Evaluation of the ADDIE and ARCS-V Models of Instructional Design

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Instructional design models (IDM) act as blueprints for the provision of “effective instructional design to overcome gaps in what is learned due to either instruction, motivation, or resources” (Dousay, 2017, p.425). Since the 1970s, a proliferation of IDM’s has emerged (Molenda, 2015), with two particular models prevailing as arguably the most commonly used in instructional design (ID): the ADDIE and ARCS-V models (Goksu et al., 2017). To determine the effectiveness of both models, this paper aims to evaluate the ADDIE and ARCS-V models against learner performance and perceived contributions to the field of ID. Furthermore, this paper concludes that the ADDIE and ARCS-V models, when used effectively, can lead to significant contributions to the field of ID, and in turn, positively influence learner performance.

Evolved from the instructional systems design (ISD), the ADDIE model’s point of origin is difficult to trace, as there is no original, authoritative version to be revealed (Molenda, 2015). Rather, it is seen as a development paradigm or process, and less as a model (Branch, 2008; Molenda, 2015). Gagne et al. (as cited in Clayton et al., 2006) describe the ADDIE approach as a systematic process that is used to determine training needs, as well as design, develop, and implement training materials and programs, and evaluate training effectiveness. The approach uses a five-phase process which includes analysis, design, development, implementation, and evaluation phases to generate effective ID (Branch, 2008; Clayton et al., 2006).

Created by John Keller and based on the original ARCS model (Keller, 1983), the ARCS-V model was established to account for differences in persistence among learners, a perceived flaw of the original ARCS model (Keller, 2016). By adding a fifth category, volition (Keller, 2008), the ARCS-V model can be used to explain differences among learners and accordingly, utilize motivational and volitional concepts and theories to provide a foundation for effective motivation-based ID (Keller, 2016). Furthermore, the model uses a ten-step systematic process for designing and implementing motivational strategies, and to help guide lesson and module planning (Keller, 2016).

ADDIE provides an excellent pathway to effective ID but does require a certain level of expertise in teaching, learning, and technology. The complexity of ADDIE can slow development (Clayton et al., 2006) and lead to overly complex design stages that are too “pre-determined, linear and inflexible” (Bates, 2015) to handle the volatile learning contexts of the digital era (Allen, 2006). Furthermore, ADDIE is criticized for not providing enough guidance on which methodological practices, including technology used and assessment implementation, to incorporate within that framework (Bates, 2015). Such criticisms question ADDIE’s ability to accommodate an ever-evolving digital education landscape, which in the eyes of many ID experts, is simply not accurate. Allen (2006) states that ADDIE reflects a time-tested and versatile ID process that allows for the successful adaptation and revision of modern ID systems. Furthermore, Branch (2008) explains, “ADDIE is merely a process that serves as a guiding framework for complex situations … appropriate for developing educational products and other learning resources” (p.2). ADDIE’s adaptability, however, is subject to the user’s overall competencies to employ effective ID. For instance, for ADDIE-based ID to satisfy dynamic instructional environments, new instructional technologies, and emerging ID tools, the user’s expertise should feature not only ID, but also media, learning theory, and an array of related subject matter (Allen, 2006). The culmination of relevant expertise enables effective and detail-oriented ADDIE-based ID that addresses learning conditions such as technology implementation and learner needs, thereby contributing greatly to the field of ID.

ADDIE can be an effective tool to generate high levels of learning performance, but each phase must be modified to account for learning conditions. Constructivists argue that the model is too “front-end loaded” and that it privileges a behaviourist’s approach to teaching; meaning, it puts too much emphasis on the content design and development, while giving little emphasis on instructor-student interactions (Bates, 2015). In this light, students would then passively absorb and regurgitate learning material, as opposed to actively engaging in thought-provoking and often problem-centered collaborative activities to acquire new knowledge; however, many ID experts disagree with this line of reasoning. Allen (2006) states that ADDIE is more than a tool for applying behaviorally oriented learning principles to classroom instruction; rather, the process has a proven track record of creating learning systems that result in learners acquiring specified expertise through appropriate provisions. Simply put, ADDIE can accommodate constructivist teaching methodologies as long as the process is modified accordingly. For example, Shelton and Saltsman (2006) explain how the analysis phase can be focused on the learner, their needs, and their learning preferences, which is supported by Olgren’s (1998) thoughts on the goal of education: “if learning is the goal of education, then knowledge about how people learn should be a central ingredient in course design” (p.77). If certain teaching and learning methodologies, such as collaborative tools, assessment analytics, and AI, are required to achieve specific learning outcomes, then ADDIE’s five-step process can be implemented in specified ways to meet these needs, a known tactic that can generate high levels of learner performance (Brown et al., 2015).

At the centre of the ARCS-V model is perhaps both its greatest strength and weakness: the complexities of student motivation. On one hand, learner motivation can be a difficult concept to understand (Ucar & Kumtepe, 2020); for instance, learner motivation will likely fluctuate as varying conditions are introduced throughout a course. Ucar and Kumtepe (2020) explain that it is this complexity that presents a great challenge for those who design through the ARCS-V model. For example, there is no established best practice for evaluating learner motivation, nor is there a standardized approach that can be used to promote learner motivation through ID (Ucar & Kumtepe, 2020). Despite the many challenges this may present for ARCS-V designers, experts agree that a motivation-centered approach to ID can lead to effective learning and teaching design (Keller, 2016; Li & Keller, 2018). Motivation is one of the most influential factors contributing to a learner’s ability to adapt to a new learning environment, reflecting a core element that must be considered in ID (Ucar & Kumtepe, 2020). Keller (2016) points out the plethora of challenges related to managing motivational components of learning environments, particularly the integration of technology and innovative delivery systems, can be remedied through the successful integration of the ARCS-V model with the ISD process, and acknowledges the analysis phase as essential to accommodating “instructionally rich and motivating learning events that are appropriate for a given setting” (p.14).

The ARCS-V model has the potential to improve learner performance, but motivation strategies must account for confounding variables, otherwise, the effectiveness of such strategies will vary (Ucar & Kemtepe, 2020). For example, some studies failed to allocate adequate time for motivation strategies to take effect on learners, while other studies isolated motivation strategies to single learning components, as opposed to integrating them into the entire course; both scenarios result in little to no effect on learner performance (Li & Keller, 2018). Accordingly, the model could be criticized for its inability to support first-time users of ARCS-V, suggesting that effective motivation-based ID may require repeated attempts of the process (Li & Keller, 2018). While this may be true, positive effects on student performance have been reported when each phase of the model is used appropriately. Li and Keller (2016) explain that appropriate motivation strategizing is all about understanding the particularity of the specific group of learners. For instance, an analysis that considers learning conditions, learner preferences and backgrounds, and learning outcome requirements will likely produce relevant insight for effective motivation-based design in accordance with that specific learning group (Li & Keller, 2018; Ucar & Kumtepe, 2020). With such conditions integrated into the ID, research shows motivation strategies can lead to enhanced active learner behaviors (Chen, 2014 as cited in Li & Keller, 2018), improved learning efficiency, increased participation, improved retention, lowered failure rates, and higher completion rates (Li & Keller, 2018). In addition, Ucar and Kemtepe (2020) report positive effects on online learners’ performance, motivation, volition, and course interest.

To be effective, ID through either model must cater to learning conditions and outcome requirements. Both models present unique challenges for practitioners but can be modified to fit any learning need if the user has sufficient expertise to correctly implement the model. In addition, ARCS-V and ADDIE can be combined to form a systematic hybrid approach that delivers quality learning materials and content that is learner-centered, thus creating a well-rounded ID approach. Therefore, the ADDIE and ARCS-V models, when used accordingly, can lead to improved learner performance, thus contributing to the field of ID.




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