11.3 Ongoing Professional Development in Learning Engineering

Note: This page is located within the 11-1-1 certification pathways directory but covers the 11.3 Ongoing Professional Development topic. See the canonical version at 11.3 Ongoing Professional Development for the full section treatment.

Ongoing professional development is a foundational obligation for learning engineering practitioners. The field draws simultaneously from cognitive science, data analytics, instructional design, and software engineering — no practitioner can master all these domains at entry level, and all of them continue to evolve. Professional development is therefore not an occasional supplement to practice; it is structural to it.

Conferences and Community Engagement. The primary conference venues for learning engineering professional development include the ITS Conference [LE-LS-CE-001], which focuses on intelligent tutoring systems and student modeling; the LAK Conference [LE-LS-CE-002], which covers learning analytics and ethical deployment; and the annual IEEE ICICLE meeting [LE-LS-CO-001], which is the field's primary professional home. These venues provide access to current research, practitioner presentations, and the community relationships that sustain a professional identity. Attending and presenting at these venues is an active form of professional development — not passive consumption but engagement with a community of practice.

Self-Directed Learning. Herbert Simon argued that teaching effectiveness is learnable expertise grounded in science [LE-LS-GL-001]. This framing implies that practitioners have an ongoing obligation to study both the underlying science and its application. The Journal of Learning Engineering [LE-LS-JO-006] is the field's primary applied research venue and should be part of every practitioner's regular reading. The Simon Initiative at CMU [LE-LS-CO-002] maintains open access to DataShop datasets and OLI course materials, enabling self-directed empirical study. Research demonstrating the doer effect [LE-LS-AP-012] applies to practitioners' own development: active engagement with problems, projects, and data produces more durable professional knowledge than passive reading alone.

Action Research and Reflective Practice. Design-based research methodology [LE-LS-AP-003] provides a model for practitioner-led inquiry: iterative cycles of design, implementation, measurement, and revision in authentic contexts. Practitioners who apply DBR methodology to their own work — documenting design decisions, collecting outcome data, revising based on evidence — are simultaneously improving their practice and contributing to the field's knowledge base. The Generalizable LEAMM model identifies reflective practice capacity as a marker of organizational LE maturity [LE-LS-AP-013]. Ryan Baker's work building educational data mining as a field [LE-LS-PP-008] exemplifies how practitioner-researchers can grow a community through systematic contribution over time. Bror Saxberg's framing of "precision education" [LE-LS-PP-009] similarly implies that practitioners must continually refine their own diagnostic and intervention skills through deliberate practice and reflection.

Staying Current with AI Developments. The pace of AI development in education requires particular attention to current literature and pre-print venues. "High-Leverage Opportunities" identifies AI infrastructure investments as a field priority [LE-LS-GL-003], and Kenneth Koedinger's longitudinal work at CMU [LE-LS-PP-005] demonstrates that practitioners who engage with AI developments as researchers — not merely as consumers — contribute most durably to the field. Professional development in AI for LE includes not just learning new capabilities but developing critical judgment about which capabilities are ready for deployment, which carry unacceptable risks, and which require further evidence before use.

Community Corpus — Grounded Context

All items below are drawn verbatim from the lecommons corpus. Each entry is attributed to its source identifier. Confidence: medium. Expert validation required.

Programs & Organizations

Learning Engineering Virtual Institute (LEVI)

LEVI brought together practitioners and researchers to define learning engineering practice. Published foundational resources on LE process and evidence standards.

IEEE ICICLE

International Community for IEEE Learning Engineering. Primary professional home for LE. Developing BoK, standards, credentialing. Resources page is a primary seed source for this corpus.

IEEE ICICLE SIGs & MIGs

The working-group directory for IEEE ICICLE. SIGs and MIGs are where the field's standards, definitions, and research agendas actually get hammered out between annual conferences.

Key Contributors

Ken Koedinger

Co-originator of learning engineering as a field. Creator of Cognitive Tutor. Pioneer of PSLC DataShop and LearnLab. Foundational theorist and practitioner.

Jim Goodell

Co-founder of Learning Engineering book (IEEE Press). Co-editor of canonical LE textbook. Core IEEE ICICLE contributor.

Key Resources

IEEE ICICLE Annual Meeting

Annual community gathering for the IEEE learning engineering community. Standards, BoK, and credentialing discussions.

Recommended Reading from the Learning Engineering Toolkit

These open-access chapters from the Learning Engineering Toolkit (Goodell & Kolodner, 2022) are available free via Taylor & Francis and provide practitioner-level grounding for this topic.

Chapter 3: LE Toolkit — Introduction (open access chapter)

Introductory chapter of the Learning Engineering Toolkit. Frames learning engineering as an evidence-based, iterative design practice. Provides field overview for practitioners and researchers entering the discipline.

Authors: Jim Goodell. License: Taylor & Francis Open Access.

Chapter 5: LE Toolkit — Learning Engineering is a Process (open access chapter)

Defines learning engineering as an iterative, evidence-based problem-solving process. Covers five-phase LE process: challenge identification, solution creation, implementation, data investigation, and continuous iteration. Technology-agnostic principles applicable to AI-driven and low-tech interventions.

Authors: Aaron Kessler, Scotty Craig, Jim Goodell, Dina Kurzweil, Scott Greenwald. License: Taylor & Francis Open Access.

Chapter 6: LE Toolkit — Learning Engineering Applies the Learning Sciences (open access chapter)

Covers the learning-sciences foundations of LE practice. Demonstrates how cognitive science, motivation theory, and evidence-based instructional methods are applied by learning engineers to design, build, and evaluate learning experiences.

Authors: Jim Goodell, Janet Kolodner, Aaron Kessler. License: Taylor & Francis Open Access.