Integrate SageStepper with Your LMS and SIS: A 30–60–90 Day Playbook

Why this integration matters now

Innovation without accountability doesn’t last a budget cycle. Integrating SageStepper with your LMS and SIS lets you tie daily interview practice to outcomes your board cares about: placement, equity, time-to-offer, and mentor efficiency.

• Readiness, visible in the LMS: Nova AI interviewer sessions write back to your LMS gradebook and SageStepper progress radar, so students see progress where they already work.
• Evidence for accreditation: Map interview competencies to program learning outcomes; export artifacts and analytics for site visits.
• Capacity gains: Teams report saving 8–15 mentor hours/week by shifting early practice to SageStepper and using action boards for targeted 1:1s.
• Results that move the needle: Institutions see +22–35% readiness score lift in 6–8 weeks and +6–11 point offer-rate deltas term over term.

When practice is integrated into the LMS and rostered from the SIS, adoption jumps above 80% and stays there. Visibility drives habit, habit drives outcomes.

Stakeholder map and decision rights

You’ll move fastest when roles are explicit. Use this lightweight RACI to clarify who decides what and when.

stakeholders:
  executive_sponsor:
    role: Dean/Provost/Director
    responsibilities:
      - Approves scope, timeline, success metrics
      - Unblocks policy decisions
    cadence: Bi-weekly 20-min briefing
  it_security:
    role: CIO delegate / Security analyst
    responsibilities:
      - Reviews SSO/SCIM, LTI, data retention, SOC 2
      - Approves firewall/SFTP rules
    cadence: Two 60-min reviews (week 1–2)
  lms_admin:
    role: LMS platform owner (Canvas/Blackboard/D2L/Moodle)
    responsibilities:
      - Installs LTI 1.3, configures developer keys, tests grade passback
    cadence: One 90-min working session + 2 async tests
  sis_registrar:
    role: Registrar / SIS analyst (Banner/PeopleSoft/Workday/Colleague)
    responsibilities:
      - Exports rosters, attributes (program/cohort/term), and schedules nightly sync
    cadence: One 60-min mapping + one 45-min SFTP/API test
  career_services:
    role: Program lead / Career outcomes
    responsibilities:
      - Defines Interview Sprint rituals, selects rubrics, monitors readiness
    cadence: Weekly 30-min standup
  faculty_champions:
    role: 2–4 instructors across pilot programs
    responsibilities:
      - Embed activities in LMS, model usage, provide feedback
    cadence: 20-min weekly check-in during pilot
  institutional_research:
    role: IR analyst
    responsibilities:
      - Validates metrics, builds executive dashboard, publishes term report
    cadence: Bi-weekly sync

Decision principles:

• Security first: No student data moves until SSO and LTI keys pass review.
• Pilot fast, scale safely: Two programs, two weeks, then scale via templated LMS shells.
• Measure what matters: Define readiness target, adoption threshold, and placement delta up front.

30–60–90 day timeline and milestones

Move from sandbox to scale without disrupting a term. Here’s a proven cadence.

timeline:
  days_0_30:
    goals:
      - Complete SSO (Okta/AzureAD) and LTI 1.3 install in LMS sandbox
      - Map SIS fields and test nightly roster file
    milestones:
      - Security review complete (day 10)
      - LTI deep link + grade return verified (day 18)
      - Pilot courses created from template (day 25)
  days_31_60:
    goals:
      - Run 2-week Interview Sprint in 2–3 programs
      - Enable progress radar and action boards; collect faculty feedback
    milestones:
      - 75%+ student activation by day 45
      - Readiness +15% lift by day 60
      - IR validates export schema (day 58)
  days_61_90:
    goals:
      - Scale to additional sections; turn on automated exports to IR data lake
      - Publish exec dashboard and term-end report
    milestones:
      - 85%+ LMS adoption across sections
      - Offer-rate delta +5 pts vs. prior term
      - Mentor hours saved ≥8/wk

Practical weekly rhythm:

1. Week 1: Security + SSO review; create LMS developer key; confirm FERPA scope.
2. Week 2: Install LTI 1.3, test deep links and grade return; import sandbox roster.
3. Week 3: Faculty champions co-design activities; publish pilot shells.
4. Weeks 4–5: Launch Interview Sprint; track streaks, readiness, and feedback scores.
5. Weeks 6–8: Expand to more sections; automate SIS sync and IR exports; brief leadership.

Data model and technical patterns that just work

Integrate with the systems you already trust. SageStepper supports LTI 1.3, SSO, and roster sync via SFTP or REST.

• Identity & access: SSO via Okta/AzureAD; optional SCIM for lifecycle management.
• LMS: LTI 1.3 launch, deep linking, and names/roles. Grade passback writes readiness or assignment scores to the LMS gradebook.
• SIS: Nightly SFTP CSV or API to sync rosters and attributes. Minimal fields required: student_id, email, first_name, last_name, program, cohort, term.
• Privacy: FERPA-aligned, SOC 2 Type II. Only practice artifacts and analytics needed for learning outcomes are stored.

Sample SIS-to-SageStepper mapping (TypeScript):

// Map SIS CSV row to SageStepper roster payload
interface SisRow { student_id: string; email: string; first_name: string; last_name: string; program: string; cohort: string; term: string; }
interface RosterUser { externalId: string; email: string; name: { first: string; last: string }; groups: { program: string; cohort: string; term: string }; }

export function mapSisRow(row: SisRow): RosterUser {
  return {
    externalId: row.student_id,
    email: row.email.toLowerCase(),
    name: { first: row.first_name.trim(), last: row.last_name.trim() },
    groups: { program: row.program, cohort: row.cohort, term: row.term }
  };
}

Nightly roster job (bash + SFTP):

#!/usr/bin/env bash
set -euo pipefail
STAMP=$(date +%F)
psql "$SIS_DSN" -c "COPY (SELECT student_id,email,first_name,last_name,program,cohort,term FROM roster WHERE active=true) TO STDOUT WITH CSV HEADER" \
  > /tmp/sagestepper_roster_${STAMP}.csv
sftp -i ~/.ssh/sage_roster_key -o StrictHostKeyChecking=yes $SFTP_USER@$SFTP_HOST <<EOF
put /tmp/sagestepper_roster_${STAMP}.csv inbound/roster_${STAMP}.csv
bye
EOF

Minimal LTI 1.3 settings to verify with your LMS admin:

lti:
  initiate_login_url: https://app.sagestepper.com/lti/login
  redirect_uris:
    - https://app.sagestepper.com/lti/launch
  public_jwks_url: https://app.sagestepper.com/.well-known/jwks.json
  required_scopes:
    - ags:score:readonly
    - ags:score:writeonly
    - names.roles.readonly
  deep_linking: enabled
  custom_claims:
    - context.label -> course_code
    - lis.person_sourcedid -> student_id

Example event you can forward to your data lake:

{
  "event": "nova_session.completed",
  "student_id": "A1234567",
  "course": "BUS-301",
  "readiness_delta": 7,
  "streak_days": 5,
  "rubric_scores": { "behavioral": 4, "technical": 3, "communication": 4 },
  "timestamp": "2025-02-03T15:04:05Z"
}

Rituals that drive adoption (inside your LMS)

Tools don’t change outcomes—rituals do. Embed these in LMS shells and syllabi so practice becomes non-negotiable.

• Interview Sprint (2 weeks): Students complete 5 Nova sessions/week. Use LMS modules titled “Interview Sprint” with deep links to SageStepper. Target: ≥80% completion, streak ≥4 days.
• Progress radar checkpoints: Add a weekly assignment—“Show your radar + one area you’ll improve.” This reflects in the LMS gradebook via LTI.
• Action boards for mentors: Career staff filter by “low-confidence on technical Qs” and offer targeted 10-minute micro-coaching instead of 30-minute generic sessions.
• Rubric-aligned practice: Map your program outcomes to SageStepper rubrics; require one behavioral and one technical mock each week.
• Peer skill circles: Within SageStepper, create small groups for feedback swaps. Prompt with “Give two specific examples and one growth move.”

Template module structure:

lms_module:
  title: Week 4 — Interview Sprint
  items:
    - type: external_tool
      title: Nova Mock 1 (Behavioral)
      link: deep_link_to_sagestepper
      points: 5
    - type: external_tool
      title: Nova Mock 2 (Technical)
      link: deep_link_to_sagestepper
      points: 5
    - type: assignment
      title: Progress Radar Reflection
      points: 5
      instructions: "Paste your radar, name one spike and one gap."

Measuring impact and reporting to leadership

Report on student growth and capacity gains, not just logins.

Core metrics to publish each Friday:

• Readiness score lift: Average change from baseline; target +20% by week 6.
• Adoption: % of enrolled students with ≥3 Nova sessions/week; target ≥75% during Sprint.
• Mentor hours saved: Total mock minutes shifted to Nova vs. live; target 8–15 hrs/week.
• Offer rate delta: Pilot vs. prior term; target +6–11 points.
• Equity lens: Disaggregate by cohort; close readiness gaps by ≥25%.

IR-friendly KPI pack:

kpis:
  readiness_lift: avg(current_readiness - baseline_readiness)
  adoption_rate: students_with_3plus_sessions / enrolled
  mentor_hours_saved: (nova_minutes - baseline_nova_minutes) / 60
  offer_rate_delta: current_term_offers/enrolled - prior_term_offers/enrolled
  equity_gap_reduction: (gap_prior - gap_current) / gap_prior
exports:
  weekly_csv: s3://institution-ir/sagestepper/weekly_metrics/*.csv
  dashboard_bi: Tableau workbook "Career Readiness — SageStepper"

Sample exec briefing script:

This week, Interview Sprint hit 81% adoption across 3 programs. Avg readiness lifted +9 points; mentor load down 11 hours. Two cohorts closed prior equity gaps by 28%. Scaling to five additional sections next week; no security exceptions noted.

Case snapshots you can replicate

• Urban Business School (480 juniors)
  • Setup: SSO + LTI in 3 weeks; SIS SFTP nightly; LMS templates across 18 sections.
  • Rituals: 2-week Sprint mid-term; progress radar reflections weekly.
  • Results: +31% readiness lift, +8.4 pt offer delta, 12 mentor hours/week saved; 87% LMS adoption.
• Regional Community College (IT pathways, 220 learners)
  • Setup: Canvas + Banner; grade return of “Interview Lab” assignments.
  • Results: 76% Sprint completion, streak median 5 days, employer panel noted stronger STAR responses; summer placements up 7 pts.
• Data Science Bootcamp (120 fellows)
  • Setup: SCIM provisioning; action boards for TA triage; Nova technical Q bank tuned to Python/SQL.
  • Results: +27% readiness, technical rubric +0.8 avg gain, TA time per fellow down from 45 to 28 minutes/week.

What made these work:

• Executive sponsor cleared data sharing quickly.
• Faculty champions modeled usage and embedded deep links in week-one modules.
• IR validated metrics and told the story with a simple weekly chart.

Risks, compliance, and how to de-risk rollout

• Data minimization: Share only the fields needed; no grades pulled from SIS.
• Access controls: Use SSO and limit admin roles; enable audit logs.
• Pilot guardrails: Start with non-high-stakes assignments; move to graded once stable.
• Fallback plan: Keep manual CSV upload in the first week while SFTP stabilizes.
• Accessibility: Confirm WCAG 2.1 AA; provide transcripts for Nova sessions.

If a risk stalls you for more than a week, narrow scope (one program, one section) and proceed—momentum matters.