Learner Placement and Support
Up: Learner Personas · Applied in: HVE Sample Labs, Competency and Evaluation Goals
These are draft instructional profiles, not fixed job titles or seniority bands. Assess technical experience separately from work focus: a senior project manager can be new to coding, while an experienced developer can be new to AI tooling. Learners can fit more than one profile.
Two Placement Dimensions
| Dimension | Starting | Working | Advanced |
|---|---|---|---|
| Engineering fluency | Reads requirements and diffs with guidance | Uses Git, runs checks, edits an application independently | Designs systems, investigates failures, reviews trade-offs |
| AI-assisted work | Has tried chat or completions | Uses repository context and verifies changes | Curates standards, manages evidence, designs team workflows |
Add a focus preference: application engineering, data/AI engineering, architecture/quality, or delivery/product. Do not collapse these into one overall level.
Working Exercise Profiles
| Persona | Typical starting point | Learning goal | Support | Appropriate evidence |
|---|---|---|---|---|
| Technical Explorer | Understands business processes; little repository experience | Frame a bounded problem and judge whether evidence supports acceptance | Guided repository tour, vocabulary, paired implementation | Requirement clarification, observed check, useful handoff |
| Application Practitioner | Can edit code, use Git, and run tests; AI practice varies | Deliver a small change using evidence and explicit acceptance criteria | Starter repository and bounded scope | Plan, working change, edge-case validation, review |
| Data and AI Practitioner | Comfortable with SQL, notebooks, or pipelines; software delivery practice varies | Validate data and translate experiments into repeatable behavior | Small CSV fixture and an explicit data contract | Deterministic result, rejected-input behavior, reproducible checks |
| Technical Lead / Architect | Experienced at design and review; agent governance varies | Encode standards and challenge AI-produced evidence and trade-offs | Incomplete decision record and conflicting completion claim | Scoped decision, review finding, accepted correction |
| Delivery / Product Lead | Owns scope, dependencies, client expectations; coding may be limited | Convert ambiguous requests into verifiable work and accountable handoffs | Artifact examples and an engineer partner | Requirements, decision ownership, acceptance disposition |
Placement Activity (Proposed 15–20 Minutes)
Ask each learner to explain a small requirement, locate a relevant function or artifact, run or observe a check, and explain why a passing check might be insufficient. Ask what they can do independently versus with support. Record experience with Git, chosen language, Copilot, and access constraints.
Placement is for support and exercise selection. Do not use employer, job title, years served, or confidence as a substitute for observed ability. Let learners move paths after the first exercise.
Suggested Starting Routes
- Explorer: Sample - Evidence Review and Handoff, then pair on Sample - Claims Validation.
- Application: Sample - Claims Validation, followed by evidence review.
- Data/AI: Sample - Claims Summary, followed by evidence review.
- Lead/architect: review either implementation, then propose a reusable rule.
- Delivery/product: clarify requirements and own the handoff review, paired with an implementer.
All routes share problem framing, evidence quality, and human accountability. Independent coding is assessed only where the role requires it. Confirm actual cohort composition through Questions.
Engagement Leader Briefing
Retain the engagement-leader path in Learner Personas. Use the evidence-review sample to discuss acceptance claims and client responsibilities, with limited coding depth. Whether a briefing leads to attendance recognition or a competency credential remains a program decision.