Based on Jung, Kim, Lee, and Shin (2021)
The ADDIE model has long been foundational to instructional design, but it is often criticized for being too linear, rigid, and time-consuming. Modern e-learning development requires faster iterations, greater collaboration, and a stronger focus on learner needs. The Successive Approximation Model (SAM), introduced by Allen and Sites (2012), offers a flexible and agile alternative to ADDIE by emphasizing prototyping, collaboration, and continuous feedback.
SAM is distinguished by its agile, iterative approach to design and development. It encourages rapid prototyping, frequent evaluation, and collaboration among designers, SMEs, and learners. Rather than moving step-by-step in a rigid sequence, SAM cycles through design, testing, and revision, allowing teams to adapt to new insights and changes efficiently. The focus is on creating engaging, learner-centered e-learning experiences.
SAM consists of three main phases: Preparation, Iterative Design, and Iterative Development. - In the Preparation Phase, designers gather background information and conduct a “Savvy Start” to brainstorm ideas. - In the Iterative Design Phase, prototypes are created, reviewed, and revised collaboratively. - In the Iterative Development Phase, the design evolves through alpha, beta, and gold versions—each incorporating learner and stakeholder feedback.
Jung et al. (2021) applied SAM to develop e-learning content for a university course. The researchers collected data through interviews, surveys, and observations with students, SMEs, and instructional designers. The process emphasized iterative collaboration and the creation of bite-sized, HTML5-based learning materials to reduce cognitive load and enhance accessibility.
The preparation phase includes four steps: information gathering, understanding learners’ needs, conducting a savvy start, and analyzing roles and opinions. This phase ensures the design process is rooted in the learners’ experiences and the team’s shared understanding of project goals.
Surveys and interviews were conducted to understand learners’ perceptions of previous e-learning materials. Learners valued engaging, well-paced lessons but found some content redundant or overly interactive. This feedback guided prototype creation and refinement in later phases.
Learners expressed the need for shorter, interactive, and accessible content. Based on these needs, the project team integrated HTML5 technologies and responsive web design to enhance usability and engagement.
The “Savvy Start” is a collaborative kickoff session involving SMEs, IDs, designers, and learners. Through brainstorming and guided questions, the team defines core objectives, potential challenges, and strategies for effective content development.
Each participant contributes unique expertise: - SMEs provide content and ensure accuracy. - Learners offer insight into usability and motivation. - IDs manage design strategy and ensure alignment with goals. - Prototypers translate ideas into functional visuals. Collaboration among these roles helps ensure content relevance and engagement.
In this phase, prototypes are developed, tested, and refined through collaboration between SMEs, designers, and learners. Tools like InVision facilitate feedback loops, allowing teams to simulate and adjust course materials before full development.
The project evolves through multiple releases: the alpha version demonstrates structure and function, the beta version refines interactivity and usability, and the gold version integrates final feedback. This process ensures the final product is polished, effective, and user-centered.
SAM demonstrates the power of agile collaboration in instructional design. By emphasizing rapid prototyping and learner feedback, it enables continuous improvement and responsiveness to changing needs. The study concluded that SAM-based e-learning was more user-friendly, adaptable, and impactful than traditional models like ADDIE.
Reference:
Jung, H., Kim, Y. R., Lee, H., & Shin, Y. (2021). Advanced Instructional Design for Successive E-Learning: Based on the Successive Approximation Model (SAM). Dankook University, Oklahoma State University, and Hanyang University.