¶
Table of Contents
Copyright, Copyleft Intentions, and Recommended Citation
Foreword
Editor, Contributing Editors, and Reviewers
Introduction to the Guide
Purpose and Objectives
Development Process
Knowledge Area Overview
Use of this Guide
Knowledge Area 1: Learning Engineering Basics
1.1 What is Learning Engineering?
1.2 The Scope of Learning Engineering
1.2.1 Engineering Social and Environmental Conditions
1.2.2 Engineering Motivational Factors
1.2.3 Engineering Learning Experiences
1.2.4 Engineering Personalization and Adaptivity
1.2.5 Engineering Human Support Systems
1.3 The Professional Disciplines of Learning Engineering
1.3.1 Learning Experience Design
1.3.2 Data Science
1.3.3 Software Engineering
1.3.4 Learning Sciences
1.3.5 Assessment and Measurement of Learning, Learner States, and Contextual Conditions
1.3.6 Education and Training Professions
1.3.7 Learning Environment Engineering
1.3.8 Subject Matter Expertise
1.3.9 Human-Centered Design
1.3.10 Project and Program Management
1.3.11 Organizational Leadership
1.4 Cross Domain Collaboration
1.4.1 Importance of Shared Understanding
1.4.2 How Learning Engineering Practitioners Collaborate
Knowledge Area 2: Human-Centered Design Foundations
2.1 Definition of Human-Centered Design
2.1.1 Empathy in Human-Centered Design
2.1.2 Collaboration in Human-Centered Design
2.1.3 Ideation in Human-Centered Design
2.1.4 Prototyping in Human-Centered Design
2.1.5 Iteration in Human-Centered Design
2.2 Empathy-Driven Research in Human-Centered Design
2.2.1 Empathy-driven Research Techniques
2.2.2 Contextual Inquiry
2.2.3 Empathy Mapping
2.3 Personas
2.4 Prototyping
2.4.1 Types of Prototypes
2.4.2 Role of Prototyping in Learning Engineering
2.4.3 Rapid Prototyping and Agile Methodologies
2.4.3.1 Rapid Prototyping
2.4.3.2 Agile Design in Learning Engineering
2.5 User Testing
2.5.1 Usability Testing
2.5.2 Accessibility and Inclusivity Testing
2.5.3 Cognitive Walkthroughs
2.5.4 A/B Testing of Alternatives
2.5.5 Testing Design Assumptions
2.5.6 Testing for Learning Impact
2.6 Iterative Design and Refinement
2.7 Integrating Human-Centered Design with Learning Science
Knowledge Area 3: Learning Sciences Foundations
3.1 Cognitive Aspects of Learning
3.1.1 Memory, Knowledge Retention, and Retrieval
3.1.1.1 Short-Term Memory
3.1.1.2 Long-Term Memory
System 1 Cognition
System 2 Cognition
Building Expertise
Strategies for Memory, Knowledge Retention, and Retrieval
3.1.2 Attention and Cognitive Load
3.2 Social Aspects of Learning
3.2.1 Social Constructivism
3.2.2 Observational Learning and Modeling
3.2.3 Community and Cultural Contexts
3.3 Motivational Aspects of Learning
3.3.1 Intrinsic and Extrinsic Motivation
3.3.2 Self-Efficacy and Growth Mindset
3.3.3 Engagement and Flow
3.3.4 Stress and Productive Learning
A. The Yerkes-Dodson Law and Arousal
B. Differentiating Eustress and Distress
C. Cognitive and Physiological Impacts of Distress on Learning
D. Stress and Feedback Design
3.4 Each Learner’s Role in Learning
3.4.1 Metacognition
3.4.2 Self-Regulated Learning
3.4.3 Productive Mindsets & Behaviors
3.4 Application of Learning Sciences in Learning Engineering
3.4.1 Evidence-Based Practice
3.4.2 Interdisciplinary Integration
3.4.3 Task Analysis and Cognitive Task Analysis
3.5 Domain-Specific Pedagogy and Context Alignment
3.5.1 Subjects and Skill Specific Pedagogy
3.5.2 Adjusting Modalities for the Level or Stage of Learning
3.5.3 Adjusting Design for Stages of Development and Life
Knowledge Area 4: Engineering Foundations
4.1 Systems Thinking
4.1.1 A Systemic Analysis of Learning as a System of Systems
4.1.2 Identifying components
4.1.3 Defining interdependencies
4.1.4 Defining interfaces
4.1.5 Engineering feedback loops
4.1.6 Designing for Scalability and Sustainability
4.2 Engineering Design
4.2.1 Design as a Problem-Solving Activity
4.2.2 Levels of Abstraction and Encapsulation
4.3 Control Theory
4.3.1 Definition and Relevance
4.3.2 Feedback Mechanisms
4.4 Iterative Design
4.4.1 Definition of Iterative Design
4.4.2 Prototyping and Testing Phases
4.5 Modeling in Engineering Design
4.5.1 Simulation and Prototyping
4.6 Empirical Methods and Experimental Techniques
4.7 Applying Engineering Foundations in Learning Engineering
4.7.1 Building Scalable Learning Solutions
4.7.2 Optimizing and Sustaining Learning Environments
4.7.3 Ensuring Evidence-Based Practices
10.8 Software Engineering Management Body of Knowledge
Knowledge Area 5: Learning Engineering Models and Methods
5.1 Agile Development
5.1.1 Agile Principles and Values
5.2 Data-Informed Design
5.2.1 Types of Data Used in Learning Engineering
5.2.2 Data-Informed Decision Making
5.3 Design Patterns
5.3.1 Concept and Origin of Design Patterns
5.3.2 Common Learning Design Patterns
5.3.3 Application of Design Patterns in Learning Solutions
5.4 Modeling and Simulation
5.4.1 Role of Modeling in Learning Engineering
5.4.2 Simulation for Testing and Validation
5.5 Digital Engineering for Learning Systems
5.6 Continuous Improvement and Iterative Design
5.6.1 Iterative Design Cycle
5.6.2 Role of Continuous Improvement in Learning Engineering
5.6.3 Integration of Agile, Data-Informed, and Design Patterns in Iterative Cycles
Knowledge Area 6: The Learning Engineering Process
6.1 Overview of the Learning Engineering Process
6.2 Challenge Identification
6.2.1 Problem Framing and Contextual Understanding
6.2.2 Stakeholder Engagement and Input
6.3 Investigation
6.3.1 Data Analytics
6.3.2 Identifying Gaps and Barriers to Learning
6.3.2 Understanding the Applicable Learning Sciences
6.4 Creation
6.4.1 Solution Design and Prototyping
6.4.2 Iterative Testing and Feedback Integration
6.4.3 Data-Driven Design Decisions
6.4.3 Instrumentation Design and Development
6.4.3 Implementation Design and Development
6.5 Implementation
6.5.1 Solution Rollout and Monitoring
6.5.2 Data-Driven Adjustments and Ongoing Support
6.6 The Role of Data-Driven Decision-Making
6.7 Feedback Loops in Learning Engineering
6.7.1 Role of Feedback Loops for the Learning Engineering Team
6.7.2 Learner Feedback Loops
Knowledge Area 7: Data Instrumentation
7.1 Data Instrumentation in the Learning Engineering Process
7.1.1 Defining Data Requirements and Relevance
7.1.2 Data Privacy and Ethical Considerations
7.1.4 Data Quality and Noise Reduction
7.1.5 Interoperability and Standards Compliance
7.1.6 Security Considerations in Data Handling
7.1.7 Scalability and Feasibility of Data Collection
7.2 Infrastructure for Learning Event Data Collection
7.2.1 Hardware and Software Sensor Basics
7.2.2 Learning Record Providers – Software
7.2.3 Learning Record Stores – Software
7.2.4 Scalable and Secure Cloud-Based Instrumentation Architectures
7.3 Standards-Based Approaches to Data Instrumentation
7.3.1 Experience API (xAPI)
7.3.2 Metadata Tagging of Learning Resources
7.3.3 Sensor Standards
7.3.3 Supporting Standards
7.4 Stages of Data Collection and Refinement
7.4.1 Learning Event Record Data Pipeline Basics
7.4.3 Transformation of Event Data to Learning Milestone and State Data
7.4.5 Enterprise Learner Record Stores
7.4.5 Adaptive Instructional Systems Record Stores
7.4.5 Interoperable and Verifiable Learning and Employment Records
7.4.5 LRS Pipeline Development – Software Development
Knowledge Area 8: Learning Analytics
8.1 Overview of Learning Analytics
8.1.1 Definition and Scope of Learning Analytics
8.1.2 Learning Analytics in Support of Learning Engineering
8.1.2 Disambiguation of Terms Used in Other Domains (e.g. Statistics, Psychometrics)
8.2 Assessing Learning Outcomes and Learner Progress
8.2.1 Methods for Assessing Learning Outcomes
8.2.2 Tracking Learner Engagement and Progress
8.2.3 Identifying Areas for Enhancement
8.3 Categories of Methods, Algorithms and Models in Learning Analytics
8.3.1 Predictive Modeling
8.3.2 Inference Modeling
8.3.3 Data Mining in Learning Analytics
8.4 Artificial Intelligence and Machine Learning in Learning Analytics
8.4.1 Categories of Artificial Intelligence Techniques
8.4.2 Machine Learning Algorithms in Learning Analytics
8.4.3 Decision Support for Learning Engineering
8.5 Ethical Considerations in Learning Analytics
8.5.1 Data Privacy and Security
8.5.2 Fairness and Bias in Analytics
8.5.3 Responsible Use of AI in Learning Analytics
Knowledge Area 9: Lean Agile Methodologies in Learning Engineering
9.1 Overview of Lean Agile Methodologies
9.1.1 Principles of Lean Agile
9.1.2 Benefits in Learning Engineering
9.2 Lean Agile Frameworks Used in Learning Engineering
9.2.1 Scrum in Learning Engineering
9.2.2 Kanban for Continuous Flow
9.2.3 Lean Software Development for Efficiency
9.3 Best Practices for Lean Agile Development in Learning Engineering
9.3.1 Iterative Development and Incremental Releases
9.3.2 Value Stream Mapping and Minimizing Waste
9.3.3 Cross-Functional Teams and Collaboration
9.3.4 Continuous Integration and Testing with End-User Input
9.3.5 Embracing Change and Pivoting Based on User Needs
9.4 Challenges and Considerations for Lean Agile in Learning Engineering
9.4.1 Balancing Speed with Quality
9.4.2 Managing Team and End-User Alignment
9.4.3 Effectively Integrating User Feedback
Knowledge Area 10: Learning Engineering Operations & Project Management
10.1 Overview of Learning Engineering Operations
10.1.1 Key Differences from Traditional Instructional Design & Development Operations
10.1.2 Importance of Agile Operations in Learning Engineering
10.2 Project Planning in Learning Engineering
10.2.1 Defining Project Objectives and Scope
10.2.2 Iterative Planning and Adaptation
10.2.3 Resource Allocation
10.3 Budgeting and Timelines
10.3.1 Budgeting for Learning Engineering Projects
10.3.2 Timeline Management
10.3.2 Budgeting got Continuous Improvement
10.4 Scaling Learning Solutions
10.4.1 Planning for Scalability
10.4.2 Managing Increased Complexity
10.5 Resource Management
10.5.1 Technical Resource Integration
10.5.2 Human Resource Integration
10.6 Agile Iterations of Creation, Implementation, and Investigation
10.6.1 Agile Iterations in Learning Engineering
10.6.2 Iterative Development and Continuous Feedback
10.7 Considerations for Project Success in Learning Engineering
10.7.1 Clear Communication and Coordination
10.7.2 Flexibility and Adaptability
10.8 Project Management Body of Knowledge
10.8.1 Project Integration Management (PMBOK Knowledge Area)
10.8.2 Project Scope Management (PMBOK Knowledge Area)
10.8.3 Project Schedule Management (PMBOK Knowledge Area)
10.8.4 Project Cost Management (PMBOK Knowledge Area)
10.8.5 Project Quality Management (PMBOK Knowledge Area)
10.8.6 Project Resource Management
10.8.7 Project Communications Management
10.8.8 Project Risk Management
10.8.9 Project Procurement Management
10.8.10 Project Stakeholder Management
Knowledge Area 11: Learning Engineering Professional Practice
11.1 Domain Certification and Professional Competencies
11.1.1 Certification Pathways in Learning Engineering
11.1.2 Key Competencies for Learning Engineers
11.1.3 Cross-Training in Multiple Domains
11.2 Ethical Considerations in Learning Engineering
11.2.1 Data Privacy and Learner Rights
11.2.2 Equity and Fairness in Learning Solutions
11.2.3 Ethical Use of AI in Learning Engineering
11.3 Ongoing Professional Development in Learning Engineering
11.3.1 Formal Education and Certifications
11.3.2 Community Involvement and Networking
11.3.3 Self-Learning, Research and Applied Practice
11.4 Shared Vocabularies and Design Patterns for Professional Collaboration
11.4.1 Shared Vocabularies in Learning Engineering
11.4.2 Design Patterns for Learning Engineering
11.5 Professional Standards in Learning Engineering
11.5.1 Standards for Quality and Effectiveness
11.5.2 Ethics and Professional Conduct
Knowledge Area 12: The Learning Engineering Enterprise
12.1 Shifting from Traditional Learning and Development to Learning Engineering
12.1.1 Moving Beyond Sequential Models
12.1.2 Enterprise-Level Support for Learning Engineering
12.2 Personnel and Team Management in Learning Engineering
12.2.1 Structuring Learning Engineering Teams
12.2.2 Personnel Management and Team Collaboration
12.3 Enterprise Management Structures for Learning Engineering
12.3.1 Moving Learning from a Cost Center to a Center of Profit and Success
12.3.2 Data-Informed Decision-Making
12.3.3 Managing Iterative Development Cycles and Data-Informed Decisions
12.4 Strategy and Long-Term Planning in Learning Engineering Enterprises
12.4.1 Scaling Learning Engineering Practices
12.5 Policy Changes to Foster Learning Engineering Practices
12.6 Addressing the Customer Experience (CX) and Operational Feasibility
12.6.1 Identifying Non-Learner Stakeholder Needs
12.6.2 Operational Feasibility and Administrative Friction
12.6.3 Interoperability and Data Architecture
12.6.4 Total Cost of Ownership (TCO) and Scaling Impact
Glossary of Terms
A
B
C
D
E
F
H
I
K
L
M
O
P
R
S
T
U
V
W
Z
Consolidated References
LEARNING ENGINEERING AS A DISCIPLINE DEFINED
LEARNING ANALYTICS USE IN LEARNING ENGINEERING
Suggested Additional Knowledge Areas & Topics
1. Data Science and Analytics
2. Professional and Interpersonal Skills
3. Technology and Tools