T15
Evidence & Evaluation Standards
What counts as evidence? Level 1-4 Kirkpatrick; decision-grade evidence; reproducibility in LE research.
12 resources tagged with this topic
Concepts in this topic
Assessment & Measurement Design Apply
Builds on: How People Learn: Cognitive Foundations, The Learning Engineering Process
Tracking Decisions & Evidence Throughout the Process Apply
Builds on: The Learning Engineering Process
Evaluation Frameworks Evaluate
Builds on: Learning Analytics, Assessment & Measurement Design
Evidence Standards in Learning Research Evaluate
Builds on: Assessment & Measurement Design, Research Methods in Learning Engineering
Resources
Reading List 5
Design-Based Research: An Emerging Paradigm for Educational Inquiry
Generalizable Learning Engineering Adoption Maturity Model
I/ITSEC 2024 Paper No. 24154 proposes a maturity model to assess how fully an organization has adopted learning engineering practice.
Learning Engineering as an Ethical Framework
Van Campenhout frames learning engineering as an explicit ethical practice in adaptive instructional systems design and evaluation.
Why Did We Do That? A Systematic Approach to Tracking Decisions in the Design and Iteration of Learning Experiences
Totino and Kessler's practical protocol for decision tracking across LE projects. Pairs well with our Field Note on Five Whys — this paper operationalizes the evidence-decision-tracker discipline ICICLE publishes.
Practice 4
Generalizable Learning Engineering Adoption Maturity Model
I/ITSEC 2024 Paper No. 24154 introduces a generalizable learning engineering adoption maturity model for organizations and enterprises.
Learning Engineering Evidence Decision Tracker
Template set for documenting design decisions with their supporting evidence across iterations, improving transparency and reproducibility.
Learning Engineering Implementation Checklist
Project checklist covering end-to-end implementation steps from problem framing through evaluation and dissemination.
Data Use Across the Learning Engineering Process
Graphic and guidance showing how formative, summative, behavioral, and survey data are used across LE phases.
Events 1
Learning Engineering in the Age of AI — Kumar Garg
Garg (Walton-funded LE efforts; formerly OSTP) frames AI-and-LE as an evidence and infrastructure problem, not a pedagogical one. The pitch: the field needs many more studies, faster, to match AI's pace — and LE is the discipline set up to do that.
Community 2
LENS @ JHU — Learning Engineering for Next-Generation Systems
The Learning Agency
Non-profit research and policy shop led by Ulrich Boser, the co-author of 'High-Leverage Opportunities for LE.' Runs several open-data competitions and the Learning Engineering Google Group that ICICLE references.