Keller’s ARCS Model of Motivational Design
Aimee Boyer McCandless
By the end of this chapter, you will be able to:
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- Define the terms: theory and model
- Identify the four original components of Keller’s ARCS model
- Identify at least one method that can be used to address each of the four original components of Keller’s ARCS model
- Identify the four phases of Keller’s ten-step design process
Introduction to Keller’s ARCS Model of Motivation
Developed and revised by John M. Keller, the ARCS Model of Motivational Design is a two-part framework for influencing learner motivation and improving learning outcomes. The first part of the ARCS model, Attention, Relevance, Confidence, and Satisfaction, states the principles for evaluating how the instruction affects learner motivation and is a problem-solving template for gaining learner motivation. The second part of the model includes a 10-step design process, building awareness of and techniques to influence learner motivation into the instruction from the outset.
During the time that Keller began developing ARCS, instructional design was heavily influenced by both behavioral and cognitive-learning psychology, hence much attention was given to the utility of rewards, and how to organize content according to a learner’s cognitive style, respectively (Keller, 1979). While these are valuable, Keller recognized an unaddressed yet vital factor – the learner’s motivation to accomplish a task. Theories that addressed aspects of motivation including, for example, expectancy-value, addressed strategies for modifying the learner’s motivational profile, rather than designing instruction that was motivating (Keller, 1979).
Prior to Keller’s work on motivation, designing instruction was akin to trying to build a bridge without a blueprint. While learning theories were helpful for conceptualizing mechanisms of learning, they provided no paradigm for either understanding or influencing learner motivation. Instructional design models largely ignored the learner, favoring a focus on environmental stimuli. ARCS was the first model to address the motivation of the learner as a factor in the learning process, bringing a new realm to the world of instructional design by providing a template for designing and improving instruction that, when utilized well, reliably increases learner motivation.
ARCS has been used successfully to improve motivation, retention, and learner outcomes in many countries (Austria, Ireland, Japan, Mozambique, and the USA, to name a few) and in e-learning, hybrid, online, asynchronous, and traditional learning environments (Keller & Suzuki, 2004)
The 5-minute video below provides a quick overview of Keller’s ARCS model.
Origins of the ARCS Model
As an Associate Professor of Instructional Design in the 1970s, Keller recognized and sought to build the missing links in instructional design, beginning with vast research of a variety of underlying motivational constructs, and a synthesis of the prevailing educational and educational psychology-related theories of the time. His goal was to develop a motivational design that could systematically produce replicable results over time in improving a learner’s motivation to learn, and instructional designers today are grateful for his success.
Keller created the ARCS Model of Motivational Design in 1987 (Keller, 1987), after publishing journal articles developing the concepts in 1979 and 1983 (Keller, 1979 and 1983). His work, which he described as a “macro-theory,” provided, for the first time, a theoretical basis for understanding and influencing learner motivation (Francom, 2010), and a blueprint to routinely improve a learner’s motivation by focusing on Attention, Relevance, Confidence, and Satisfaction (ARCS). This blueprint is routinely used today, and Keller’s updates to this model have continued to impact and improve instructional design practices.
Explore a timeline of the development and updates of the ARCS Model, below.
Comparison with Other Theories
Before proceeding in our comparison of Keller’s ARCS model with other theories, it is important to define two key terms, theory and model, to ensure clarity. ‘Theory’ is related to perception and can be understood as a set of ideas or an intelligible explanation of facts, events, or of the principles and methods of a science or art, which is based on observation and reasoning (Harper, nd). The term ‘model,’ refers to manner or measure, and is a standard for imitation, pattern, or template (Harper, nd).
Take a moment to check your understanding of these terms.
Keller’s ARCS model has elements of theory, for it was indeed born out of research, or the ‘pursuit of knowledge through observation,’ and informs us regarding the principles and methods of the science of learning; however, it is also a model – a standard serving as a pattern or template. ARCS gives us a template for designing instructional material with the ability to influence motivation.
Keller synthesized many theories in developing the ARCS model, and was influenced by behaviorism, cognitivism, and humanistic and personality psychology. One of his students, Dr. Bernie Dodge, created the illustration below, showing the theorists and schools of thought that were foundational in developing the ARCS model (Waddington and Dell, 2018).
Click the hotspots to explore the theorists who influenced the ARCS model.
The “Motivation Island” map you see below was updated by Waddington and Dell in 2018 to reflect the impact of emotions on motivation, and the influences of Kuhl and Zimmerman.
Click on the three hotspots below to learn more.
Fundamental Tenets of the Model
Because motivation is complex and multidimensional, the ARCS model, under the basic assumption that it is possible to affect motivation, provides strategies for a problem-solving process of creating and sustaining student motivation. Like the traditional instructional design process, motivational design includes a systematic process of:
- Identifying goals and motivational problems (Keller’s steps 1-4, the “Analyze” phase)
- Developing strategies for goal attainment and addressing motivational problems (Keller’s steps 5-8 and step 9, the Design and Develop phases, respectively), and
- Evaluating the outcome of the strategies (Keller’s step 10, the “Evaluate” phase).
In all settings, the purpose of motivation strategies is to help learners form the impression that success is possible if an effort is exerted (Keller, 1987). These strategies are used to address each of the four model components – attention, relevance, confidence, and satisfaction. They are not meant to take away from the course objectives and should be aligned with the course delivery format and the instructor’s style. Ideally, strategies should create a gap between what the learners know and what they need to know (Keller 2017). Instructional methods such as the flipped classroom, problem-based learning, and project-based learning help to address this gap.
According to Keller (1983), the ARCS Model of Motivational Design suggests that an instructional designer can routinely improve a learner’s motivation to learn by focusing on Attention, Relevance, Confidence, and Satisfaction. [Image Description]
As we examine the four main components of ARCS, watch for the blue textboxes with examples of ARCS implementations for each component, beginning with the context for each scenario, below.
Scenario Context
We’ll follow two instructional designers as they implement elements of the ARCS Model to improve their instruction; one is set in higher education, the other in a corporate setting.
- Example A – Dr. Martinez
Dr. Martinez, a university physics professor, noticed that students in her introductory physics course were disengaged, struggling to see the relevance of theoretical concepts, and lacking confidence in applying formulas to real-world problems. To address this, she redesigned the course using Keller’s ARCS Model as a guide.
- Example B – Alex, Corporate Instructional Design
As an instructional designer for a Fortune 500 company, Alex is tasked with redesigning the employee cybersecurity training. The previous version was text-heavy, compliance-driven, and received poor engagement. Many employees found the content dull and struggled to retain key security protocols.
Attention
A learner’s attention is required before any learning can take place. Consequently, the first step in instructional design for motivation is to capture and sustain the learners’ attention. Keller (2010) describes three categories of attention-getting strategies: perceptual arousal, inquiry arousal, and variability. Perceptual arousal refers to capturing interest by arousing learners’ senses and emotions and is usually transitory. One of the most common ways to provoke perceptual arousal is making an unexpected change in the environment; example tactics include a change in light, a sudden pause, and presenting a video after text-based information in an online learning environment. Inquiry arousal, similar to maintaining situational interest, refers to a cognitive level of curiosity. Students are cognitively attracted to learning materials, for instance, when they contain paradoxical facts. Variability concerns variation in instructional methods. No matter how effective motivational tactics are, they lose their potency when used unvaryingly.
Methods to Attract Attention
Discover techniques to attract attention by clicking each category.
Scenario: Attention
- Example A – Dr Martinez
To capture students’ interest, she began each module with a short, high-impact video featuring real-world physics phenomena (e.g., astronauts experiencing microgravity on the ISS) and posed open-ended questions like, “What would happen if you tried to pour a glass of water in zero gravity?”
- Example B – Alex, Corporate Instructional Design
Alex introduced interactive simulations and gamification elements, such as a “Cybersecurity Escape Room” where employees had to identify security threats to advance. Real-world hacking scenarios were embedded into the training, challenging employees to spot vulnerabilities.
Try the challenge below to see if you can remember ways to attract attention; drag the boxes to the correct category.
Relevance
The second category, relevance, refers to making the learning experience personally relevant or meaningful. According to the goal theory, students engage in learning activities that help to attain their goals (Locke & Latham, 1984). Also, as described in expectancy-value theory and self-determination theory, the perceived value of the task is a critical antecedent of motivation (Deci & Ryan, 2000; Wigfield & Eccles, 1992). One way to establish the perceived relevance of the learning materials is to use authentic or real-world examples and assignments. Simply relating the instruction to what is familiar to learners (e.g., prior knowledge) can also help learners to perceive its relevance. Learners need to understand how the instruction will benefit them both now and in the future. See the accordion below for more examples of how to relate relevance to increase learner motivation.
Methods to Relate Relevance
Click each category to discover strategies that relate relevance.
Scenario: Relevance
- Example A – Dr. Martinez
She connected physics concepts to careers and daily life, inviting guest speakers from NASA and engineering firms to discuss how they use physics in their work. Assignments were framed around solving real-world problems, such as designing a safe bike helmet using knowledge of force and momentum.
- Example B – Alex, Corporate Instructional Design
Instead of generic cybersecurity warnings, the training featured job-specific case studies. For instance, the finance department learned about phishing scams targeting financial transactions, while the IT team tackled real-time threat detection scenarios.
Remember how to relate relevance to your audience? Drag the box to the correct category to see!
Confidence
The confidence category is pertinent to self-efficacy and expectancies for the success of the expectancy-value theory. According to self-determination theory, the feeling of competence is one of the basic human needs (Ryan & Deci, 2000), so the goal is to organize instruction in such a way that the learner feels they have the skill and ability to accomplish the task or understand the content. If a learner perceives the content as being too difficult, they may not even try to learn the content, or they may not try their hardest because they expect to fail. Building confidence can be a delicate and careful affair, challenging the person to stretch themselves, but not so far that they are doomed to failure.
Strategies to enhance self-efficacy, such as scaffolding and experiencing success, can be applied to build confidence. Another way to enhance confidence is to foster learners’ belief that they have control over their performance. Autonomy support such as providing choices is an example.
Methods to Cultivate Confidence
Scenario: Confidence
- Example A – Dr. Martinez
Dr. Martinez scaffolded learning by incorporating low-stakes practice quizzes with immediate feedback, step-by-step problem breakdowns, and peer collaboration, before students tackled more complex assignments. She also highlighted past students’ success stories to build self-efficacy.
- Example B – Alex, Corporate Instructional Design
Using adaptive learning pathways, the training is now allowing employees to practice skills at their own pace. Immediate feedback on security quizzes helped reinforce learning, while an optional “cyber coach” chatbot provided on-demand guidance. Employees received incremental challenges that built from basic to advanced security protocols.
Check your learning – can you remember how to cultivate confidence? Drag each box to the correct category.
Satisfaction
Learning must be rewarding or satisfying in some way, whether it is from a sense of achievement, praise from a higher-up, or mere entertainment. Structuring a course so that participants utilize learned skills in real-world settings applicable to the learners is one method for encouraging intrinsic motivation; others include learners experiencing a feeling of mastery, the pleasure of accomplishing a challenging task, and helping the learners attribute their successes to their effort. High grades, certificates, and other tangible rewards are the most common extrinsic outcomes. However, for example, if a student receives a high score on a final exam because the test was extremely easy, an extrinsic reward may feel undeserved and therefore the extrinsic reward may not result in feelings of satisfaction. Satisfaction can also come from achievement, praise, and utilizing learned skills in everyday life.
Methods to Support Satisfaction
Scenario: Satisfaction
- Example A – Dr. Martinez
Students applied their knowledge in a culminating project where they designed and tested a simple crash safety system using physics principles. Public recognition came through a “Physics Showcase” where the best designs were featured, reinforcing a sense of accomplishment.
- Example B – Corporate Instructional Design
A point-based leaderboard and digital badges incentivized participation. Employees who completed the training received gift cards to a local coffee shop next to the office and recognition in company newsletters; high scorers were invited to participate in a live “Cyber Defense Challenge,” fostering motivation and long-term engagement.
Test yourself on ways to support learner satisfaction by dragging the box to the correct category.
Scenario: Outcomes
- Example A – Dr. Martinez
By incorporating the ARCS model, Dr. Martinez saw increased student engagement, a lower attrition rate, better concept retention, and a noticeable improvement in students’ confidence with problem-solving. Course evaluations reflected greater satisfaction, particularly in how applicable the material felt to students’ future careers.
- Example B – Corporate Instructional Design
Post-training assessments showed a 40% reduction in security violations, and employees reported feeling more confident in recognizing cyber threats. Engagement metrics improved, with completion rates rising from 55% to 92%. The company also saw increased enthusiasm for continuous learning, leading to the development of an advanced cybersecurity training track.
ARCS Model Updates
Although the original ARCS version remains heavily cited, Keller continued updating the model, developing later versions ARCS-V (2007) and MVP (2008). Each of the versions has stayed true to the belief that motivation is a complex and dynamic phenomenon which includes facets such as meta-cognition, desire, and persistence.
The MVP model (Motivation, which encapsulates all of ARCS, Volition, Performance) was formed to include performance as well as cognitive and emotional components that show a relationship between motivation, learning, and performance (Keller 2017). These updates recognize that even when motivation is high, stress and emotion can inhibit learning and therefore performance.

Volition is the action and attitudes that contribute to the effort persistence needed to help learners reach their goal, and aligns with other learning constructs such as mindset, resilience, and grit. Research on grit has been positively correlated with a higher purpose of learning (Angelo, 2017). Mindset is related to a student’s belief about their own intelligence and capacity to learn, which correlates to the confidence component of the ARC model. Because this is not part of the original model, we refrain from covering this in-depth here; however, it is a factor well-worth further study.
Strengths
Keller’s ARCS model of motivation is a template for using theory to craft instruction. Gleaning much from well-established theory and research, rather than just pointing out the value of learner motivation or drawing attention to the results (or lack thereof) when motivation is low, ARCS brings an entire framework of solutions to the table. The structured process and tangible tools offered by ARCS can be used in influential ways to help improve even intrinsic motivation. Research has proven time and again that the ARCS model is effective in vastly varied learning environments, from kindergarten to graduate school, the classroom to fully online instruction, and corporate training to healthcare as well as in many cultures around the world.
Online learning environments, specifically, require students to have a significant amount of self-direction and perseverance to be successful. In the ARC- MVP model these are aligned with volition. For cognitive presence to be established, relevance, reflection, and metacognitive processes need to be developed. There is also a significant need for feedback to guide students as they learn. Concerning teaching presence, the instructional design strategies and course planning components are key, and the strategies provided in the ARC models can support successful course delivery. Even in varied and challenging learning environments, ARCS can be applied to influence motivation resulting in increased student retention,
Limitations
Implementing parts of the ARCS model, such as tailoring instructive elements and individual feedback, can potentially be time-intensive, so instructors should factor this into the overall course, choosing the best use of the desired elements given the time they choose to devote to it. Reynolds et al (2017) point out that while motivation and learning are often looked at together, the presence of motivation does not guarantee that learning will occur. Although ARCS has been successfully applied in traditional, e-learning, distance learning, hybrid, online, and asynchronous environments, the complexity of learning environments has increased since the original design of the ARCs model. Variance in student demographics, multiculturalism, and student learning strategies may play a role in its currency in years to come. In addition, the rate at which technology increases and infiltrates learning environments may necessitate further updates to the strategies originally provided.
In a comprehensive literature review, Li and Keller (2018) indicated several areas for future research, including:
- Design-based research approach to motivation as opposed to experimental design
- Investigation of cognitive and psychological factors as they impact motivation
- Motivation in fully online learning environments
- Impact of cultural considerations on learner motivation
- Increased length of experimental time of studies, more longitudinal
Instructional Design Implications: A Systematic Design Process
Besides identifying the four major categories of motivational design, the ARCS model describes 10 steps for a systematic process of motivational design (Keller, 2010). Steps one through four correspond to the Analyze phase in the traditional instructional design process, steps five through eight are part of the Design phase, with steps nine and ten making up the Development and Evaluation phases.
Click on the arrows next to each design phase to discover the 10 steps of Keller’s design process.
Analyze
1. Acquire course information
2. Acquire audience information
3. Analyze your audience’s current level of motivation
4. Analyze motivational tactics in existing materials
(Use these first 4 steps to identify motivational challenges)
Design
5. Describe motivational goals and assessment methods – determine the motivational behaviors of learners that one would like to observe based on the motivational problems identified in the previous steps
6. Identify potential tactics
7. Design tactics
8. Integrate motivational tactics with instructional plans, redesigning as necessary
Develop
9. Develop materials
Evaluate
10. Evaluate student reactions
Example: 10-Step Design Process
Case Study 1: Designing an Online Course on Time Management (College Professor)
Step 1: Acquire Course Information
- Context: A college professor teaching an online course for first- and second-year students, focusing on time management. The course is aimed at helping students manage academic workloads alongside personal commitments.
- Course Topic: Time Management Strategies for College Students.
- Delivery Mode: Fully online, asynchronous, with interactive components such as quizzes, videos, and discussion forums.
Step 2: Acquire Audience Information
- Audience: First-year college students, ranging from 18 to 22 years old.
- Motivational Challenges: Many students are overwhelmed by their academic responsibilities and struggle with balancing schoolwork and personal life, leading to stress and time mismanagement.
Step 3: Analyze Your Audience’s Current Level of Motivation
- Current Motivation: Students are motivated to succeed academically but many lack confidence in their ability to manage time effectively. The transition from high school to college increases stress, leading to feelings of being overwhelmed and disorganized, in addition to the challenges they are experiencing due to a lower level of supervision, which many had relied upon in high school to support them in time management.
Step 4: Analyze Motivational Tactics in Existing Materials
- Existing Materials: The professor previously used time management articles and simple videos. However, students found them to be too general and not closely related to their personal struggles.
- Motivation Analysis: The current materials don’t actively engage students in problem-solving or offer immediate tools for real-life application, which impacts motivation.
Step 5: Describe Motivational Goals and Assessment Methods
- Motivational Goals:
- Attention: Capture students’ attention by relating course content to their personal academic challenges.
- Relevance: Ensure content is directly related to their daily experiences with time management issues.
- Confidence: Provide students with step-by-step guides and small wins to boost their confidence in time management.
- Satisfaction: Offer incentives like completion badges, certificates, and peer feedback to make students feel accomplished.
- Assessment Methods: frequent low-stakes quizzes with unlimited attempts to encourage learning, peer-reviewed assignments on creating personal schedules, and a final project submission of a personal time management plan.
Step 6: Identify Potential Tactics
- Tactics:
- Use engaging videos that feature students discussing their time management struggles and how they overcame them.
- Introduce a time tracker tool for students to implement the strategies and track their progress.
- Provide real-world, relatable examples of effective time management strategies.
Step 7: Design Tactics
- Attention: Begin with a relatable video featuring a first-year student discussing their stress due to poor time management and how the course content will help them.
- Relevance: Provide case studies that students can relate to—balancing study sessions with extracurricular activities or part-time work. Students will submit to the instructor statements with examples of time management struggles they experience. Instructor will remove identifying characteristics, and students will randomly be assigned one of these struggles. Three students each week will present a time management struggle to the class discussion forum, and the class will offer additional ideas for possible solutions.
- Confidence: Develop a “weekly time audit” where students track their time each week. This tool will guide them to see where they can improve and help them break tasks down into manageable steps.
- Satisfaction: Offer digital badges for completing each section of the course and a certificate upon completion, reinforcing accomplishment.
Step 8: Integrate Motivational Tactics with Instructional Plans, Redesigning as Necessary
- Tactics are embedded in the instructional plan with specific tasks to enhance motivation. If feedback shows that students are disengaged in quizzes, the professor might introduce gamified assessments or interactive activities to maintain attention.
Step 9: Develop Materials
- Materials: Create instructional videos, downloadable worksheets for time tracking, quizzes with immediate feedback, and a discussion forum for sharing personal experiences and progress.
- Include motivational components such as progress tracking, achievement badges, and personalized feedback.
Step 10: Evaluate Student Reactions
- Evaluation: After the course, the professor will distribute a survey asking students to rate the course’s impact on their time management skills and confidence. They will also assess whether the course was engaging and motivating, and if the strategies were practical.
Organize the design phases (in larger font) and steps (numbered) below, by dragging and dropping them in the correct order.
Conclusion
The ARCS Model of Motivational Design, created by John Keller, offers a powerful framework for enhancing learner motivation by focusing on four key elements: Attention, Relevance, Confidence, and Satisfaction. By incorporating Keller’s model, including the 10-step design process, educators can create instruction that not only delivers educational content but also motivates learners by aligning with their needs and interests. The model’s practical approach has broad implications for instructional design, emphasizing the importance of tailoring learning experiences to foster intrinsic motivation.
Ultimately, Keller’s ARCS model provides both a theoretical understanding and a practical toolkit for improving learner motivation, contributing to more effective and engaging educational experiences.
Image Descriptions
Figure 1 – The image features a four-sectioned diagram with a central square surrounded by four trapezoids, each representing different concepts. The top trapezoid is labeled “Attention” and is colored in a pinkish hue. It lists strategies such as “Active participation,” “Variability,” and “Inquiry.” The right trapezoid, labeled “Relevance,” is in green and includes points like “Experience,” “Future Usefulness,” and “Choice.” The bottom trapezoid, labeled “Confidence,” is also in green, listing tasks like “Help students understand their likelihood for success” and “Grow the Learners.” The left trapezoid is labeled “Satisfaction” and is in a coral color, including ideas like “Skill Utility” and “Honest Reinforcement.” [Return]
Figure 2 – The image is a detailed flowchart illustrating a psychological processing model. It is divided into several segments, each representing different stages and components involved in motivational and volitional processing. The top section contains orange boxes labeled with various strategies such as “Motivational Strategies,” “Implementation Strategies,” and “Volition Strategies.” These influence the main section highlighted in blue, titled “Motivational and Volitional Processing.” This section includes elements like “Goals & Desires,” “Pre-Action Planning,” and “Actions.” “Mental Resource Management” and “Information and Psychomotor Processing” are also detailed with components like “Engagement,” “Sensory Inputs,” and “Working Memory.” The bottom section outlines the resulting stages, from “Effort Direction” to “Consequences.” Arrows connect these components, demonstrating the flow and interaction among different psychological processes. [Return]
References
Angelo, T. A. (2017). Assessing motivation to improve learning: Practical applications of Keller’s MVP model and ARCS-V design process. New Directions for Teaching and Learning, 2017 (152), 99–108. https://doi.org/10.1002/tl.20272
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84 (2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11 (4), 227–268.
Franklin, J. L. (2017). MVP and instructional systems design in online courses. New Directions for Teaching and Learning, 2017 (152), 39–52. https://doi.org/10.1002/tl.20267
Francom, G., & Reeves, T. C. (2010). A significant contributor to the field of educational technology. Educational Technology, 50(3), 55–58. http://www.jstor.org/stable/44429809
Harper, D. (n.d.). Etymology of theory. Online Etymology Dictionary. Retrieved February 10, 2025, from https://www.etymonline.com/word/theory
Keller, J. M. (1979). Motivation and instructional design: A theoretical perspective. Journal of Instructional Development, 2(4), 26–34. https://doi.org/10.1007/BF02904345
Keller, J. M. (1983). Motivational design of instruction. In C. M. Reigeluth (Ed.), Instructional-design theories and models: An overview of their current status. Lawrence Erlbaum Associates.
Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2–10. https://doi.org/10.1007/BF02905780
Keller, J. M. (2000, February). How to integrate learner motivation planning into lesson planning: The ARCS model approach. Paper presented at the VII Semanario, Santiago, Cuba.
Keller, J. M. (2008). An integrative theory of motivation, volition, and performance. Technology, Instruction, Cognition & Learning, 6(2), 79–104.
Keller, J. M., (2008). First principles of motivation to learn and e3-learning. Distance Education, 29(2), 175–185. https://doi.org/10.1080/01587910802154970
Keller, J. M. (2010). Motivational design for learning and performance: The ARCS model approach. Springer.
Keller, J. M. (2011, October 11). ARCS: A conversation with John Keller [Video]. YouTube. https://www.youtube.com/watch?v=E1ugbX2EKN0
Keller, J. M. (2016). Motivation, learning, and technology: Applying the ARCS-V motivation model. Participatory Educational Research, 3(2), 1–13. https://doi.org/10.17275/per.16.06.3.2
Keller, J. M. (2017). The MVP model: Overview and application. New Directions for Teaching and Learning, 2017(152), 13–26. https://doi.org/10.1002/tl.20265
Keller, J., & Suzuki, K. (2004). Learner motivation and E-learning design: A multinationally validated process. Journal of Educational Media, 29(3), 229–239. https://doi.org/10.1080/1358165042000283084
Keller, J. M., & Syracuse University, School of Education. (1983). Use of the ARCS model of motivation in teacher training (IDDE Working Paper No. 10).
Locke, E. A., & Latham, G. P. (1984). Goal setting: A motivational technique that works! Prentice-Hall.
Li, K. (2017). Motivational design in chemistry MOOCs: Applying the ARCS model. In Online approaches to chemical education (Vol. 1261, pp. 35–45). American Chemical Society. https://doi.org/10.1021/bk-2017-1261.ch003
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
OpenAI. (2025). ChatGPT (Feb 20 version) [Large language model]. OpenAI. https://chat.openai.com/
Reynolds, K. M., Roberts, L. M., & Hauck, J. (2017). Exploring motivation: Integrating the ARCS model with instruction. Reference Services Review, 45(2), 149–165. https://doi.org/10.1108/RSR-10-2016-0057
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
Straker, D. (2013, May 5). ARCS motivation model. Changing Minds. https://changingminds.org/explanations/motivation/arcs.htm
Waddington, L., & Dell, D. (n.d.). ARCS motivation and distance learning. Pressbooks. https://pressbooks.bccampus.ca/arcanddl/chapter/chapter-2/ARCS Motivation and Distance Learning – Open Textbook
Ward, J. K. (2021). Plato’s contribution to theoria. In Searching for the divine in Plato and Aristotle: Philosophical theoria and traditional practice (pp. 50–85). Cambridge University Press.
Ward, J. K. (2021). Aristotle’s refinement of theoria. In Searching for the divine in Plato and Aristotle: Philosophical theoria and traditional practice (pp. 86–117). Cambridge University Press.
Wigfield, A., & Eccles, J. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12, 265–310.
Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. https://doi.org/10.1006/ceps.1999.1015
Further Reading
Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students’ learning strategies and motivation processes. Journal of Educational Psychology, 80(3), 260–267. https://doi.org/10.1037/0022-0663.80.3.260
Bohlin, R., & Milheim, W. D. (1994). Application of an adult motivational design instructional model.
Bong, M. (2001). Role of self-efficacy and task-value in predicting college students’ course performance and future enrollment intentions. Contemporary Educational Psychology, 26, 553–570. https://doi.org/10.1006/ceps.2000.1048
Butler, Y. G. (2017). Motivational elements of digital instructional games: A study of young L2 learners’ game designs. Language Teaching Research, 21(6), 735–750.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum.
Dennissen, J. J. A., Zarret, N. R., & Eccles, J. S. (2007). I like to do it, I’m able, and I know I am: Longitudinal couplings between domain-specific achievement, self-concept, and interest. Child Development, 78(2), 430–447.
Dickey, M. D. (2007). Game design and learning: A conjectural analysis of how massively multiple online role-playing games (MMORPGs) foster intrinsic motivation. Educational Technology Research & Development, 55, 253–273.
Durik, A. M., Shechter, O. G., Noh, M., Rozek, C. S., & Harackiewicz, J. M. (2015). What if I can’t? Success expectancies moderate the effects of utility value information on situational interest and performance. Motivation and Emotion, 39(1), 104–118.
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256–273. https://doi.org/10.1037/0033-295X.95.2.256
Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72, 218–232. https://doi.org/10.1037/0022-3514.72.1.218
Elliot, A. J., & Harackiewicz, J. M. (1996). Approach and avoidance achievement goals and intrinsic motivation: A mediational analysis. Journal of Personality and Social Psychology, 70(3), 461–475.
Elliot, A. J., & McGregor, H. A. (2001). A 2 × 2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501–519.
Franklin, J. L. (2017). MVP and instructional systems design in online courses. New Directions for Teaching and Learning, 2017(152), 39–52. https://doi.org/10.1002/tl.20267
Greene, B. A., Miller, R. B., Crowson, H. M., Duke, B. L., & Akey, K. L. (2004). Predicting high school students’ cognitive engagement and achievement: Contributions of classroom perceptions and motivation. Contemporary Educational Psychology, 29(4), 462–482. https://doi.org/10.1016/j.cedpsych.2004.01.006
Harackiewicz, J. M., Barron, K. E., Pintrich, P. R., Elliot, A. J., & Thrash, T. M. (2002). Revision of achievement goal theory: Necessary and illuminating. Journal of Educational Psychology, 94(3), 638–645. https://doi.org/10.1037/0022-0663.94.3.638
Hinshaw, S. (2018). Children’s mental health Part A: The influence of behavioural inhibition, attention, and impulsivity. Alberta Family Wellness Initiative. https://training.albertafamilywellness.org/#/courses/course/70cf456d-7100-488e-8a70-fab224bfaf4b
Hidi, S., & Renninger, K. A. (2006). The Four-Phase Model of Interest Development. Educational Psychologist, 41(2), 111–127. https://doi.org/10.1207/s15326985ep4102_4
Howell, A. J., & Watson, D. C. (2007). Procrastination: Associations with achievement goal orientation and learning strategies. Personality and Individual Differences, 43, 167-178.
Huang, B., & Hew, K. F. (2016). Measuring learners’ motivation level in massive open online courses. International Journal of Information and Education Technology, 6(10), 759–764. https://doi.org/10.7763/IJIET.2016.V6.788
Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136(3), 422–449. https://doi.org/10.1037/a0018947
Immordino-Yang, M. H. (2016). Emotions, learning, and the brain: Exploring the educational implications of affective neuroscience. W W Norton & Co.
Immordino-Yang, M. H., & Gotlieb, R. (2017). Embodied Brains, Social Minds, Cultural Meaning: Integrating Neuroscientific and Educational Research on Social-Affective Development. American Educational Research Journal, 54(1_suppl), 344S-367S. https://doi.org/10.3102/0002831216669780
Joo, Y. J., Lim, K. Y., & Kim J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Computers & Education, 62, 149-158.
Juan, Y.-K., & Chao, T.-W. (2015). Game-based learning for green building education. Sustainability, 7(5), 5592–5608. https://doi.org/10.3390/su7055592
Keller, J. M. (1999). Motivational systems. In H. D. Stolovitch & E. J. Keeps (Eds.), Handbook of human performance technology (2nd ed., pp. 373–394). Jossey-Bass Pfeiffer. https://www.researchgate.net/publication/252687186_How_to_integrate_learner_motivation_planning_into_lesson_planning_The_ARCS_model_approach
Keys, T. D., Conley, A. M., Duncan, G. J., & Domina, T. (2012). The role of goal orientations for adolescent mathematics achievement. Contemporary Educational Psychology, 37, 47–54. https://doi.org/10.1016/j.cedpsych.2011.09.002
Kim, C., & Bennekin, K. N. (2013). Design and implementation of volitional control support in mathematics courses. Educational Technology Research & Development, 61, 793-817.
Kim, C., & Bennekin, K. N. (2016). The effectiveness of volition support (VoS) in promoting students’ effort regulation and performance in an online mathematics course. Instructional Science, 44(4), 359-377.
Kirriemuir, J. (2002). Video gaming, education and digital learning technologies. D-Lib Magazine, 8(2).
Krapp, A. (2005). Basic needs and the development of interest and intrinsic motivational orientations. Learning and Instruction, 15, 381–395.
Li, K. (2017). Motivational design in chemistry MOOCs: Applying the ARCS model. In Online Approaches to Chemical Education (Vol. 1261, pp. 35–45). American Chemical Society. https://doi.org/10.1021/bk-2017-1261.ch003
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
Linnenbrink, E. A. (2005). The dilemma of performance–approach goals: The use of multiple goal contexts to promote students’ motivation and learning. Journal of Educational Psychology, 97(2), 197–213. https://doi.org/10.1037/0022-0663.97.2.197
Linnenbrink-Garcia, L., Tyson, D. F., & Patall, E. A. (2008). When are achievement goal orientations beneficial for academic achievement? A closer look at moderating factors. International Review of Social Psychology, 21, 19–70.
Maehr, M. L., & Zusho, A. (2009). Achievement goal theory: The past, present, and future. In K. R. Wentzel & A. Wigfield (Eds.) Handbook of Motivation at School (pp.77-104). New York, NY: Routledge.
McMahon, M. (2013). Keller’s ARCS model. Research Starters: Education (Online Edition).
McQuain, B., Sammons, D., Neill, M.W., & Coffland, D. (2016). Using an appreciative inquiry approach to enhance intrinsic motivation in higher education courses. AI Practitioner, 18, 77-83. https://doi.org/10.12781/978-1-907549-29-8-13
Meece, J. L., Anderman, E. M., & Anderman, L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annual review of psychology, 57, 487–503. https://doi.org/10.1146/annurev.psych.56.091103.070258
van der Meij, H., van der Meij, J., and Harmsen, R. (2015). Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment. Educational Technology Research & Development, 63, 381-403.
Middleton, M. J., & Midgley, C. (1997). Avoiding the demonstration of lack of ability: An underexplored aspect of goal theory. Journal of Educational Psychology, 89(4), 710–718.
Park, S. W., & Huynh, N. T. (2015). How are non-geography majors motivated in a large introductory world geography course? Journal of Geography in Higher Education, 39(3), 386–406. https://doi.org/10.1080/03098265.2015.1048507
Park, S. W., & Kim, C. (2012). A design framework for a virtual tutee system to promote academic reading engagement in a college classroom. Journal of Applied Instructional Design, 2(1), 17-33
Park, S. W., & Kim, C. (2015). Boosting learning-by-teaching in virtual tutoring. Computers & Education, 82, 129-140.
Park, S. W., & Kim, C. (2016). The effects of a virtual tutee system on academic reading engagement in a college classroom. Educational Technology Research and Development, 64(2), 195-218.
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33
Prensky, M. (2001). Digital game-based learning. McGraw-Hill.
Reynolds, K. M., Roberts, L. M., & Hauck, J. (2017). Exploring motivation: integrating the ARCS model with instruction. Reference Services Review, 45(2), 149–165. https://doi.org/10.1108/RSR-10-2016-0057
Schiefele, U. (1991). Interest, Learning, and Motivation. Educational Psychologist, 26(3–4), 299–323. https://doi.org/10.1080/00461520.1991.9653136
Schiefele, U. (1996). Topic interest, text representation, and quality of experience. Contemporary Educational Psychology, 21, 3–18.
Schiefele, U. (2001). The role of interest in motivation and learning. In J.M. Collis & S. Messick (Eds.), Intelligence and personality: Bridging the gap in theory and measurement (pp. 163–194). Lawrence Erlbaum Associates, Inc.
Schiefele, U. (2009). Situational and individual interest. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 197–222). Routledge.
Schunk, D. H., Meece, J. L., & Pintrich, P. R. (2014). Motivation in education: Theory, research, and applications (4th ed.). Pearson.
Skinner, B. F. (1963). Operant behavior. American Psychologist, 18(8), 503-515.
Song, S. H., & Keller, J. M. (1999). The ARCS model for developing motivationally-adaptive computer-assisted instruction (Conference Paper) (5). Houston.
Tuan, H. L., & Chin, C. C. (2010). The Influence of Motivation on Students’ Learning in Science. Journal of Science Education and Technology, 19(5), 450–458. https://doi.org/10.1007/s10956-010-9217-6
Tüzün, H., Yilmaz-Soylu, M., Karakus, T., & Înal, Y., & Kizlkaya, G. (2009). The effects of computer games on primary school students’ achievement and motivation in geography learning. Computers & Education, 52, 68-77.
Urdan, T. (2004). Predictors of academic self-handicapping and achievement: Examining achievement goals, classroom goal structures, and culture. Journal of Educational Psychology, 96, 251–264. https://doi.org/10.1037/0022-0663.96.2.251
Vallerand, R. J., & Pelletier, L. G. (2000). The development of internalization of motivation in sport: A hierarchical model. In M. S. Hagger & M. L. Chatzisarantis (Eds.), Intrinsic motivation and self-determination in exercise and sport (pp. 79–104). Human Kinetics
Wentzel, K. R. (2000). What is it that I’m trying to achieve? Classroom goals from a content perspective. Contemporary Educational Psychology, 25, 105–115.
Weiner, B. (1986). An attributional theory of motivation and emotion. Springer-Verlag.
Weiner, B. (2010). The Development of an Attribution-Based Theory of Motivation: A History of Ideas. Educational Psychologist, 45(1), 28–36. https://doi.org/10.1080/00461520903433596
Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6, 49–78.
Wigfield, A., & Eccles, J. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12, 265–310.
Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. https://doi.org/10.1006/ceps.1999.1015
Wolters, C. A. (2003). Understanding procrastination from a self-regulated learning perspective. Journal of Educational Psychology, 95, 179–187. https://doi.org/10.1037/0022-0663.95.1.179
Wolters, C. A. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96(2), 236–250.
Zakrajsek, T. M. (2017). The MVP model as an organizing framework for neuroscience findings related to learning. New Directions for Teaching and Learning, 2017(152), 27–37. https://doi.org/10.1002/tl.20266
Licenses and Attributions
“Keller’s ARCS Model of Motivational Design” by Aimee Boyer McCandless is adapted from “ARCS Motivation and Distance Learning” by Leeann Waddington and Debra Dell from BCcampus, used under a CC Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, “Motivation Theories and Instructional Design” by S. Won Park from Pressbooks, used under a CC BY: Attribution License, and “Keller’s ARCS Model: Integrating Ideas About Motivation – Educational Psychology.” by Nicole Arduini-Van Hoose, licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. “Keller’s ARCS Model of Motivational Design” is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.