Real academic credit.Real talent intelligence.
Woolf gives every learner an accredited transcript that motivates them to finish — and gives you a live, evidence-backed read on what they know, can do, and are becoming across more than 1,000 benchmarks.
Beyond the transcript
Real credit motivates learners. The Competency Report tells you who they are becoming.
Accredited credit is the engine that gets a learner to the finish line. But a transcript alone cannot tell you which specific competencies are sharp, where judgment is still developing, or what they are ready to do next. Woolf gives you both: real academic credit, and a live competency model underneath it.
Accredited academic record
Competency Report
The credential learners care about. ECTS-backed credit, signed and on-record. The motivation engine that keeps cohorts moving from week one through capstone.
The intelligence you act on. Every Intended Learning Outcome scored, banded, and tied to evidence — refreshed whenever new artifacts arrive.
Three-layer model
Knowledge. Skills. Competences. Measured separately, together.
Every Woolf course maps its outcomes onto three orthogonal layers — across more than 1,000 benchmarks of skills, knowledge, and applied competence. A learner can know a domain cold and still struggle to apply it. The report makes that visible instead of averaging it away.
Knowledge
What they understand. The conceptual ground beneath every decision.
Skills
What they can do. Verifiable through the artifacts they ship.
Competences
How they apply it. Judgment under ambiguity — the rarest layer to assess.
The arithmetic mean of every assessed outcome — derived, not declared.
Click into any score and the report tells you exactly which outcomes pulled it up and which dragged it down. No black box.
Granularity, not averages
Every Intended Learning Outcome, scored and defended on its own terms.
One score per course tells you nothing. Twenty scores per course — each with a band, an evidence trail, and a confidence rating — tells you exactly where this person is sharp, where they are growing, and what they should do next.
Critically evaluate statistical inference techniques for business decision-making.
Demonstrated nuanced trade-off analysis across three distinct case studies.
"Switching from Frequentist CI to Bayesian credible intervals reduced our false-positive rate by 38%."
Module 3 Capstone, p. 14"…I would not recommend the bootstrap here because the sample is structurally non-IID."
Live Defense, 12:42"My prior assumed independence; reading Gelman changed how I framed the entire analysis."
Reflective Journal, Week 7Build reproducible data pipelines using version-controlled, tested code.
All three submitted projects shipped with passing CI, pinned dependencies, and seed control.
Communicate quantitative findings to non-technical stakeholders to drive action.
Stakeholder presentations rated "clear and actionable" by 4 of 5 reviewers.
Explain core supervised learning algorithms and their inductive biases.
Correctly compared bias/variance trade-offs across linear, tree, and ensemble methods.
Design and execute causal inference studies in observational settings.
Identified confounders correctly but did not formalize a DAG or run sensitivity analysis.
Identify ethical risks in data use and propose mitigations grounded in policy.
Spontaneously flagged a privacy issue mid-project and produced a remediation plan.
Articulate the strengths and limitations of model interpretability methods.
Solid on permutation importance; conflated SHAP and LIME in one assessment.
Conduct exploratory data analysis on novel, messy datasets.
EDA notebooks are well-structured, hypothesis-driven, and tightly scoped.
Lead an end-to-end analytics project from ambiguous problem to deployed decision.
Has executed components excellently but has not yet owned a project end-to-end.
Apply optimization theory to constrained business resource problems.
Correctly formulated three LP and MILP problems with appropriate solver choice.
Continuously updated
Every artifact updates the model. The report is alive.
Submissions, peer reviews, capstone drafts, mentor feedback, oral defenses — each one flows in as evidence and refreshes the relevant outcomes within minutes. No annual review. No batch processing.
Critically evaluate statistical inference techniques.
Build reproducible data pipelines.
Communicate findings to non-technical stakeholders.
Design and execute causal inference studies.
The result: a live, evidence-backed model of every learner. Whenever an enterprise reviewer opens the report, it reflects the state of the world right now — not a snapshot from semester end.
Benchmarked, not just measured
Promotable? Hireable? Ready for the next stretch assignment? Now you can answer.
A score in isolation tells you nothing. The Competency Report places every learner against the benchmark that matters for the decision you’re making — across more than 1,000 calibrated outcomes.
Senior Data Analyst
The learner’s competency vector overlaid on the role profile your team requires. Strengths and gaps surface in one glance.
What this tells you
This learner exceeds the Knowledge bar for a Senior Data Analyst role and meets the Skills bar. Competences sit just below target — the report flags exactly which outcomes are holding them back, with linked evidence and recommended next assignments.
Cohort distribution
Where this learner sits among 48 peers in the same program and term.
Top quartile
A 78 in the 70–79 bucket places this learner in the top third of their cohort. Hover to see exactly which outcomes drove the placement.
Outcome coverage
Of 20 intended outcomes for this course, these are how many we have direct evidence for, partial evidence for, and none for.
Coverage is the honest version of completeness.
The transcript confers credit for the course as a whole. The Competency Report tells you exactly which outcomes were demonstrated and which are still an open question.
Defensible decisions, not gut calls.
Whether you’re writing offers, building succession plans, or allocating training budget, every decision can point to a specific outcome, a specific evidence trail, and a specific gap.
Defensible by design
Every score withstands a hostile question. Every gap is on the record.
Talent decisions get challenged. The Competency Report is built so that every line item can be defended with linked evidence, an explicit methodology, and a transparent acknowledgment of what we don’t yet know.
How the numbers are made.
Analytical framework
Outcome-based assessment grounded in Bloom-aligned ILO mapping. Every claim about competency is traced to one or more learner artifacts and weighted by evidence strength.
Scoring methodology
Per-ILO scores are computed as the weighted mean of evidence quality. Category scores are the arithmetic mean of constituent ILOs. The overall score is the arithmetic mean of all assessed ILOs.
Evidence hierarchy
Direct artifacts > supervised performance > reflective self-report. Synchronous evaluations (defenses, live work) outrank asynchronous submissions for skills claims.
We tell you what we don’t know, too.
- Capstone defense — full alignment with three Knowledge ILOs
- Source repository — verifiable, version-controlled implementation
- Mentor feedback — independent third-party evaluator
- End-to-end project ownership — no single artifact yet covers full lifecycle
- Causal inference design — observational only; no quasi-experiment
The report explicitly surfaces what we’re unsure about. Confidence ratings, gaps, and missing artifacts are first-class fields — not buried footnotes.
Bring rigor to every talent decision.
Real academic credit to motivate the learner. Real talent intelligence to inform the decision. Talk to us about deploying Woolf inside your organization.