After you finalize a model, DataRobot makes it easy to deploy it into your desired decision environment. Decision environments are the methods by which predictions will ultimately be used for decision-making.
Automation | Augmentation | Blend
Individual components of this process may be automatable. For example, perhaps students that are unlikely to pass at least two courses are automatically referred to the academic support office so that academic coaches can proactively check in. More commonly, though, this kind of model is a decision-support tool for academic advisors and teaching staff.
Because the end consumers are student-facing staff, they should find predictions and visualizations understandable and consumable. This could mean Power BI, Tableau, or an internal database maintained by the university are used for providing model results to the staff.
Scores should be updated as frequently as the underlying features. If it is possible to include grades, attendance, staff notes, or other relevant features that are collected throughout the term, then the scoring could be done on a daily or weekly basis to provide the most useful information.
- Decision Executors: Student support staff (coaches, academic advisors, tutors, TAs) are the most direct consumers and will use the predictions on a daily or weekly basis.
- Decision Managers: The Academic Affairs Office (or equivalent) is ultimately responsible for making sure that students have what they need to be successful.
- Decision Authors: Data scientists, analysts, and statistical modeling practitioners are all well-positioned to build the models in DataRobot. IT Support or vendors can be brought in if there are specific deployment needs (e.g., Tableau integration).
- Identify students that would benefit from tutoring, coaching or other academic support before midterm projects and grades are determined
- Guide faculty and teaching assistants with insights about the drivers of success for their specific courses (e.g., attendance, previous lab experience, completing a certain prerequisite class)
Models should be retrained when data drift tracking shows significant deviations between the scoring and training data. If student enrollment changes dramatically (for instance, a shift towards online learning and more students living at home rather than on-campus), then the models should be reevaluated for accuracy.
Universities should think carefully about what actions can and should be taken in response to these predictions. This use case is positioned around proactively offering students additional support given their academic load. In that context, we might be willing to use demographic features (ethnicity, scholarship recipient status) to provide additional support to historically underserved groups.
Theoretically, the model could also be used as a “What If?” scenario planning tool to help set students up for success within a given semester. In which case, incorporating information about gender, race, first-generation college status, or scholarship status could systematically steer those students away from challenging classes or ambitious course loads. If this is the case, those features should be excluded from the model. More broadly, it is essential that student-facing staff using the models understand the limitations and appropriate uses of the models with which they interact.
Experience the DataRobot AI Platform
Less Friction, More AI. Get Started Today With a Free 30-Day Trial.
Sign Up for Free