Successive Approximation Model (SAM)

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Within the realm of Instructional Design, its early models were based on the Behaviorist theory whereby specific desired behaviors were rewarded once displayed and external reinforcements were the motivating factor for such desired behavior. Within recent times, there has been a shift in approach towards a more inclusive design which considers all elements involved in the process. One of these models is the Successive Approximation Model (SAM). Coined by Michael Allen (2012), the SAM was birthed from Agile software development and adapted to instructional design model specifications. Michael Allen, CEO of Allen Interactions is a stalwart in the field of educational interactive multimedia systems and his company designs customizable e-learning applications as well as provides training and consultation services. Allen has a PhD in educational psychology from The Ohio State University and is the author of nine books. His best sellers include Michael Allen's Guide to e-Learning and a popular among instructional designers, Leaving ADDIE for SAM. The SAM is a derivative of the Analysis, Design, Development, Implementation and Evaluation (A.D.D.I.E) instructional design model. Hence, one model cannot be referred to without acknowledging the other. Galagan (2013) offers a definition of the model. “Successive approximation is a tool for estimating the value of an unknown quantity by repeated comparison with a sequence of known quantities” (p. 23). SAM was the answer to Allen’s quest for an alternative model of accelerated learning whereby users were not confined to the ‘perfect completion of this task before moving on to the next’ linear design which ADDIE exemplifies. According to Glova (2018), “The SAM model focuses on the iterative nature of (1) analyze, (2) design, and (3) develop, suggesting that agile development should be applied to the instructional design process (p. 2). Whereas ADDIE’s model focuses on the end product created in the evaluation phase, the SAM allows for repetitive attempts until the end product is closest possible to the desired outcome. Galagan states “Design and development happen simultaneously and evaluation occurs throughout the process” and furthermore, SAM uses “iteration and short work cycles to produce quick results” (p. 23). Hart (2018) also supports by stating “this model incorporates a collaborative team approach and enables designers to quickly develop educational materials. It is aimed at performance-driven learning with designers and project teams repeating small steps through three repetitive iteration phases (p. 59). Allen (2013), in his quest to discover a more effective method of learning cites the SAM as his alternative choice to the ADDIE model as it allows for a greater creative platform for its users. He posits “SAM is much more practical and less tedious. It’s more natural, too, in that it encourages experimentation, changes, and new ideas as you go along, rather than trying to lock in designs and content as early as possible” (p. 69). Allen, who was an ADDIE advocate for many years also contends that the SAM model is also more tolerant of the affective of its users. He puts forth “Successive approximation recognizes, first of all, that no project is going to be perfect. It also recognizes that your best ideas are going to come late in the process, which used to upset everybody” (p. 69). The process of the SAM has its roots in ADDIE with the differing component being its allowance for iteration or repetition at each phase. In other words, the user can move backward to review and rectify in the lead up to the desired product or outcome. The SAM model is also a more compact version of its 5-phase predecessor by way of condensing into 3 phases (Preparation, Iterative Design, Iterative Development) with the added feature of user manipulation throughout the process (see Figure 1). Czeropski and Pembrook (2017) put forth “Like the Agile process, the SAM process takes smaller steps within a larger framework. This model follows the basic tenets of evaluate, design, and deliver […] (p. 39). Turayev (2018) concurs by stating “SAM is an alternate way to progress with ET integration through short cycles rather than taking a longer step-by-step procedural process (p. 23). In essence, the SAM’s emphasis is on the refinement of the ongoing design processes which lead up to the final product rather than on the final product itself (Dodd, 2013, p.71).

The SAM’s initial preparation phase involves gathering information based on a need assessment basis. Participants are asked to assemble data which acts as the foundation upon which the design will be built. Also known as a Savvy Start, this phase involves collaboration and brainstorming to ascertain the way forward. During this session, background data is collated and initial ideas are proposed. The design phase focuses on creating a prototype which is reviewed and redesigned as required based on evaluation. Iteration occurs at this phase until some semblance of the desired product is attained and can be implemented. In the development phase, the prototype is implemented and evaluated on a broader scale and based on results can either be sent back for editing or redesigning, remain in the developmental stage for fine tuning or is finalized to be launched. In this way, the SAM is flexible and allows for continuous iteration throughout the entire process and is not obsessed with precision at each step before moving forward. The user has the advantage of testing and re-testing the prototype and launching and re-launching it which accrues essential early detection of errors, problems and issues which can then be efficiently remedied before the final product is delivered. Essentially, the SAM provides “an upfront analysis and data-gathering, followed by a design, a quick prototype to test the design, and an iterative process to continually test and improve the design as the development process works its way to a completed product (Ulrich, 2017). The following table by Tamez (2016) highlights the SAM in terms of its concepts, functions and underlying theories (p.21).

The main reason for the SAM’s development was to facilitate the need for an alternative option to the established ADDIE model which embodied a linear or waterfall design. There was no option of regressing to modify or alter that which was already created in the former phase. As cited in Yocum (2015), the SAM “attempts to incorporate criteria the authors [Allen and Sites, 2012] noted were mistakes in previous projects” (p. 59). Elements such as allowance for iteration, collaboration, efficiency and efficacy were included in the modern-day SAM. Moreover, the cyclic nature of the SAM further allows for its designers to engage and deliberate on meaningful feedback from all levels and from all parties involved in a timely manner (Glova, 2018, p.2) which further buffers and guides the design process. In the same vein, Fitzgerald (2015) also refers to the cyclical nature of the SAM and mentions feedback as having a major influential role on the final product. “Throughout the process, prototypes are created and tested, and feedback is solicited from stakeholders” (p. 68) and goes on to credit the model’s efficacy in his study. He posits:

Using the SAM process for the module’s development proved to be beneficial, allowing the module’s design to be evaluated and refined continuously. Throughout the module’s development, the feedback received from multiple sources helped shape its practice strategy, screen design, and activities. Many issues found within the module’s preliminary testing were able to be fixed in time for its release to the two participating middle schools. (p. 68) The SAM continues to be one of the go-to instructional design methods whether its use is carded for educational, professional or co-operate purposes as it allows ease of access to create a plurality of prototypes which can be tested and evaluated along the way. Feedback from investors act as guidance and aids in the tweaking process. The model emphasizes the needs of its stakeholders rather than the final product as was the instructional models of yesteryear. Fu (2016) puts the Successive Approximation Model “as a more modern and agile instructional design model focusing on performance needs throughout the process (p. 128). Fitzgerald (2015) also advocates for the continued teaching of the SAM and other similar modern models as this will provide more options to choose from as well as pave the way for contemporary skill sets. He states that the SAM “will provide instructional designers with additional models and resources that can enhance the quality of the courses they design as well as help them to create a course development process that is more efficient and fosters more collaboration and creativity.”


Agudelo, O., & Salinas Ibáñez, J. (2015). Flexible Learning Itineraries Based on Conceptual Maps. Journal of New Approaches in Educational Research, 4(2), 70-76. doi: Allen Interactions. Retrieved from: Brown, T. M. (2016). Instructional design in higher education: Identifying the connection between theory and practice (Order No. 10162671). Available from ProQuest Dissertations & Theses Global. (1830471447). Retrieved from

Brusin, J. (2013). Michael W. Allen. T+D, 67(9), 68–69. Retrieved from Czeropski, S., & Pembrook, C. (2017). E-Learning Ain’t Performance: Reviving HPT in an Era of Agile and Lean. Performance Improvement, 56(8), 37–45. Dodd, B. J. (2013). Toward a theoretical model of decision-making and resistance to change among higher education online course designers (Order No. 3598895). Available from ProQuest Central; ProQuest Dissertations & Theses Global. (1465434145). Retrieved from Fitzgerald, A. T. (2015). Supporting teachers' integration of technology with e-learning (Order No. 1603340). Available from ProQuest Central; ProQuest Dissertations & Theses Global. (1732678082). Retrieved from Fu, I. (2016). The role of instructional design in competency-based education in the united states: A qualitative inquiry in three higher education institutions (Order No. 10296985). Available from ProQuest Central; ProQuest Dissertations & Theses Global. (1847568752). Retrieved from Galagan, P. (2013). Greed for Speed. T+D, 67(5), 22–24. Retrieved from Glova, S. E. (2018). Toward effective facilitation for adult learners: An action research study on the design and delivery of workshops for women business owners (Order No. 10969799). Available from ProQuest Dissertations & Theses Global. (2091410093). Retrieved from

Hart, J. (2018). Nontraditional community college students' motivational regulation in a blended core technology course (Order No. 10785493). Available from ProQuest Dissertations & Theses Global. (2051302716). Retrieved from Tamez, R. (2016). Theoretical Frameworks of Performance-Based Models. Performance Improvement, 55(6), 19–24. Turayev, O. (2018). Educational technology integration among community college instructors (Order No. 10813388). Available from ProQuest Dissertations & Theses Global. (2063075672). Retrieved from Ulrich, R. C. (2017). Creating online training courses for the corporate environment: What have been the experiences of instructional designers? (Order No. 10743483). Available from ProQuest Dissertations & Theses Global. (2008506683). Retrieved from

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