Data-driven research, practitioner guides, and institutional strategies for improving student retention and success in higher education.
A deep dive into how machine learning models generate risk scores, the four-layer analytics architecture, and what institutions need to succeed with AI-driven retention.
Research-backed analysis of the LMS, financial, and academic signals that consistently precede student withdrawal — and how to detect them weeks earlier.
How behavioral patterns in your LMS — login frequency, submission consistency, session depth — are among the most predictive signals for student persistence.
Which retention strategies actually work? A research-backed comparison of proactive advising, gateway course interventions, financial support, and more.
How institutions can move from passive engagement measurement to active intervention — and why the timing of outreach matters as much as its content.
A comprehensive overview of evidence-based tactics institutions are deploying right now to improve first-year, transfer, and online student persistence.
Transfer students face unique barriers to persistence. This article outlines the specific risk signals, institutional gaps, and targeted interventions that drive transfer retention.
How institutions are using behavioral data and AI-prioritized intervention queues to transform navigator workflows and dramatically improve outreach effectiveness.
Online students withdraw at rates 10–15% higher than on-campus peers. This article examines why — and what data-driven institutions are doing about it.
Financial disruption is one of the top three causes of student withdrawal. Here's how retention platforms connect financial aid signals to navigator workflows.
A practitioner's guide to the earliest detectable signals of student disengagement — and the intervention windows that determine whether outreach succeeds.
Boom AI puts these insights into action — identifying at-risk students automatically and routing them to navigators before it's too late.
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