CTFFactory Docs

Learning Paths Overview

Learning Paths are structured, curriculum-aligned progressions that guide individuals from foundational cyber security concepts through to advanced specialist competencies. Each path combines theory, practical exercises, and hands-on challenge labs, culminating in a verifiable Open Badges v3 credential issued by CTFFactory.


Who Are Learning Paths For?

Learning Paths are designed for a broad audience across the cyber security ecosystem:

  • Students and early-career professionals looking to build verified, structured skills
  • Instructors and training coordinators who need to assign, track, and report on learner progress
  • Enterprise security teams seeking objective evidence of staff competency
  • CTF organizers who want to supplement competitions with credentialled learning content

Framework Alignment

CTFFactory Learning Paths are aligned to two internationally recognized competency frameworks, ensuring the skills you develop map directly to industry and government expectations.

CCSSF β€” Canadian Cyber Security Skills Framework

The Canadian Cyber Security Skills Framework defines the competencies expected of Canadian cyber security practitioners across government, critical infrastructure, and private sector roles. CTFFactory paths use CCSSF competency clusters as the primary classification for each learning card, enabling learners and employers to speak a common language about demonstrated abilities.

NICE β€” NIST Workforce Framework for Cybersecurity

The NIST NICE Framework provides a national taxonomy of cyber security work roles, tasks, knowledge, and skills. Paths tagged with NICE alignment include the corresponding Work Role ID and Task references in card metadata, supporting U.S.-based hiring managers and academic programs.

Note: A single Learning Path may align to both CCSSF and NICE simultaneously. Alignment metadata is displayed on the path detail page and embedded in issued Open Badges credentials.


Badge Levels

Every Learning Path is assigned one of five progressive badge levels, reflecting the depth and complexity of the competencies covered.

Level Description
Foundation Core concepts, terminology, and awareness. Suitable for non-technical or entry-level audiences.
Associate Hands-on familiarity with tools and procedures. Equivalent to junior practitioner readiness.
Practitioner Applied skills in realistic environments. Competency suitable for operational roles.
Specialist Deep expertise in a focused domain (e.g., reverse engineering, cloud security).
Advanced Expert-level mastery, cross-domain integration, and leadership-relevant capabilities.

Badge levels appear on the issued Open Badges v3 credential and are searchable in the path catalog. Learners are encouraged to progress through levels sequentially within a domain, although prerequisites are not enforced unless a workspace administrator configures them.


Path and Card Structure

Each Learning Path is composed of one or more courses, and each course contains one or more learning cards. This hierarchy keeps content modular and allows instructors to assign individual cards or entire paths depending on their curriculum needs.

Learning Path
  └── Course
        └── Learning Card (theory + lesson outline + evidence + lab)

Progress is tracked at the card level. A path is considered complete only when all cards within it have been marked as fulfilled by the learner submitting the required evidence and, where applicable, completing the associated challenge lab.


Certifiable, Gap, and Blocked Cards

Each card carries a cert_status value that indicates its role in the credentialling outcome:

  • Certifiable β€” Completion of this card contributes directly to badge issuance.
  • Gap β€” The card covers a competency the learner has not yet demonstrated; completing it closes a skills gap without independently gating the badge.
  • Blocked β€” The card cannot be completed until a prerequisite card or external condition is satisfied.

This model allows instructors to build differentiated paths that adapt to learner backgrounds while still producing a meaningful, verifiable credential at the end.

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