Researchers and practitioners have spent the past 60 years attempting to define and create models of design with the intent to improve instruction. As part of a joint, inter-university project, Barson (1967) defined instructional development as the systematic process for improving instruction. While the project report provides a guiding definition to frame and study instructional design (ID), it also cautions that many different conditions influence learning, including the use of media and the hazards of generalizing any sort of model. This caution continues to serve as a limitation on ID practice and research, as evidenced by current and past ID models.

Soon after World Wars I and II, experts in the field recognized that systematic approaches to developing instruction were a popular idea, highlighting that ID methods vary from simple to complex (Twelker et al., 1972). These historical observations predicted the reality where every instructional design project is always unique, with no two projects progressing through the design process the same way. The differences, sometimes subtle while at other times significant, have given way to dozens of different models used with varying popularity in a wide variety of learning contexts.

In the midst of an explosion of models and theories, Gustafson (1991) drafted his first monograph that would become the Survey of Instructional Development Models, now in its fifth edition (Branch & Dousay, 2015). The book provides brief overviews of instructional design models, classifying them within the context of classroom-, product-, and process-oriented instructional problems. Also known as "the pencils book,"  this resource provides a concise summary to help novice instructional designers visualize different design approaches. It also assists more advanced instructional designers with an annotated bibliography on current practice and research. However, this text is just one of many often used in the study and practice of instructional design. Those seeking to expand their knowledge of design processes can learn much from the rich history and theoretical development over decades in the field. (See the Additional Resources section for suggestions.)

In this chapter, we explore a brief history of instructional design models, common components of models, commonly referenced models, and resources and advice for instructional designers as they engage in the instructional design process.

Historical Context

The field of Learning and Instructional Design Technology (LIDT) has experienced many periods of rapid development. Reiser (2001) noted that training programs during World War II sparked the efforts to identify efficient, systematic approaches to learning and instructional design. It would be another 20 years before the first models emerged, but the 1960s and 1970s gave way to extracting instructional technology and design processes from conversations about multimedia development (Reiser, 2017), which in turn produced more than three dozen different instructional design models referenced in literature between 1970 and 2005 (Branch & Dousay, 2015; Gustafson, 1991; Gustafson & Branch, 1997, 2002).

These models help designers, and sometimes educational stakeholders, simplify the complex reality of instructional design and apply generic components across multiple contexts (Gustafson & Branch, 2002), thus creating standardized approaches to design within an organization. However, Molenda (2017) observed that standardization of processes and terminology triggered interest in the field. This presents an interesting relationship between defining the field of instructional design and perpetuating its existence. As designers seek to justify their role in education—whether K-12, higher education, or industry—they often refer to existing models or generate a new model to fit their context. These new models then become a reference point for other designers and/or organizations. This relationship contributes to contentions when teaching and practicing ID.

Industry vs. Academic Expectations

Differences exist between academic instructional design and industry expectations when it comes to how instructional design models are referenced and used. Many introductory instructional design courses introduce students to models and rely on models to guide students through an applied instructional design process. Within these introductory courses, students who are new to the field have an opportunity to navigate the instructional design process in a scaffolded manner led by their instructor.

More specifically, coursework can use models as a mechanism to explore ID aptitudes. Several studies conducted over the last decade examined competencies expected of instructional designers (Ritzhaupt & Kumar, 2015; Ritzhaupt & Martin, 2014). These studies support the need for instructional designers to be knowledgeable of design and commonly recognized design processes. Further, context analysis studies of instructional design job postings reveal that employers expect instructional designers to be knowledgeable about a variety of processes and models such as ADDIE, Agile, SAM, Rapid deployment, Bloom’s taxonomy, ARCS, and Gagne’s Conditions of Learning (Kang & Ritzhaupt, 2015; Klein & Kelly, 2018; Raynis, 2018; Wang et al., 2021). Recognizing that job postings explicitly ask instructional designers to demonstrate knowledge and application of instructional design models, we suggest that instructional design programs prepare students to recognize multiple instructional design models in addition to understanding how to navigate through the instructional design process while using a systemic lens. 

Systemic Nature

In addition to criticisms that ID models do not accurately convey the iterative nature of design, others denounce models for a lack of systemic implications. In a systematic review examining the systemic reach of ID models, Stefaniak and Xu (2020) found that a majority of studies did not address the interrelationships between instructional processes and activities related to instructional design. Rather, applications rely heavily on using a model to guide the design and development of a product. Thus, we must attend to the systemic implications of learning and applying models. 

Systems are dynamic, in nature, and constantly changing as new information and inputs to the system become available (Richardson & Chemero, 2014). Gibbons (2003) argued that the field’s existing ID models focus on the medium rather than the design process. Treating models in this way places emphasis on the product, relegating the steps conveyed within an ID model to prescriptive or procedural-based actions or steps. However, such an approach does not reflect the complexities that exist with systems, including learning design (Gibbons, 2014).

The pencils book emphasizes that models should be used to “serve as conceptual, management, and communication tools” (Branch & Dousay, 2015, p. 23) for supporting instructional design efforts. Thus, we strongly recommend that instructional designers approach the use of ID models by thinking of them as blueprints to frame design activities within the overarching system. By shifting focus on the design process as opposed to the development of a product, instructional designers may be more apt to embrace the iterative nature of design and integrate design decisions that consider the dynamic complexities of the system. 

Process vs. Models

The process of analyzing, designing, developing, implementing, and evaluating (ADDIE) forms the basic underlying procedure (illustrated in Figure 2) that is a distinct component of instructional design regardless of which model is used (Gustafson & Branch, 1997). Branch (2009) said it well when he conceptualized the phases of the ADDIE process as follows:

1. Analyze – Identify the probable causes for a performance gap.
2. Design – Verify the desired performances and appropriate testing methods.
3. Develop – Generate and validate the learning resources.
4. Implement – Prepare the learning environment and engage the students.
5. Evaluate – Assess the quality of the instructional products and processes, both before and after implementation (p. 3).

Notice the use of the term “process” rather than “model.” For ID purposes, we define a process as a series of steps necessary to reach an end result. Similarly, we define a model as the specific instance of a process that can be imitated or emulated. In other words, a model seeks to personalize the generic into distinct functions for a specific context. Thus, when discussing the instructional design process, we often refer to ADDIE as the overarching paradigm or framework by which we can explain individual models. The prescribed steps of a model can be mapped or aligned back to the phases of the ADDIE process.

This discussion might also be facilitated with a business example. Consider the concept of process mapping; it helps organizations assess operational procedures as they are currently practiced (Hunt, 1996). Analytically mapping the process to identify the steps carried out in practice leads to process modeling, an exercise in optimization. In other words, modeling helps move processes to the desired state tailored to the unique needs of an organization. Many businesses of a similar type find that they have similar processes. However, through process modeling, their processes are customized to meet their needs.

The relationship between ADDIE and instructional design models functions much like this business world scenario. As instructional designers, we often follow the same process (i.e., ADDIE). However, through modeling, we customize the process to meet the needs of our instructional context and of our learners, stakeholders, resources, and modes of delivery. Models assist us in selecting or developing appropriate operational tools and techniques as we design.

Modeling the Process

Consider the following examples. The plan, implement, evaluate (PIE) model from Newby, Stepich, Lehman, and Russell (1996) encourages designers to consider how technology assists with instructional design, focusing on the what, when, why, and how. This phase produces an artifact or plan that is then put into action during implementation, followed by evaluating learner performance and instruction effectiveness. During planning, designers work through a series of questions related to the teacher, learner, and technology resources. The questions are answered while also taking into consideration the implementation and evaluation components of the instructional problem. When considered through the lens of the ADDIE process, PIE combines the analyzing, designing, and developing phases into a singular focus area, which is somewhat illustrated by the depiction in Figure 3.

Similarly, the Diamond (1989) model prescribes Phase I “Project Selection and Design” and  Phase II “Production, Implementation, and Evaluation for Each Unit.” Phase I of the Diamond model combines analyzing and designing, while Phase II combines developing, implementing, and evaluating. Robert Diamond placed an emphasis on the second phase of the model by prescribing an in-depth, parallel development system to write objectives, design evaluation instruments, select instructional strategies, and evaluate existing resources. As a result,  new resources consider  previously designed evaluation instruments. The evaluation is again consulted during the implementation, summative evaluation, and revision of the instructional system. These two examples help demonstrate what meaning is behind ADDIE being the general process and models being specific applications. (For further discussion of how aspects of specific models align with the ADDIE process, see Dousay and Logan, 2011.)

ID Models in Practice

Models serve as a source of research questions as we seek to develop a comprehensive theory of instructional development. Rarely are these models tested through a rigorous assessment of their results against predetermined criteria. Rather, ID models with wide distribution and acceptance gain their credibility by being found useful by practitioners who frequently adapt and modify them to match specific conditions (Branch & Dousay, 2015, p. 24). Thus, popularity serves as a form of validation for these design models. However, a wise instructional designer knows when to use, adapt, or create a new model of instructional design to fit their purposes.

Because there are so many different ID models, how do we choose which one to use? In framing this conversation, the Survey of ID Models (Branch & Dousay, 2015) serves as a foundation, but by no means should be the sole reference. A total of 36 different instructional design models (see Table 1 for a summary) have been covered in the pencils book since its first edition, and this list does not include every model created or used. Still, this list of models is useful in providing a concise guide to some of the more common approaches to instructional design.

Determining which ID model to use might best be decided by taking into account a few factors. First, what is the anticipated delivery format? Will the instruction be synchronous online, synchronous face to face, asynchronous online, or some combination of these formats? Additionally, some models are better tailored for online contexts, such as Dick and Carey (1978); Bates (1995); Dabbagh and Bannan-Ritland (2004); or Morrison, Ross, Kemp, Kalman, and Kemp (2012).

Another way to think about how to select a model involves accounting for the context or anticipated output. Is the instruction intended for a classroom? In that case, consider 3PD (Sims & Jones, 2002); 4C/ID (van Merriënboer, 1997); ASSURE (Heinich et al., 1982); Briggs (1970); DeCecco (1968); Dick & Reiser (1989); Gerlach & Ely (1971); learning systems design (Davis et al., 1974); Morrison, Ross, Kemp, & Kalman (Kemp, 1977); PIE (Newby et al., 1996); or UbD (Wiggins & McTigue, 2000). Perhaps the instructional context involves producing an instructional product handed over to another organization or group. In this case, consider Agile (Beck et al., 2001); Baker & Schutz (1971); Banathy (1968); Bates (1995); Bergman & Moore (1990); CASCADE (Nieveen, 1997); de Hoog, de Jong, & de Vries (1994); Leshin, Pollock, & Reigeluth (1992); or Van Patten (1989). Lastly, perhaps your context prescribes developing a system, such as a full-scale curriculum. These instructional projects may benefit from the courseware development process (Control Data Corporation, 1979); culture based model (Young, 2008); Diamond (1989); Dick, Carey, & Carey (Dick & Carey, 1978); Gilbert (1978) front end analysis; Instructional Development Institute (Twelker et al., 1972); ILDF (Dabbagh & Bannan-Ritland, 2004); IPISD (Branson et al., 1975); IPDM (Gentry, 1993); ISD model 2 (Seels & Glasgow, 1997); layers of necessity (Tessmer & Wedman, 1990); pebble in the pond (Merrill, 2002); rapid collaborative prototyping (Dorsey et al., 1997); or Smith & Ragan (1993) models.

Conclusion

This chapter synthesizes nearly 60 years of practice and study of ID models. From K-12 to higher education and industry, dozens of models continue to guide efforts that systematically design learning. However, when learning and practicing ID, it is easy to become constrained by models or lose sight of their role in systemic applications. Thus, we strongly recommend developing ID competencies indicative of design processes. Deciding which model to use does not need to be a cumbersome or overwhelming process. So long as a designer can align components of an instructional problem with the priorities of a particular model, they will likely be met with success through a systems thinking approach.

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Additional Resources

The following resources represent a broad collection of discussion, debate, and research in the field of learning and instructional design. The list has been compiled from resources such as the Survey of Instructional Design Models (Branch & Dousay, 2015), reading lists from graduate programs in LIDT, and publications sponsored by the Association for Educational Communications & Technology. However, the list should not be considered exhaustive. It is provided here as a starting point for individuals or organizations seeking to learn more about the field and how models are developed and implemented.

1. Altun, S., & Büyükduman, F. İ. (2007). Teacher and student beliefs on constructivist instructional design: A case study. Educational Sciences: Theory & Practice, 7(1), 30–39.

2. Angeli, C., & Valanides, N. (2005). Preservice elementary teachers as information and communication technology designers: An instructional systems design model based on an expanded view of pedagogical content knowledge. Journal of Computer Assisted Learning, 21(4), 292–302. http://doi.org/10.1111/j.1365-2729.2005.00135.x

3. Carr-Chellman, A. A. (2015). Instructional design for teachers: Improving classroom practice. New York, NY: Routledge.

4. Cennamo, K. S. (2003). Design as knowledge construction. Computers in the Schools, 20(4), 13–35. http://doi.org/10.1300/J025v20n04_03

5. Chongwony, L., Gardner, J. L., & Tope, A. (2020). Instructional design leadership and management competencies: Job description analysis. Online Journal of Distance Learning Administration, 23(1), 1–18.

6. Costa, J. M., Miranda, G. L., & Melo, M. (2021). Four-Component Instructional Design (4C/ID) model: A meta-analysis on use and effect. Learning Environments Research. https://doi.org/10.1007/s10984-021-09373-y

7. Dirksen, J. (2016). Design for how people learn (2nd ed.). San Francisco, CA: New Riders.

8. Dong, H. (2021). Adapting during the pandemic: A case study of using the rapid prototyping instructional system design model to create online instructional content. Journal of Academic Librarianship, 47(3), 1–9. https://doi.org/10.1016/j.acalib.2021.102356

9. Fox, E. J. (2006). Constructing a pragmatic science of learning and instruction with functional contextualism. Educational Technology Research and Development, 54(1), 5–36. http://doi.org/10.1007/s11423-006-6491-5

10. Gagné, R. M., Wager, W. W., Golas, K. C., & Keller, J. M. (2004). Principles of instructional design (5th ed.). Boston, MA: Cengage Learning.

11. Gibbons, A. S. (2013). An architectural approach to instructional design. New York, NY: Routledge.

12. Hannafin, M. J. (2006). Functional contextualism in learning and instruction: Pragmatic science or objectivism revisited? Educational Technology Research and Development, 54(1), 37–41. http://doi.org/10.1007/s11423-006-6492-4

13. Hokanson, B., & Gibbons, A. (Eds.). (2014). Design in educational technology: Design thinking, design process, and the design studio. New York, NY: Springer International Publishing. http://doi.org/10.1007/978-3-319-00927-8

14. Hoogveld, A. W. M., Paas, F., Jochems, W. M. G., & van Merriënboer, J. J. G. (2002). Exploring teachers’ instructional design practices from a systems design perspective. Instructional Science, 30(4), 291–305. http://doi.org/10.1023/A:1016081812908

15. Jonassen, D. H. (2006). On the role of concepts in learning and instructional design. Educational Technology Research and Development, 54(2), 177–196. http://doi.org/10.1007/s11423-006-8253-9

16. Jonassen, D.H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. Routledge. 

17. Koper, R., Giesbers, B., van Rosmalen, P., Sloep, P., van Bruggen, J., Tattersall, C., … Brouns, F. (2005). A design model for lifelong learning networks. Interactive Learning Environments, 13(1–2), 71–92. http://doi.org/10.1080/10494820500173656

18. Laporte, L., & Zaman, B. (2018). A comparative analysis of programming games, looking through the lens of an instructional design model and a game attributes taxonomy. Entertainment Computing, 25, 48–61. https://doi.org/10.1016/j.entcom.2017.12.005

19. Larson, M. B., & Lockee, B. B. (2019). Streamlined ID: A practical guide to instructional design. Routledge.

20. Lee, C.-J., & Kim, C. (2017). A technological pedagogical content knowledge based instructional design model: A third version implementation study in a technology integration course. Educational Technology Research and Development, 65(6), 1627–1654. https://doi.org/10.1007/s11423-017-9544-z

21. Lee, J., Lim, C., & Kim, H. (2017). Development of an instructional design model for flipped learning in higher education. Educational Technology Research and Development, 65(2), 427–453. https://doi.org/10.1007/s11423-016-9502-1

22. Magliaro, S. G., & Shambaugh, N. (2006). Student models of instructional design. Educational Technology Research and Development, 54(1), 83–106. http://doi.org/10.1007/s11423-006-6498-y

23. Nadolski, R. J., Kirschner, P. A., van Merriënboer, J. J. G., & Wöretshofer, J. (2005). Development of an instrument for measuring the complexity of learning tasks. Educational Research and Evaluation, 11(1), 1–27. http://doi.org/10.1080/13803610500110125

24. Richey, R. C., Klein, J. D., & Tracey, M. W. (2010). The instructional design knowledge base: Theory, research, and practice. New York, NY: Routledge.

25. Salter, D., Richards, L., & Carey, T. (2004). The “T5” design model: An instructional model and learning environment to support the integration of online and campus-based courses. Educational Media International, 41(3), 207–218. http://doi.org/10.1080/09523980410001680824

26. Sims, R. (2014). Design alchemy: Transforming the way we think about learning and teaching. New York, NY: Springer International Publishing. http://doi.org/10.1007/978-3-319-02423-3

27. Smith, P. L., & Ragan, T. J. (2004). Instructional design (3rd ed.). Hoboken, NJ: John Wiley & Sons, Inc.

28. Song, H.-D., Grabowski, B. L., Koszalka, T. A., & Harkness, W. L. (2006). Patterns of instructional-design factors prompting reflective thinking in middle school and college-level problem-based learning environments. Instructional Science, 34(1), 63–87. http://doi.org/10.1007/s11251-005-6922-4

29. Stefaniak, J.E., & Reese, R.M. (2022). The instructional design trainer’s guide: Authentic practices and considerations for mentoring ID and ed tech professionals. Routledge. 

30. Stubbs, M., Martin, I., & Endlar, L. (2006). The structuration of blended learning: Putting holistic design principles into practice. British Journal of Educational Technology, 37(2), 163–175. http://doi.org/10.1111/j.1467-8535.2006.00530.x

31. van Berlo, M. P. W., Lowyck, J., & Schaafstal, A. (2007). Supporting the instructional design process for team training. Computers in Human Behavior, 23(3), 1145–1161. http://doi.org/10.1016/j.chb.2006.10.007

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