Identifying individual students who are at-risk of dropping out is often a subjective exercise in higher education. Additionally, for each student the institution intervenes with, we must answer the question of why are we intervening? And what resources will we provide this student?
SightLine's lead data scientist will be sharing best practices on how to use data to identify individual at-risk students and then create segments within this at-risk population for tailored interventions. With this data-based, semi-automated approach, higher-ed leaders can spend more time focusing on supporting at-risk students rather than sifting through spread sheets identifying risk factor, making lists of which students to target and negotiating resource allocation.
- Identifying data needs for at-risk student analysis,
- Analytical methods towards identifying at-risk student populations with 1-2 semester lead time,
- Data based segmentation of at-risk students with common behaviors,
- Intervention methodologies, and communication strategies between students and key project stakeholders.
Who should attend?
New, mid-level, and senior professionals in student affairs whose focus is related to strategic intervention planning for student success and retention.
Click HERE for the on-demand webinar link.