Advising at Scale: Student Advising Analytics That Works

When your LMS lights up with risk flags, the hard part isn’t more data—it’s getting the right human to the right student at the right moment. Advising at scale means converting mastery gaps, inactivity streaks, and missed work into early intervention, tight case management, and outcome-focused success coaching. Use this blueprint to stand up a defensible system that moves needles on persistence and retention improvement—this term.

If stakeholders need a quick primer to align on concepts, share explainers on adaptive learning, personalized learning paths, and AI in education to anchor your signal definitions and interventions.

The advising analytics stack (what actually needs to exist)

• Ingest layer. LMS events, grades, attendance, tutoring, and registrar/bursar holds.
• Feature store. Weekly features: inactivity streak, missed-work count, mastery gap by outcome, grade volatility, help-seeking.
• Decision layer. Deterministic triggers + an interpretable risk score.
• Orchestration. Case engine that assigns owners, enforces SLAs, and manages cool-downs.
• Advisor console. Triage queue with root-cause signals and recommended next actions.
• Evidence trail. Immutable log of alerts, outreach, and outcomes—so you can prove impact.

Signals that predict action (not just risk)

Engineers and advisors should agree on signal definitions and lead time (how early the signal fires before a course risk materializes). Start with a small, high-precision library:

Publish a one-page data dictionary so faculty and advisors share the same language.

From signals to cases: triage that respects human capacity

Alerts should open cases with owners, SLAs, and next steps. Use a priority score that blends risk with how recoverable the situation is:

priority = 0.6 * risk_score + 0.3 * recoverability + 0.1 * equity_boost

• Risk score: interpretable model or weighted rules (e.g., low mastery + inactivity).
• Recoverability: higher early in term with small mastery gaps.
• Equity boost: gentle nudge for populations your policy prioritizes.

Capacity math (so queues don’t explode).
If you expect 300 cases/week and average handling time is 12 minutes, total effort = 60 hours. With 6 advisors at 10 hours/week each, capacity = 60 hours—your ceiling. If forecasted cases exceed capacity, raise thresholds, automate first-touch nudges, or add hours before you launch.

SLAs you can hit.

• Priority 1: contact within 24 hours, resolution attempt within 72 hours
• Priority 2: contact within 48 hours
• Priority 3: contact within 5 days (nudges + self-serve first)

Early intervention playbooks your advisors will actually use

Each trigger maps to a 3–4 step playbook with scripts, escalation, and a close-the-loop step.

Playbook: Inactivity + missing work

• T0 (SMS): “We missed you this week. Want a 10-min plan to restart. Reply 1 for deadlines, 2 for study plan, 3 to book a quick call.”
• T+24h (email): Two time slots + checklist link; include one relevant recap clip.
• T+72h: Phone call; if still no response, log outcome and notify instructor.
• Close loop: Assign a micro-review targeting the missed concept; schedule a 7-day check.

Playbook: Low mastery on core outcome

• T0 (email): Two-sentence diagnosis + 15-minute targeted practice set.
• T+24h (SMS): “Can I send a two-step exercise for [Outcome X]. It takes ~10 min.”
• T+72h: Book a short success coaching slot to plan next steps.
• Close loop: Re-check mastery in 7 days; if still low, escalate to tutoring.

Playbook: Admin hold + good coursework

• T0 (SMS): “You’re on track academically; a registrar/billing hold may block enrollment. Want help clearing it today.”
• T+24h: Warm handoff to registrar/bursar, confirm resolution timeline.
• Close loop: Confirm hold removal; send “you’re clear” message.

Keep scripts short, supportive, and action-oriented—every message should ask for one small next step.

Case management that scales success coaching

Move beyond “send a message” into success coaching with clear stages:

• Intake: Structured note with top three drivers (e.g., mastery gap, work hours, device access).
• Plan: One measurable goal for the next 7 days (submit X, pass Y outcome).
• Supports: Link to tutoring, office hours, or a personalized learning path segment.
• Follow-up: Automated reminder + advisor check at 72 hours.
• Closure: Mark as Resolved (goal met) or Reassign (needs specialist).
• Quality check: 10% of cases reviewed weekly for note quality and adherence to playbook.

Status codes to standardize reporting: New → In Progress → Waiting on Student → Resolved → Escalated.

Dashboards that prove impact (at a glance)

• Queue health: open cases, median/p90 time-to-first-contact, SLA breaches
• Behavior change: 7-day engagement recovery; overdue → submitted within 72h
• Mastery movement: Developing → Proficient transitions by outcome
• Retention signals: withdrawals vs prior terms, flagged vs matched controls
• Equity panel: effect by year level, program, modality (in-person/online)
• Advisor workload: cases closed per FTE, reassign rates, coaching minutes

Use one shared dashboard so CTL, registrars, deans, and advising leads argue about priorities—not data.

Quick privacy & trust notes

Publish a plain-language page that explains what you collect, why you collect it, and how students can ask questions or opt out of nonessential nudges. Limit visibility of sensitive indicators to the smallest necessary roles, and log who accesses what.

Where PathBuilder fits

PathBuilder turns signals into action without adding busywork.

• Targeted practice on demand. When a case opens for low mastery, assign outcome-aligned practice from adaptive learning—then verify completion.
• Playbooks in the flow. Advisors send short, rubric-aligned prompts and track outcomes from the same view.
• Evidence, not anecdotes. Mastery transitions, engagement recovery, and case outcomes roll into program dashboards for real retention improvement.
• Faculty-ready rollout. Share AI in education to align staff on formative use, then request a structured walkthrough from About PathBuilder to map signals, SLAs, and playbooks to your stack.

Advising at scale is not more dashboards, it is faster help for real students

When signals, SLAs, playbooks, and capacity planning work as one system, advisors spend less time sorting alerts and more time coaching. Start small, prove lift, then expand with guardrails for quality and equity.

Next 30 days

• Select two high-enrollment courses and lock the signal definitions, triage rules, and SLAs.
• Launch two playbooks, one for inactivity plus missing work, one for low mastery on a core outcome.
• Stand up a shared dashboard with three KPIs: time to first contact, seven-day engagement recovery, Developing to Proficient transitions.
• Publish a short student transparency note and a privacy FAQ.
• Run a weekly review to tune thresholds and workload.

When the basics are reliable, scale to more sections, add success coaching capacity, and keep the evidence trail clean so everyone can see the wins. If you want a working demo that connects your signals to triage and targeted practice, book a structured walkthrough on the About PathBuilder page.

Data schema & SLA cheat sheet

Tables: enrollments, events, submissions, mastery_updates, holds, cases, outreach_logs.
Core fields: student_id, section_id, outcome_id, risk_score, priority, sla_due_ts, case_status.
SLAs: P1 = 24h contact; P2 = 48h; P3 = 5 days. Cool-down after reply: 72h. Case re-open if no activity in 7 days.