Knowledge Area 1: Learning Engineering Basics
Description: This knowledge area defines the high level concepts of learning engineering as a process and a multidisciplinary practice that combines insights from the learning sciences, engineering, data analytics, and human-centered design. It outlines the scope and goals of learning engineering in creating, implementing, and optimizing learning solutions and conditions for learning.
Knowledge Area 2: Human-Centered Design Foundations
Description: This knowledge area covers human-centered design, focusing on empathy-driven research, prototyping, and user testing. Describes how design is adapted to learners' needs and the learning context.
Knowledge Area 3: Learning Sciences Foundations
Description: This knowledge area covers the learning sciences, including branches of science addressing cognitive, social, physiological and motivational aspects of learning. Emphasizes how research in these areas supports evidence-based practices in learning engineering to foster effective, durable learning.
Knowledge Area 4: Engineering Foundations
Description: This knowledge area covers foundational engineering principles essential to learning engineering, including systems thinking, design methodologies, control theory, and iterative design. Discusses how these principles guide the creation and evaluation of scalable learning solutions.
Knowledge Area 5: Learning Engineering Models and Methods
Description: This knowledge area covers common models and methods used in learning engineering, such as agile development, and data-informed design. Explains how these methodologies are applied to structure the development of learning solutions from concept through continuous improvement.
Knowledge Area 6: The Learning Engineering Process
Description: This knowledge area covers the iterative process of learning engineering, including problem identification (Challenge), needs assessment (Investigation), design and development cycles (Creation), and implementation (Implementation). Explains the role of data-driven decision-making throughout the process and the role of feedback loops in refining learning solutions and learning conditions.
Knowledge Area 7: Data Instrumentation
Description: This knowledge area covers standards-based infrastructures and methods data collection and management used within learning solutions and in support of learning and development. It covers hardware and software sensors used to collect data, data pipelines, and learning record stores. It covers standards and generally accepted approaches to instrumentation using Experience API (xAPI), metadata tagging of experience data and related learning resources and events, and tagging with machine-readable competency definitions. Defines stages used to collect and refine data for different purposes, such as collection in a noisy learning record store (LRS), filtering the noise into a transactional LRS, and further stages for inference, assertions, and certifications of learning milestones achieved.
Knowledge Area 8: Learning Analytics
Description: This knowledge area covers learning analytics, including how data are used to support learning engineering data-informed decision-making. Discusses the role of various methods in assessing learning outcomes, understanding learner progress, and identifying areas for enhancement. Discusses categories of algorithms and models used, including predictive modeling, inference modeling, and data mining. Discusses categories of artificial intelligence and machine learning techniques being used in learning analytics and learning engineering decision support.
Knowledge Area 9: Lean Agile Methodologies in Learning Engineering
Description: This knowledge area covers concepts and methods of lean-agile development to optimize team productivity and project outcomes.
Knowledge Area 10: Learning Engineering Operations & Project Management
Description: Provides an overview of operational considerations in learning engineering, such as project planning, resource allocation, and scaling of learning solutions. Covers logistical aspects, including budgeting, timelines, and the integration of technical and human resources. Calls out operational differences introduced by learning engineering compared to traditional instructional design processes, such as engagement of multidisciplinary teams throughout the process rather than design and development as sequential stages done by separated teams. Considers differences in planning and resourcing projects intended to include multiple agile iterations of creation, implementation, and investigation rather than a single pass design-build effort.
Knowledge Area 11: Learning Engineering Professional Practice
Description: Covers professional practices and standards within learning engineering, including domain certification, ethical considerations, and ongoing professional development. Discusses key competencies and pathways for becoming a learning engineering professional, cross-training in the multiple domains supporting learning engineering, and the need for shared vocabularies and design patterns for professional collaboration by learning engineering teams.
Knowledge Area 12: The Learning Engineering Enterprise
Description: Examines policies and practices of enterprises that support learning engineering operations, such as approaches to personnel, management structures, procurement, strategy, and management. Cover changes needed in enterprise policies, structures, and practices to shift from traditional models of learning and development to the process and practice of learning engineering.