The Attention, Relevance, Confidence, Satisfaction, and Volition (ARCS-V) Model

Keller (1987) developed the ARCS model to address the unpredictability of student motivation through a systematic motivational design (MD) process that recommends practical strategies for key motivational components. These components of student motivation—attention, relevance, confidence, and satisfaction—were based on research into human motivation and expectancy-value theory (Keller, 1987) which suggests that people are motivated by their level of confidence in success (expectancy) and the weight they put on their goal (value) (Keller, 2010). Later, Keller (2016) found his model lacked consideration into how some learners persisted while others did not and expanded the model to include volition: a student’s ability to self-regulate to avoid distraction from their task. Fundamentally, the ARCS-V model aims to aid in building or adapting teaching environments to sustain student attention through curiosity, personal relevance, confidence that success is possible, the potential for satisfaction in their accomplishment, and support to keep students committed to their work.

Rather than being a standalone design model, ARCS-V is a motivational design (MD) model that works in partnership with an instructional design (ID) process or as an aid in reflecting on or analyzing existing courses. In many cases, ARCS-V is done in 10 steps that can run parallel to those of another ID model, with key steps relating to the predictive analysis of audience motivation, listing potential strategies, and selecting the most appropriate tactics (Keller, 2010). During analysis, a designer will work to predict audience motivation for each motivational category and its subcategories to reveal areas of need. Next steps involve identifying motivational objectives, defining methods of measurement, and conceiving potential strategies to improve motivation. Finally, strategies will be selected and integrated into instructional material. Without the model, ID processes may focus too much on developing instruction without consideration of what can be done to improve motivation, something that may build curiosity and meaningful discussion within learning environments.

While the ARCS-V model is a flexible tool with a range of potential applications, it is not intended to be a standalone ID model and can have limited effect if cultural differences around motivation are not addressed. Studies have shown that the ARCS-V model improves student motivation and completion rates (Li & Keller, 2018), and the model has been adapted for time-sensitive projects by focusing on the steps of assessing gaps and designing solutions to meet both student and instructor motivational needs (Keller, 2016). While it is flexible enough to help in both creating and improving courseware, its focus on motivation means that new design projects require ARSC-V to be integrated into another ID process, a few examples of which are offered by Keller (Keller, 2010). Additionally, designer’s must be aware of cultural differences as they construct strategies—such as motivation through individual achievement or responsibility to society (Simsek, 2014)—otherwise their efforts in improving motivation may prove ineffective (Li & Keller, 2018). Ultimately, ARCS-V is both impactful and flexible, but designers and instructors must remember that it cannot serve as a panacea for student motivation.

Pebble-In-The-Pond (PITP) Model

Merrill (2002a) developed the Pebble-in-the-Pond (PITP) model out of his view that ID models often focus on instructional approaches rather than foundational learning principles and the establishment of the instructional goal early in the process. After years of research, Merrill (2002b) synthesized the commonalities between the range of instructional design theories, resulting in the first principles of instruction. These principles state that learning increases as learners activate previously acquired knowledge, have new knowledge demonstrated to them, apply that newly acquired knowledge, and integrate it into their unique context via real-world problems. The ability for each student to be able to solve real-world, task-centered problems underpins the PITP model through the application of these first principles to the instructional strategy (Merrill, 2002a).

While some ID models attempt to create whole new processes (Brown & Green, 2018), PITP is intended to be performed during the design and development phases of a traditional ID process—such as ADDIE—and begins with the establishment of a real-world problem (Merrill, 2002a). Identifying the problem is the first of five “ripples,” or stages, that grow in complexity throughout the design process. The next two ripples have the designer identify skills that must be mastered and the parts of each skill that must be taught; followed by defining a strategy for instruction; and concluding in the production of the educational end product. These ripples should expand with fluidity from the central goal, allowing one ripple to flow smoothly into the next, and designers may also feel free to move back to previous ripples as necessary to refine the strategy (Gardner & Jeon, 2009). If designers follow the first principles of design throughout this process, students will benefit from authentic, task-oriented instructional activities and assessments that build their confidence and abilities.

The PITP model’s focus on a core task or problem may reduce scope creep and its flexibility means it functions well as an add-on to ID models or processes already in use, but that flexibility makes it unsuitable as a replacement for more robust ID models. By targeting the model around a single real-world task, designers are encouraged to avoid superfluous details and irrelevant activities which do not lead the student closer to accomplishing their task. As Gardner & Jeon (2009) point out, designers may “wander from the task” (p. 30) and a task-based model can help keep a project focused on its core needs. In addition, the lack of steps relating to either analysis or evaluation allow a designer to adapt the model to their current process, introducing a focus on students solving real-world problems and tasks. While these missing steps make the model unsuitable as a stand-alone ID model for course development, it is a powerful model for providing deeper focus for new ID projects as well as evaluating and updating existing courseware.

References

Brown, A. H., & Green, T. D. (2018). Beyond teaching instructional design models: Exploring the design process to advance professional development and expertise. Journal of Computing in Higher Education, 30(1), 176–186. https://doi.org/10.1007/s12528-017-9164-y

Gardner, J., & Jeon, T. (2009). Creating Task-Centered Instruction for Web-Based Instruction: Obstacles and Solutions. Journal of Educational Technology Systems, 38(1), 21–34. https://doi.org/10.2190/ET.38.1.c

Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2. https://doi.org/10.1007/BF02905780

Keller, J. M. (2010a). The Arcs Model of Motivational Design. In J. M. Keller (Ed.), Motivational Design for Learning and Performance: The ARCS Model Approach (pp. 43–74). Springer US. https://doi.org/10.1007/978-1-4419-1250-3_3

Keller, J. M. (2010b). The Study of Motivation. In J. M. Keller (Ed.), Motivational Design for Learning and Performance: The ARCS Model Approach (pp. 1–19). Springer US. https://doi.org/10.1007/978-1-4419-1250-3_1

Keller, J. M. (2016). Motivation, Learning, and Technology: Applying the ARCS-V Motivation Model. Participatory Educational Research, 3(2), 1–15. https://doi.org/10.17275/per.16.06.3.2

Li, K., & Keller, J. M. (2018). Use of the ARCS model in education: A literature review. Computers & Education, 122, 54–62. https://doi.org/10.1016/j.compedu.2018.03.019

Merrill, M. D. (2002a). A pebble-in-the-pond model for instructional design. Performance Improvement, 41(7), 41–46. https://doi.org/10.1002/pfi.4140410709

Merrill, M. D. (2002b). First principles of instruction. Educational Technology Research and Development, 50(3), 43–59. https://doi.org/10.1007/BF02505024

Simsek, A. (2014). Interview with John M. Keller on Motivational Design of Instruction. Contemporary Educational Technology, 5(1), 90–95. https://eric.ed.gov/?id=EJ1105558