Gamifying your Designs Through Self-Determination

By Shakiyla Huggins May 12, 2025

Gamification has taken the educational world by storm, transforming traditional learning experiences into interactive, engaging, and motivational journeys. But what exactly makes gamification so effective in a learning environment?

In their 2015 study, Kim & Lee introduced a groundbreaking framework called The Dynamical Model for Gamification of Learning. This model highlights four critical factors that drive educational effectiveness: curiosity, challenge, fantasy, and control.

Understanding the Dynamical Model

Kim & Lee’s model is both flexible and versatile, bridging the gap between two gamification types:

• Structural Gamification: Game elements are woven into the learning process itself—think points, badges, and leaderboards.
• Content Gamification: The learning content is transformed into a game-like experience, making complex topics more engaging.

The Dynamical Model is grounded in four foundational theories that enhance its application:

1️⃣ Game Design Features

This theory focuses on the essential elements that make games captivating. By integrating these features—like challenges, feedback loops, and progression systems—learning can mimic the engagement found in popular games.

2️⃣ Key Characteristics of a Learning Game

Here, the emphasis is on embedding learning objectives within game mechanics. This ensures the experience remains not just fun, but educationally meaningful.

3️⃣ ARCS Model (Attention, Relevance, Confidence, Satisfaction)

Developed by Keller, this model serves as a roadmap for capturing and maintaining learner interest. It emphasizes:

• Attention: Engaging the learner right from the start.
• Relevance: Making the content meaningful to their goals.
• Confidence: Building learner belief in their ability to succeed.
• Satisfaction: Providing a sense of accomplishment through mastery.

4️⃣ MDA Framework (Mechanics, Dynamics, Aesthetics)

This well-known game design framework emphasizes three core components:

• Mechanics: The rules and systems that govern the game.
• Dynamics: How these mechanics interact during gameplay.
• Aesthetics: The emotional experience of the game.
  When applied to learning, MDA ensures that game-based activities are not only structured but also emotionally resonant.

The Heart of the Model: Curiosity, Challenge, Fantasy, and Control

At the core of the Dynamical Model are four driving forces:

• Curiosity: Learners are driven to explore and discover.
• Challenge: Carefully calibrated difficulty levels keep learners motivated.
• Fantasy: A narrative layer that makes the learning experience immersive and context-rich.
• Control: Learners have agency, allowing them to make choices and explore consequences.

Control is particularly critical as it empowers learners to navigate the experience, deepening engagement and promoting autonomy. Importantly, curiosity must outpace challenge; if learners feel overwhelmed, motivation can quickly fade.

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Kim & Lee’s Dynamical Model for Gamification of Learning, illustrating the core elements of Challenge, Control, Fantasy, and Curiosity alongside Mechanics, Dynamics, and Aesthetics.

Maintaining the Balance

A key insight from Kim & Lee’s model is the importance of time management and balance. If challenges escalate too rapidly or the fantasy elements feel disconnected from learning objectives, students may disengage. The secret lies in a well-balanced integration of curiosity, challenge, fantasy, and control, ensuring each element amplifies the others.

Final Thoughts

Kim & Lee’s Dynamical Model for Gamification of Learning is more than just a framework—it’s a blueprint for creating deeply engaging and educationally impactful learning experiences. For instructional designers and educators, mastering this model means turning ordinary lessons into adventures, where learning is driven by exploration, mastery, and meaningful challenge.

References

Kim, J. T., & Lee, W. H. (2015). Dynamical model for gamification of learning (DMGL). Multimedia Tools and Applications, 74(19), 8483–8493. https://doi.org/10.1007/s11042-013-1612-8