What should you read to understand the evidence base behind learning engineering?
The definitive practitioner guide to cognitive task analysis — the set of methods for eliciting the tacit knowledge, decision strategies, and perceptual skills of domain experts. Essential reading for learning engineers working in complex, high-stakes domains where expertise is hard to articulate.
The definitive revision of Bloom’s original taxonomy, restructuring it into a two-dimensional framework (cognitive process x knowledge dimension) that learning engineers use to write measurable objectives and align assessments with instruction.
Leadership-focused bridge text arguing that technology adoption should be driven by learning science rather than device-first strategy.
Often cited in LE origin stories for its emphasis on becoming “learning engineers” at the leadership level.
Open-access introductory chapter of the Learning Engineering Toolkit.
Open-access chapter on the learning-sciences foundations of LE practice.
Open-access chapter defining learning engineering as an iterative process.
Edited volume on learning analytics methods and applications, frequently cited in LE discussions around evidence and measurement.
Cross-disciplinary book covering analytics foundations, implementations, and implications for evidence-informed learning systems.
An early anchor text for the field's online-learning strand, connecting theory to design-based case studies. (Frequently cataloged in 2018/2019 depending on edition metadata.)
By Jim Goodell and Janet Kolodner.
The canonical book-length treatment of learning engineering — edited by Goodell and Kolodner with contributions from many ICICLE practitioners. Defines the field, walks through the process, and gives concrete applied examples. Several chapters are free via Taylor & Francis Open Access.
Introduces the ECD framework for designing assessments that produce valid evidence of student competencies. Central to how learning engineers think about what to measure and how assessment artifacts connect to claims about knowledge.
Surveys algorithmic fairness concerns in educational technology — from adaptive learning systems to automated grading. Maps fairness definitions from machine learning onto educational contexts and identifies where bias can enter the learning engineering pipeline.
Thille and Smith argue that higher ed can borrow manufacturing's continuous-improvement methods to raise learning quality. An early intellectual precursor to learning engineering's core idea of iterative, data-driven design.
Outlines a framework for structuring and evaluating competency-based education programs. Provides the conceptual grounding for how learning engineers decompose complex capabilities into assessable competency units.
Higher-ed classroom case study. Fensie walks through a specific course intervention, what data moved, and what didn't — an honest practitioner paper at a major learning sciences venue.
The foundational paper introducing design experiments as a research methodology for education. Brown argues that laboratory studies alone cannot capture the complexity of real classrooms, pioneering the approach that learning engineers now use to iterate on interventions in authentic settings.
Landmark paper defining design-based research (DBR) as a methodology for studying learning in context. DBR aligns naturally with learning engineering’s iterative design process — both treat interventions as objects of study that evolve through cycles of design, enactment, and analysis.
A compact case study of the LE process applied to a real adaptive-learning product (Acrobatiq). Worth reading back-to-back with Fensie's ICLS piece on Data-Informed Course Improvement.
MODSIM paper on building a skills simulator with transfer as the explicit design target. Good example of LE-in-defense-adjacent training contexts where simulators are the primary medium.
I/ITSEC 2024 Paper No. 24154 proposes a maturity model to assess how fully an organization has adopted learning engineering practice.
Conference paper introducing a structured adoption-maturity rubric for learning engineering in enterprise, government, and academic settings.
Van Campenhout frames learning engineering as an explicit ethical practice in adaptive instructional systems design and evaluation.
Chapter contribution in HCII 2021 that makes ethical reasoning a first-class concern in learning engineering workflows.
Note: ICICLE images and references are from 2020.
Presents Learning Factors Analysis, a statistical method for comparing cognitive models of student knowledge. Demonstrates how knowledge component models can be empirically tested and refined using interaction data — a core technique in learning engineering.
Published in Journal of the Learning Sciences. By Janet Kolodner.
This paper presents the value proposition of the GIFT framework for advancing learning engineering, published in the Proceedings of the 11th Annual GIFT Users Symposium.
Educause's concise primer on learning engineering — the best single-page overview for newcomers. Covers what it is, how it works, and why it matters in a format designed for busy higher-ed leaders.
Blurb pending.
Long-form feature on learning engineering for a broad audience, profiling CMU/ICICLE-aligned work and the case for evidence-based instructional improvement at scale.
Accessible overview piece that helped translate LE concepts from specialist circles to a broader edtech readership.
Practitioner article on staffing and collaboration patterns for data-informed learning improvement teams.
Practitioner article on staffing and collaboration patterns for data-informed learning improvement teams.
Practitioner article on assembling multidisciplinary teams to leverage learning data effectively. Covers roles (data analyst, learning engineer, instructional designer) and collaborative workflows.
Source URL: https://www.td.org/content/td-magazine/build-a-learning-data-dream-team
Part of the 'Learning Engineering Enlightenment: Think Like an Engineer' series, this article explores the development of an ontology for the learning engineering field.
Published in LinkedIn post. By Nirmal Patel.
Full proceedings from the 2024 ICICLE conference — the most current snapshot of peer-reviewed learning engineering research and practice papers.
The counterpoint to Goodell's LE-vs-ID piece. Fensie argues the distinction is more about mindset and context than a hard boundary — a widely shared perspective from the ID side of the debate.
Short military-facing overview connecting LE concepts to mission-critical workforce training and high-consequence performance contexts.
Short military-facing overview connecting LE concepts to mission-critical workforce training and high-consequence performance contexts.
Concise overview of learning engineering for military and defense training contexts, published by US Army Press. Bridges LE field concepts with high-consequence domain workforce applications.
The 'Learning Engineering at a Glance' poster won the People's Choice Award for Best Poster Design at the Advanced Distributed Learning Initiative iFEST 2022 conference.
Wagner's 2024 follow-on to her long thread of LE-vs-ID articles. The sharpest version of her argument that the shift isn't tools, it's disposition: LEs reason like engineers about uncertainty and evidence.
Article on how learning engineering can shape educational systems at scale through changes to policy, infrastructure, and practice.
Article on how learning engineering can shape educational systems at scale through changes to policy, infrastructure, and practice.
Multi-author article framing how learning engineering perspectives can transform educational systems at scale. Addresses policy, infrastructure, and community dimensions.
Source URL: https://journals.sagepub.com/doi/full/10.1177/21695067231192886
EdSurge's dedicated learning engineering topic page — an ongoing collection of journalistic pieces that track the field's evolution for a general ed-tech audience.
Blake-Plock's I/ITSEC paper formalizing learning engineering as both an academic discipline and a profession. Lays out the standards-based, IEEE-rooted vision that shaped ICICLE's working groups.
Open-access overview of the field’s current state, defining practices and outlining priority research problems for the next stage of development.
Open-access overview of the field’s current state, defining practices and outlining priority research problems for the next stage of development.
Open-access article surveying the state of the learning engineering field as of 2022. Reviews definitions, practice norms, evidence standards, and key open problems.
Source URL: https://tmb.apaopen.org/pub/5ib9cpqa/release/1
Scholarly analysis of the relationship between learning sciences and learning engineering, arguing for tighter integration of the two.
Scholarly analysis of the relationship between learning sciences and learning engineering, arguing for tighter integration of the two.
Scholarly article examining the relationship between learning sciences and learning engineering. Argues for integration rather than separation, tracing epistemological differences and convergences.
Source URL: https://www.tandfonline.com/doi/full/10.1080/10508406.2022.2100705#abstract
Research article demonstrating how student-centered data science and iterative LE design can reinforce one another in applied settings.
Research article demonstrating how student-centered data science and iterative LE design can reinforce one another in applied settings.
Research article integrating educational data science methods within a learning engineering framework. Demonstrates how student-centered analysis and iterative design reinforce each other in practice.
Source URL: https://link.springer.com/chapter/10.1007/978-981-99-0026-8_1
Case study showing how LE team practices are applied in medical and healthcare education, where performance stakes are high.
Case study showing how LE team practices are applied in medical and healthcare education, where performance stakes are high.
Case study and practitioner article applying learning engineering team approaches to medical and healthcare professional education. Demonstrates LE in a high-stakes, complex domain.
Source URL: https://253f0a53-bb62-46af-b495-b4548f4d5d90.filesusr.com/ugd/c9b0ce_5251ffb6173c4c9a968a76832aa36778.pdf
Published in Journal of Applied Instructional Design. By Dina Kurzweil and Karen (Beth) Marcellas.
ASU researchers McNamara, Craig, and Roscoe discuss how LE blends design creativity with empirical rigor. Good overview of the ASU Learning Engineering Institute perspective.
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.
Includes a section on “Elevation of Instructional Design, Learning Engineering, and UX Design in Pedagogy in Practice” — widely cited in making the case for LE as an emerging role.
The 2020 EDUCAUSE Horizon Report Teaching and Learning Edition identifies the elevation of instructional design, learning engineering, and UX design in pedagogy as one of six emerging technologies and practices.
Federal policy report examining how AI is being used in educational contexts, with recommendations for responsible deployment. Addresses equity, privacy, transparency, and the role of educators alongside AI systems — essential context for learning engineers building AI-powered tools.
One of the earliest articulations of the learning-engineer role, by Candace Thille. Still cited as a founding document.
Thille's ACE report making the economic case: technology-mediated learning can change higher ed's cost-quality tradeoff, but only if institutions treat courseware as engineered systems. A policy-level precursor to LE thinking.
Discusses the potential of learning engineering to advance the theory and practice of learning, identifies challenges that have slowed its uptake, and proposes how they can be addressed.
This research report explores how learning engineering is expected to impact L&D — a concise primer by Ellen Wagner for practitioners and leaders new to the field.
MIT policy report arguing that online education should catalyze structural reform in higher ed — an early institutional signal that learning needs engineering-style discipline, not just more technology.
State-of-field report on XR in learning, summarizing evidence, adoption patterns, and implementation considerations for immersive modalities.
State-of-field report on XR in learning, summarizing evidence, adoption patterns, and implementation considerations for immersive modalities.
Annual state-of-the-field report on extended reality (XR/VR/AR) in learning contexts, co-produced with ICICLE. Covers research landscape, practitioner adoption, and evidence base for immersive learning.
Source URL: https://www.immersivelrn.org/initiatives/state-of-xr-immersive-learning-project/
Pandemic-era compilation from MIT Open Learning distilling what the learning sciences say about effective remote instruction. Practical, evidence-based, and widely shared during the 2020 pivot.
One of the two most-cited pieces on the LE-vs-ID question. Goodell draws a clear line: learning engineers apply engineering process discipline and data feedback loops that traditional instructional design lacks.
Jeremy Creech describes the learning engineering work at Cisco Networking Academy to create equitable experiences for people to develop employable skills.
Published in Cisco Blog. By Stacey Faucett.
The AI that powers the language-learning app today could disrupt education tomorrow.
Published in IEEE Specturm. By Klinton Bicknell, Claire Brust, Burr Settles.
Published in Linkedin. By Eric Basu.
Stodd, Schatz, and Stea apply Stodd's social learning framework to learning engineering, arguing for open, iterative practice. A useful bridge between the L&D community's language and LE's engineering mindset.
Blake-Plock's announcement of IEEE ICICLE and the piece that popularized the Saxberg definition of learning engineering. Essential context for understanding how the field organized itself.
Clark Quinn's early call to the L&D community: learning engineering is coming, and practitioners who already think systematically about evidence-based design are well positioned to lead it.
The foundational text. Saxberg's 2015 Chronicle piece made the original public case that higher education needs professionals who apply engineering rigor to learning — the argument that launched the field's identity.
Reflective essay on the origins, purpose, and practical value of learning engineering from a leading field contributor.
Reflective essay on the origins, purpose, and practical value of learning engineering from a leading field contributor.
Personal essay and scholarly reflection by Janet Kolodner on the nature of learning engineering, its roots in learning sciences, and her reasons for engagement with the field.
Source URL: https://www.tandfonline.com/doi/full/10.1080/10508406.2023.2190717?src=recsys