Integrate SageStepper with LMS and SIS: A 90‑Day Playbook for Accountable Innovation

Why integrate SageStepper with your LMS and SIS now

If practice isn’t in the LMS, it doesn’t happen at scale. Integrating SageStepper means students launch the Nova AI interviewer from Canvas, Blackboard, D2L Brightspace, or Moodle without extra logins, and advisors see progress in one place. The results are measurable:

• 20–30 point lift in average readiness score within 6 weeks when SageStepper is assigned weekly.
• 2–3x increase in practice streak length when progress is visible in the LMS gradebook.
• 10–15 mentor hours saved per week in programs over 300 students by automating first-pass feedback and triage through the progress radar.
• 10–20% offer-rate improvement in capstone cohorts that adopt Interview Sprints and action boards.

For provosts and deans balancing innovation with accountability, LMS/SIS integration turns “nice tool” into reportable outcomes aligned to accreditation, career readiness frameworks, and student success KPIs.

Links you may want handy: Product overview, Workflow templates, Pricing.

90‑day integration roadmap (three sprints)

Keep the scope tight, the cadence weekly, and decisions documented. Here’s a proven sequence across public, private, and bootcamp partners.

1. Days 0–14: Align and decide

   • Appoint a PM (0.2 FTE) and executive sponsor; confirm participating departments.
   • Choose methods: LTI 1.3 for LMS embed + grade passback; SCIM for roster or nightly SFTP; webhooks/xAPI for events.
   • Define minimal viable data: identity, enrollment, program/term in; readiness and completion out.
   • Approve privacy approach and retention window with legal/IR.

2. Days 15–45: Connect and configure

   • Create the SageStepper developer key/app in your LMS and test launch to Nova.
   • Set up roster sync (SCIM or SFTP) and verify new/withdrawn student flows.
   • Configure gradebook columns (readiness score, sprint completion) and rubric mapping.
   • Pilot two Interview Sprints in a test course; collect faculty and student feedback.

3. Days 46–90: Prove and scale

   • Run a 6–8 week pilot across 2–3 courses or one 200+ student cohort.
   • Weekly triage ritual: advisors use the progress radar to prioritize outreach.
   • Report at day 45 and 90 on readiness lift, streaks, offer signals, and mentor hours saved.
   • Decide scale-up, maintain, or iterate based on thresholds you set at kickoff.

Tip: Freeze scope after day 14. Add “nice-to-haves” to a backlog for phase 2.

Stakeholder map and decision rights

Avoid churn by clarifying who decides what. Use this lightweight RACI.

• Executive sponsor (Dean/Associate Provost): Accountable for goals; approves scale decision.
• Project manager (Student Success/Career Services): Runs timeline, stand-ups, and risk log.
• IT/LMS admin: Owns LTI 1.3, SSO, SCIM/SFTP, and security reviews; configures deployment.
• Registrar/SIS: Provides enrollment feeds and program/term mappings; validates FERPA boundaries.
• Career Services lead: Designs Interview Sprints, rubrics, and communication cadence.
• Faculty champions: Embed links, grade policy, and in-class rituals; give feedback weekly.
• Institutional Research/Assessment: Defines metrics and reporting cadence; audits validity.
• Accessibility/Disability Services: Reviews workflows and learner supports.
• Legal/Privacy: Approves data processing and retention policy.

Standing rituals

• 20‑minute weekly stand-up: blockers, decisions, and next tests.
• 30‑minute biweekly faculty feedback circle: friction logs and quick wins.
• Monthly executive 15‑minute readout: metrics vs. baseline, student stories, scale asks.

Data model: what flows where (and why)

Minimize data in, maximize value out. Here’s the standard map.

Data in from SIS/IdP (required)

• external_id (student ID that never changes)
• email (login and notifications)
• first_name, last_name
• program, term, cohort
• enrollments (course/section if you’re grading in LMS)

Data back to LMS/analytics (selected)

• readiness_score (0–100 scale; composite across question banks)
• interviews_completed (count per week)
• streak_days (consecutive days with practice)
• sprint_completion (boolean/percent per Interview Sprint)
• milestones (e.g., “Top 30% on behavioral”, “Technical round-ready”)

Sample gradebook mapping (two columns)

{
  "assignments": [
    {
      "name": "Interview Sprint Week 3",
      "points": 10,
      "rule": "Complete 3 Nova sessions (>=7 min each) + 1 reflection on action board",
      "lms_column": "Sprint W3 Completion"
    },
    {
      "name": "Readiness Index (Rolling)",
      "points": 100,
      "rule": "Average of last 5 Nova sessions across behavioral + role-specific banks",
      "lms_column": "Readiness Score"
    }
  ]
}

Data minimization and retention

• Collect only fields above; avoid GPA, sensitive demographics, or notes unless explicitly justified.
• Retain identifiable practice artifacts only as long as needed for learning analytics and accreditation.
• Provide opt-out where required; surface consent in the first LTI launch.

Integration methods and sample configs

LMS embed and grade passback (LTI 1.3)

• Deep-link Nova practice into modules; auto-provision accounts via OIDC.
• Use assignment-level launch so SageStepper knows context and can pass grades back.

Canvas example (developer key and deployment)

lti:
  version: "1.3"
  platform: "canvas"
  client_id: "<from SageStepper>"
  deployment_id: "<from Canvas>"
  issuer: "https://canvas.instructure.com"
  public_jwk_url: "https://app.sagestepper.com/.well-known/jwks.json"
  redirect_uris:
    - "https://app.sagestepper.com/lti/launch"
  placements:
    - "assignment_selection"
    - "link_selection"

Roster sync (pick one)

• SCIM for near real-time provisioning/deprovisioning.
• Nightly SFTP CSV for simple, robust batch sync.

SCIM-style mapping in TypeScript

type SisPerson = {
  sisId: string;
  email: string;
  firstName: string;
  lastName: string;
  program: string;
  term: string;
  cohort?: string;
};

type SageUser = {
  externalId: string;
  email: string;
  name: string;
  cohort: string;
  tags: string[];
};

function mapSisToSage(u: SisPerson): SageUser {
  return {
    externalId: u.sisId,
    email: u.email.toLowerCase(),
    name: `${u.firstName} ${u.lastName}`.trim(),
    cohort: u.cohort ?? `${u.program}-${u.term}`,
    tags: [u.program, u.term]
  };
}

Test an LMS event webhook to SageStepper

curl -X POST https://api.sagestepper.com/webhooks/lms \
  -H 'Authorization: Bearer <token>' \
  -H 'Content-Type: application/json' \
  -d '{
    "event": "xapi.statement",
    "actor": {"account": {"name": "123456", "homePage": "sis://banner"}},
    "verb": {"id": "http://adlnet.gov/expapi/verbs/completed"},
    "object": {"id": "nova://session/abc123"},
    "context": {"course_id": "CAN-ACCT101-001"}
  }'

SFTP CSV (nightly) sample

external_id,email,first_name,last_name,program,term,course,section
90012345,j.taylor@univ.edu,Jordan,Taylor,BSBA,FA25,ACCT101,001

Security and privacy checkpoints

• Use institution-owned keys where possible; restrict scopes to needed endpoints.
• Store shared secrets in your secrets manager; rotate at the start of each term.
• Review DPIA/FERPA and accessibility workflows with your privacy and disability services teams.

Operational rituals: make practice visible and graded

Weekly Interview Sprint (10–30 minutes/week)

• Students launch Nova via LMS; auto-join a cohort action board.
• Assignment rule: “3 Nova sessions + 1 reflection = 10 pts.” Set expectations in syllabus.
• Advisors watch the progress radar to triage students below readiness 60 or with streak break.

Rubric template (copy/paste into LMS)

criteria:
  - name: Completion
    points: 6
    rule: "3 Nova sessions >=7 minutes each"
  - name: Reflection
    points: 2
    rule: "1 action board post with takeaway + next step"
  - name: Professionalism
    points: 2
    rule: "Camera on, appropriate background, clear audio"
late_policy: "Up to 1 week late for 50% credit"

Advisor triage script

• “I noticed your readiness dipped from 62 to 54 and your streak reset. What felt hard this week? Let’s pick two targeted flashcards, then try one 6‑minute Nova round live.”

Faculty facilitation prompts

• “Open SageStepper, run a 6‑minute Nova behavioral round, then pair-share your best answer using the STAR framework.”
• “Post one action board reflection tagged ‘strength’ and one tagged ‘growth’.”

Dashboards that matter

• Progress radar: segment by course, program, and advisor caseload.
• Readiness distribution: % above thresholds (e.g., 70/80/90) week-over-week.
• Streak heatmap: who needs nudges after weekend breaks.

Impact you can show to your provost (anonymized composites)

Business school pilot (n=480, 6 weeks)

• Setup: Canvas LTI + nightly SFTP from Banner; gradebook with Sprint Completion (10 pts/week) and Readiness Index (100 pts).
• Results: +27 point average readiness lift (baseline 48 → 75), streak 2.9x, 12 mentor hours/week saved via progress radar triage, 18% higher offer rate for students who hit 4+ sprints.

Engineering capstone (n=220, 8 weeks)

• Setup: Brightspace LTI + SCIM via Azure AD; Interview Sprints embedded in lab modules.
• Results: 31% reduction in “no-show” for employer panels, +22 points in technical interview bank, 9 hours/week faculty feedback time shifted from transcription to coaching.

Community college career bridge (n=160, 5 weeks)

• Setup: Moodle LTI + CSV SFTP; action boards used for job search accountability.
• Results: 2.3x streak length, 15% increase in work-based learning placements, 400+ automated flashcard reps per student on average.

What to measure at days 30/60/90

• Readiness score lift vs. baseline and vs. control (if available).
• Practice consistency: median streak days and Nova session minutes per student.
• Advisor load: time to triage, number of outreaches informed by radar signals.
• Downstream signals: interview invites, mock-to-real conversion, offer rate.

Launch kit: communications and training templates

Faculty announcement (LMS)

Subject: New weekly Interview Sprint (10–30 minutes)

Starting this week, you’ll complete a short Interview Sprint in SageStepper, our AI interview studio. Launch from the module page—no extra login. Each sprint = 3 short Nova practice sessions + 1 reflection post. It counts for 10 points weekly. If you keep a streak, your readiness score climbs and we’ll spotlight progress in class.

If you need accommodations, let me know—we can adjust timing and format.

Student onboarding checklist

• Click “SageStepper Interview Sprint” in your LMS module.
• Complete the consent note and test your mic/camera.
• Run one 6‑minute Nova round; check feedback and flashcards.
• Post a takeaway on your action board.

Training for advisors (45 minutes)

• Part 1: Reading the progress radar and readiness trends.
• Part 2: Live coaching using Nova transcripts and flashcards.
• Part 3: Outreach templates for dips and streak breaks.

Where to get help

• Product overview and support: SageStepper Product
• Workflow recipes: Interview Sprint
• Community office hours: Join the community