Successive Approximation Model 
(SAM)

The Successive Approximation Model, SAM, developed by Michael Allen, is an agile instructional design framework that emphasizes rapid prototyping, collaboration, and iterative refinement. Within this framework, SAM 1 is typically used for smaller projects, while SAM 2 is structured for larger or more complex initiatives. The Unit 6 materials focus explicitly on the SAM 2 configuration, which includes three phases: Preparation, Iterative Design, and Iterative Development leading to Rollout (Allen Interactions Inc., 2021; Thomas, 2015).
The Preparation phase centers on background research, needs assessment, and measurable objective development. The Iterative Design phase introduces the Savvy Start and rapid prototyping cycles. The Iterative Development phase includes Design Proof, Alpha, Beta, Gold, and Rollout stages, embedding formative evaluation throughout. Unlike strictly sequential models, SAM 2 operationalizes iteration early and continuously.

Explore new horizons

Operational Implementation and Feasibility

Because this minicourse is being developed primarily by one designer using limited resources, SAM 2 will be implemented in a scaled manner. The Preparation phase will use backward design principles to define outcomes first (Wiggins & McTighe, 2005). The Iterative Design phase will include two structured prototype cycles rather than extended multi round development. A small pilot group of adult learners will provide formative feedback in place of a full stakeholder team. The Iterative Development phase will progress through streamlined Alpha and Beta stages before final rollout, using free or trial based digital authoring tools.
This adaptation preserves the iterative strengths of SAM while maintaining feasibility within time, technological, and staffing constraints. The model is therefore blended with backward design for outcome clarity and scaled for solo implementation.

Strengths and Limitations of SAM 2 in My Minicourse Context

My minicourse, Digital Literacy and A.I. Sensemaking for Professional Contexts, addresses a performance discrepancy in which adult learners use A.I. tools but lack structured evaluation strategies. The objectives involve higher order cognitive processes, including evaluation, ethical reasoning, and credibility analysis. These align with constructivist and connectivist perspectives, which emphasize contextualized learning within digital information networks (Siemens, 2005).
SAM 2 supports these goals through iterative prototyping of authentic professional scenarios. Early testing of A.I. generated case studies allows refinement based on learner interaction, ensuring realism and alignment with professional practice.

Comparison to ADDIE

SAM 2 was compared to ADDIE, Analysis, Design, Development, Implementation, Evaluation. ADDIE provides a structured and systematic framework that is valuable for well defined or stable instructional environments (Pappas, 2021). Modern applications of ADDIE can be iterative in practice, and evaluation can occur throughout the process.
However, iteration is structurally embedded in SAM 2 rather than adaptively applied. For a minicourse addressing evolving A.I. technologies, the early and continuous prototyping cycles of SAM reduce the risk of outdated scenarios and misaligned assessments. While ADDIE could be implemented iteratively, SAM 2 foregrounds responsiveness as its defining characteristic.
In this context, a blended approach is most appropriate. Backward design establishes learning outcomes, and a scaled SAM 2 framework governs development and refinement. This combination balances theoretical rigor, operational feasibility, and responsiveness to evolving content demands.

References

Allen Interactions Inc. 2021. SAM: The successive approximations model.
Herrholtz, K. 2020. Rapid instructional design with SAM. eLearning Industry. https://elearningindustry.com/sam-successive-approximation-model-for-rapid-instructional-design
Pappas, C. 2021, January 13. ADDIE model vs SAM model: Which is best for your next elearning project. eLearning Industry. https://elearningindustry.com/addie-vs-sam-model-best-for-next-elearning-project
Siemens, G. 2005. Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
Thomas, J. 2015, September 27. SAM, successive approximation model. YouTube. https://youtu.be/zJwDCrl0bq4
University of Maryland Global Campus. 2024. Overview of the successive approximation model, SAM. Brightspace.
Wiggins, G., & McTighe, J. 2005. Understanding by design, 2nd ed. Association for Supervision and Curriculum Development.

Successive Approximation Model (SAM)

Explore new horizons

Operational Implementation and Feasibility

Strengths and Limitations of SAM 2 in My Minicourse Context

Comparison to ADDIE

References

Allen Interactions Inc. 2021. SAM: The successive approximations model.

Herrholtz, K. 2020. Rapid instructional design with SAM. eLearning Industry. https://elearningindustry.com/sam-successive-approximation-model-for-rapid-instructional-design

Pappas, C. 2021, January 13. ADDIE model vs SAM model: Which is best for your next elearning project. eLearning Industry. https://elearningindustry.com/addie-vs-sam-model-best-for-next-elearning-project

Siemens, G. 2005. Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.

Thomas, J. 2015, September 27. SAM, successive approximation model. YouTube. https://youtu.be/zJwDCrl0bq4

University of Maryland Global Campus. 2024. Overview of the successive approximation model, SAM. Brightspace.

Wiggins, G., & McTighe, J. 2005. Understanding by design, 2nd ed. Association for Supervision and Curriculum Development.

Dr. J. Ryner, Ed.D.