appt link, the system displays the "Send Tutor Request" dialog.

The first field in this dialog is a command box where a user should enter information about when the student is able to meet with a tutor. Be as specific as possible when commenting on when the student would like to meet with the tutor. This information will make it easier to match the request with a tutor. Next, select a preferred location for this request. When all the information has been entered, you can preview the request before sending. Click Save when done. The system will then send an email notification to the Tutor Coordinator for that location.

Schedule Tutor Appointment -- Rather than simply request a tutor appointment, if you have this permission, you can bypass requesting and actually create a tutor appointment. When you click this link, the system will display the Schedule an Appointment page so that you can create a tutor appointment for your student.

Send Message to Professor -- You can check the box beside one or more professors and click the Send Message to Professor in the Actions Menu to quickly communicate with the student's professors.