SAM
The Successive
Approximations Model
Welcome to your hands-on journey through the SAM Model of Instructional Design—a dynamic, agile framework that transforms how learning experiences are created. Unlike traditional linear models like ADDIE, SAM (Successive Approximation Model) thrives on rapid prototyping, iterative development, and collaborative feedback. The SAM Model is a flexible instructional design process that emphasizes continuous improvement through three core phases:
- Preparation Phase: Define goals, gather content, and align with stakeholders.
- Iterative Design Phase: Create quick prototypes, test ideas, and refine based on feedback.
- Iterative Development Phase: Build, test, and revise learning solutions in real-time.
This interactive experience breaks down each phase into clickable modules, helping you explore SAM learning strategies, eLearning development tools, and best practices for agile instructional design.
Click through the various phases and tools at your own pace.
Preparation Phase
Information Gathering and Analysis
Interviews
Learning Management System Analytics
Industry Reports
Competitor Analysis Tools
Job Description Analysis
Skills Gap Analysis Software
Learning Style Assessments
Stakeholder Mapping Tools
Mind Mapping Tools
Surveys
Iterative Design Phase
Prototype
Storyboarding Software
Wireframing Tools
Prototyping Platforms
Design
Collaborative Design Tools
Visual Design Software
User Persona Creation Tools
Learning Objective Generators
Content Curation Platforms
Microlearning Design Tools
Gamification Design Platforms
Review
Accessibility Checkers
Design Thinking
Surveys
Focus Groups and Interviews
Iterative Development Phase
Develop
eLearning Authoring Tools
Video Production Software
Audio Editing Tools
Virtual Reality Development Platforms
Augmented Reality Tools
Mobile Learning Development Platforms
Code Editors for Custom Web-Based Learning
AI-Powered Content Generation Tools
Collaboration and Version Control Systems
Implement
Learning Management Systems
Content Delivery Networks (CDNs)
Gamification Platforms
Web Conferencing Tools
Mobile Device Management (MDM) Software
Evaluate
Assessment and Quiz Creators
Learning Analytics Platforms
User Feedback Tools
Behavioral Analytics Tools
A/B Testing Tools
Release Phase
The Release phase in the SAM model isn’t merely a conclusion; it’s a strategic launch and a commitment to ongoing optimization. Far from a simple handover, this stage represents the culmination of iterative design and development, where the instructional solution transitions from a carefully crafted prototype to a live, impactful learning experience.
Unlike traditional linear models where release signifies finality, SAM’s Release phase embodies a philosophy of continuous improvement. It acknowledges that true learning effectiveness is best understood and optimized in situ. By strategically releasing, meticulously monitoring, and iteratively refining, the Release phase ensures that the instructional solution doesn’t just exist, but actively evolves to maximize its impact on learner performance and organizational goals. It transforms the act of “delivery” into an ongoing process of discovery and enhancement, solidifying the instructional design’s value and longevity.
Common Challenges and Considerations
While the SAM Model offers exciting advantages, it’s not without its skeptics in the eLearning world. Some instructional designers find the ongoing cycles of review and prototyping a bit unsettling, especially if they’re used to traditional models where you build something once and move on. Others might feel uneasy starting a project without a fully fleshed-out roadmap or polished end-product in mind.
For those more comfortable with clearly defined outcomes and minimal revision, the iterative nature of SAM can seem chaotic or unpredictable. Adapting to frequent feedback loops and shifts in direction may require a mindset shift—and that’s not always an easy ask.
Ultimately, embracing SAM means welcoming a bit of productive messiness in exchange for flexibility and laser-focused learning results.
