What is ELSA and why should I use it?
Learn about how ELSA detects issues in your experience and provides you with one-click, impactful solutions to keep participants on track
What is ELSA?
ELSA is a tool built into your dashboard that detects issues occurring in your learner experience and helps you resolve these issues quickly and easily. In a couple of clicks, the program manager can send impactful interventions to keep their participants on track and ensure positive experiences.
So how does it work?
ELSA uses AI and key information about your experience, such as due dates, to detect issues most important to great experiential learning, for example, team dissonance, overdue submissions, or an unassigned review. ELSA will then display these issues as an actionable to-do list on your dashboard and so allow you to catch and rectify issues before they negatively impact participants’ experiences.
Each automated to-do list item has a suggested action that should be taken to resolve the issue. ELSA will provide you with message templates based on best-practices from thousands of previous interventions.
Once an intervention message has been sent, it will automatically be logged and tracked by the system, so that you can see whether or not a particular participant or mentor has actioned on your intervention.
Wow, pretty cool, huh!
Types of issues ELSA will raise
Below are some of the types of issues (and associated intervention tasks) you can expect to see on ELSA. Some are drawn from AI and some from key information or statistics relevant to your program that you have provided experience.
Based on statistical analysis:
- Team dissonance (Detecting dissonance between the students answering the Pulse Check question “Do you feel your team is on track”)
- Missing teams allocations (Detecting participants without a team in an experience that has at least one team)
- Submission overdue (Detecting an overdue submission)
- Submission unassigned (Detecting a feedback cycle that is stuck because a submission hasn’t been assigned to a reviewer yet)
- Reviewer reminder (Detecting a feedback cycle that is stuck because an assigned reviewer hasn’t done their review yet)
- Feedback unpublished (Detecting a feedback cycle that is stuck because the feedback hasn’t been published to the student or team yet)
Based on artificial intelligence and machine learning:
- Negative sentiment (Detecting negative sentiment in any part of a mentor, student or team submission)
- Team disengaged (coming soon)
- Team off track (coming soon)
So, why should I use it?
If you’re not already convinced, here are our top reasons to use ELSA as part of your program management.
- Forget surveys, phone calls, emails! Spend your time actioning and tracking interventions rather than seeking out the issues.
- Find out about issues in “real time” and before they become far bigger issues!
- Focus on the issues that truly matter to excellent experiential learning, and that would otherwise be very hard to detect
- It’s quick, easy and effective, thanks to ready-made message templates based on best practice
Conclusion
So, you now understand what ELSA is and the types of information and intervention tasks it can offer you. Next steps, we recommend you learn how to:
