John M. Quick, PhD, is a learning experience expert who has driven innovation as a designer, researcher, educator, and consultant. John applies a rare combination of technology savvy, multimedia arts, and learning science to inspire people to reach their maximum potential.
John has created evidence-based multimedia learning experiences to support organizations such as Pearson, Macmillan Learning, Pluralsight, Arizona State University, Michigan State University, and the U.S. Army. Examples include learning principles that bridge the gap between proven educational research and practical product design, instructional videos that bring concepts to life through visual storytelling, and collaborative, empathetic advising that drives innovation strategies forward.
John earned a PhD in Educational Technology at Arizona State University, where he researched motivation and enjoyment in games. He created the Gameplay Enjoyment Model (GEM) and Gaming Goal Orientations (GGO) model to guide the evidence-based design of game-based learning. In addition, he has created more than 20 digital games for mobile devices, desktops, and the web.
John has over 6 years of teaching experience at the higher education level in the United States and Southeast Asia. He has instructed online and in-person courses in game design and development, computer programming, and computer literacy at Michigan State University, Arizona State University, and DigiPen Institute of Technology Singapore.
John is the author of three books that apply learning science and novel storytelling approaches to help people learn practical technology skills. These include Learn to Code with Games (ISBN: 9781498704687), Learn to Implement Games with Code (ISBN: 9781498753388), and Statistical Analysis with R (ISBN: 9781849512084).
Quick, J. M. (2017). Learn to implement games with code. Boca Raton, FL: CRC Press. ISBN: 9781498753388.
Quick, J. M. (2016). Learn to code with games. Boca Raton, FL: CRC Press. ISBN: 9781498704687.
Quick, J. M. (2010). Statistical analysis with R. Birmingham, UK: Packt Publishing Ltd. ISBN: 9781849512084.
Challenge Hint Solution:
12 Principles of Evidence-Based Learning Design
On the first day of class, I was greeted by a group of nervous, fearful freshman design students. This was their first computer programming class and my first time teaching it. They had heard the stories of their elders: the things you learn are pointless, nothing makes sense, you will fail. Many told themselves that there was no possible way they could learn or enjoy our class. We hadn't even started, yet they were already prepared to give up on their dreams.
To begin class, I loaded up a complete video game I created. As I demonstrated the game, their sense of wonder returned. They happily named the game character Luna. I explained that they would be using their own coding skills to make a game world like this one. Each day, they would determine what Luna is capable of doing and what adventures she would have. Gradually, over the course of the semester, the students successfully learned to create their own games. Along the way, they were empowered to rewrite their personal stories: I can code, I can succeed, I love making video games.
This was the situation that inspired me to dramatically change the way people learn. I worked with a variety of talented learners, but also empathized with the pain and anxiety they felt. I refused to allow traditional, systemic follies destroy their passion and rob us of successful future professionals. That's why I created the Challenge Hint Solution (CHS) learning method.
Challenge Hint Solution (CHS)
The CHS approach consists of these three phases:
Challenge: Begin by introducing the learner to a challenge. A challenge can take many forms, such as a problem to solve or goal to achieve. Briefly explain the context behind the challenge. In addition, provide any supplementary resources available to assist the learner. Subsequently, list the challenge requirements. These requirements should describe a successful resolution to the challenge. They identify what the learner is expected to achieve, but do not dictate a specific approach. Complex problems tend to have a vast array of potential solutions and problem-solving approaches. Hence, it is up to the learner to develop an appropriate problem-solving method and to create a personalized solution that achieves the requirements.
Hint: Gradually introduce hints that contain small chunks of key information that the learner can leverage. Hints can take almost any useful form. For example, a hint could be an explanation of a new concept, a reference to prior learning, or a visualization. Regardless of the format, hints should be brief and to the point. They act as a small bridge between the learner's current state and being one step closer to solving the challenge. There can be a few hints or very many of them. However, each hint should be spaced out in time. The time before, after, and between hints is filled with important learning processes, such as reflecting on concepts and forming relationships. Give the learner time to exercise these processes by using hints effectively as supports and scaffolds.
Solution: Ultimately, the learner should arrive at a personalized solution that meets the stated challenge requirements. At this point, it is appropriate to share an example solution. Note that this does not suggest a single correct or expert approach. The example is merely a demonstration that the learner's own solution can be compared to. In the case that an expert provides the example, it likely demonstrates proficiency according to domain-specific experience. However, example solutions could also be shared through peer review, in which case they are alternatives of varying quality that provide insights into others' problem-solving processes. Either way, through exploring an alternative solution, the learner gains a deeper understanding of their own approach.
12 Principles of Evidence-Based Learning Design
CHS embodies 12 evidence-based design principles that have been synthesized from high-quality research in the learning sciences . It has been applied extensively in my own works [e.g. 2, 3]. By applying these principles to learning design, we are able to achieve better learning outcomes.
The Principle of Context: Situate learning within authentic contexts that are meaningful to learners and reflect the experiences of real-world experts.
The Principle of Expert-Level Challenge: Challenge learners with complex, expert-level tasks that are beyond their current ability level and for which they have yet to acquire the requisite knowledge and skills.
The Principle of Expert Performance: Help learners apply the same processes and practices that experts use.
The Principle of Demonstration: Model effective performance for learners by demonstrating processes, practices, and outcomes.
The Principle of Guidance: Provide guidance in real time as learners progress through a task.
The Principle of Clarity: Clearly identify the desired outcomes of a task and assess learner performance according to explicit criteria.
The Principle of Tangibility: Encourage learners to create tangible artifacts that demonstrate their learning and expertise.
The Principle of Metacognition: Support the development of learners' metacognitive awareness and self-regulated learning.
The Principle of Community Comparison: Engage learners with experts, peers, and community members whose work can be compared to their own.
The Principle of Personalization: Encourage learners to explore ill-structured problems through personalized goals, strategies, and solutions.
The Principle of Scaled Challenge: Gradually increase the complexity and diversity of tasks to match learning demands.
The Principle of Focused Scope: Introduce learners to the overall structure of a task, then guide them to execute its individual components at the appropriate times.
It is on rare occasion that excellent research and practical implementation meet. Together, we can bridge the gap between research and practice to create learning experiences that transform lives. As a leader, educator, or designer, people rely upon your expertise to drive meaningful outcomes. By applying CHS and evidence-based learning design principles in practice, you can make a more powerful impact on your learners.
 Sawyer, R. K. (Ed.). (2014). The Cambridge handbook of the learning sciences. New York: Cambridge University Press. ISBN: 9781107626577.
 Quick, J. M. (2016). Learn to Code with Games. Boca Raton, FL: CRC Press. ISBN: 9781498704687.
 Quick, J. M. (2017). Learn to Implement Games with Code. Boca Raton, FL: CRC Press. ISBN: 9781498753388.