This week we welcome back guest blogger Dave Eng, founder of University XP to illuminate some of the complex concepts surrounding distributed practice in games-based learning.Dave Eng is an intellectual and creative educator, designer, and researcher who combines games, theory, and technology. Dave has played games for most of his life. As a result he studies game design and teaches others how to use games for education and learning. Dave serves as a faculty member at New York University’s School of Professional Studies. Dave hosts the podcast Experience Points and consults at University XP on games-based learning. He also leads the Games-Based Learning Alliance: a community of individuals who use games for teaching, training, learning, and development. His interests include professional development, learning theory, technology, and games. Find out more at www.davengdesign.com
How is distributed practice structured?
The key to distributed practice is the spacing effect and ensuring that enough time has passed in order to return to the learning activity. Preferably this is done over a longer period of time and over a repeated basis.
Studying distributed practice and the spacing effect originally centered around information acquisition and its lasting effect in animals. However, studies generalized this effect on human learning – specifically verbal learning.
The greatest effects in humans centered on the accomplishment of discrete tasks that have a high degree of fidelity (realism) towards its application. This is closest to learning a new language where repeated and direct application of the new language in everyday settings has a lasting effect. This is why foreign immersion programs are relatable and applicable opportunities to use and practice a foreign language.
The most meaningful distributed practice structures incorporate large conceptual ideas into smaller and more discrete tasks. This is included in games-based learning where concepts such as supply and demand in economics are closely connected to game mechanics where actual commodities are traded which affects the pricing of goods. Players can see the connection of these smaller mechanics to the larger concept of supply and demand.
Thus, through the core loop of the game, continual action, and repeated exposure, the student learns experientially about the concept put into practice. Distributed practice builds on this by engaging with the student on multiple different occasions spread over time. This is where academic scheduling and reinforcement structure take effect. This is especially true when students repeatedly play the game and use their own agency to enact meaningful decisions within it.
Time in distributed practice
Distributed practice relies on the spacing effect and the positive impact it can have for students when they return to the learning material. This means that regular breaks and regular return to engaging activities distributed over time is the path to greatest student information retention.
However, the type and length of spacing is at the discretion of the instructor. The length of time may be set by them. Despite this, it’s recommended that spacing be set at longer intervals of rest between activities compared to shorter ones. The reason behind this is that students will encounter or otherwise engage with other activities, subjects, or concepts between learning activities. These other experiences challenge their recall and application of the learning material every time they return to it.
Time between learning sessions is also in the hands of the students for self-directed activities. These include reading as well as studying as it requires that they manage and determine their own spacing schedule. This can often be a challenging prospect for students whose time and attention often compete against other subjects, activities, and commitments.
Implementing distributed practice
Student accommodation and dedication to their own studying, learning, and engagement schedule is paramount. Instructors in the classroom have great leeway into how students engage with the material. Often, reviews that happen immediately after the original learning event is much more intense. However, outside of the classroom, it is ultimately up to the student to choose how and when they revisit the material and study.
A common format for re-engaging with learning material for students and instructors is practice testing. Specifically re-developing the scenarios in which the information will be used and applied in order to improve performance. Likewise, instructors can build on this model through the application of review exercises both before and after learning sessions. Such activities provide students with opportunities to re-engage with material at regular, spaced, and formal intervals.
Distributed practice and gaming
Distributed practice for teaching and learning is already well defined. But how about distributed practice in gaming? Some of the most engaging and addicting games already use distributed practice by engaging and enticing players to come back to them. Because of this, games already institute and use distributed practice in order for players to succeed, achieve, and develop mastery of the game.
Furthermore, games-based learning programs can also be structured to break up learning programs into smaller sections – thus reducing the amount of time spent on any one area. This provides opportunities for students to take a break from the content in order to review, debrief, and reflect. Such structure takes advantage of the spacing effect of distributed practice.
Instructors can use the spacing between games-based learning in order to use distributed practice to its full effect. Some of the most popular games can even require that players take breaks from playing order to mitigate the negative aspects of massed practice.
Educators can also use different styles, themes, and mechanics of games all surrounding a particular learning outcome for students. Such a structure forces the learner to use and apply information, concepts, and procedures in different ways utilizing different perspectives in order to more thoroughly engage with the learning experience.
Games-based learning can be used in conjunction with traditional learning models in its application of distributed practice, interleaved practice, and active learning. Doing so ensures that multiple modalities and approaches accommodate different learner perspectives and needs in the process.
When distributed practice is combined and applied with games-based learning the positive effects for learners can be significant. Especially when compared against traditional learning techniques and massed practice.
Designing distributed practice in games-based learning
This section will outline how distributed practice can be used in games-based learning through five different areas: motivation, investment, new content, meaningful choices & emergent challenges, and returning players.
Understanding player motivation is paramount for using games-based learning for engaging learners in the most earnest way possible. Player motivation is what triggers learners to play and keep playing throughout the game.
The best motivation to take advantage of here is intrinsic motivation where players continue to play and engage based on their internal desires to excel. This is compared to extrinsic motivation where outside factors (such as money, fame, or prestige) motivate players to play.
Extrinsic motivation may be a good way to engage players at the beginning of the game. However, continual involvement (and distributed practice) depends on players’ development of emergent play. That occurs thorough systemic depth of the game. This usually happens when players discover new and previously realized strategies, tactics, or philosophies to implement in the game that they can use to their advantage.
Players are most likely to return to the game and continue playing once they are intrinsically invested in it. This is especially relevant if players’ first stages include some sort of early progression (i.e. leveling up) or personalization (the ability to create one’s avatar) in the game. Both effects positively influence the player experience.
While this type of investment is preferred; other more mechanical forms of investment can be created in serious games. These include daily bonuses for engaging or logging into the game regularly to play. Such engagement focuses on extrinsic rewards. However, this mechanic can be used in order to re-engage the player with the game in a distributed way in order to re-expose them to the content within the game.
Investment at its’ surface level is a valuable form of engagement. Invested players are more likely to keep playing and returning to a game. This is true whether a game was created for entertainment or educational value.
Introduction of new content into a games-based learning application can be useful – particularly when structuring your learning material and scaffolding players to use and apply information in new contexts.
This can be seen in current digital games where new player quests are introduced at regular intervals as well as “streaks” for routinely returning to the game. Such regular and timely return (for instance every 24 hours) gives players “boosts” in terms of in game abilities such as experience and rewards.
Adding new content can be difficult in games when the player base is diverse. This diversity can be represented in a population divided between original players and new players. Original players will want to see new content and new opportunities to keep the game fresh – where as newer players may still require strict scaffolding for creating their own player experience.
This can occur when creating training programs where returning employees and new employees are mixed together. The result of which are different needs and applications for both groups of students. This can be overcome by creating game structures where new players are incentivized to review basic content to develop competency in the game. This is compared to experienced players who are charged with finding new ways to use and apply old competencies or help new players get acclimated to play.
Meaningful Choices & Emergent Challenges
Providing players with meaningful choices creates a space for them to deepen their connection and investment in the game. As such, these choices should provide players with feedback and reinforcement based on their application of concepts.
Likewise, designers and educators should create a structure where the game provides easier “on-boarding” challenges for players to learn the game while gradually increasing the difficulty as they continue to play and engage over multiple sessions.
Returning players, or more seasoned and experienced students, represent a big challenge for games-based learning and education as a whole. That’s because these individuals bring to the classroom personalized knowledge that may upset the balance with new players.
This could come in the form of highly experienced players whose play in the game is beyond other players. Likewise these could also be from returning players who have not engaged in a while and would need to re-learn the game (and content) all over again. The experience could be completely alien and foreign to them.
Despite this, it’s up to instructors and designers to create learning environments and serious games that cater to and include these returning players, players with preconceived notions, and new players alike. Doing so ensures that your game applies to the widest possible audience while also helping individuals achieve their learning outcomes.
This article defined distributed practice for learning. It compared distributed practice to massed practice and the use of the spacing effect for greatest learning and knowledge retention. Specific references to retrieval for retention were addressed as well as the benefits of using distributed practice in an educational environment.
Structure is important for distributed practice: especially as it relates to time between learning sessions. Tips for implementing distributed practice for both educators and students were covered as well as how gaming addresses distributed practice. Using distributed practice in games-based learning was covered from five perspectives. Those perspectives included motivation, investment, new content, meaningful choices & emergent challenges, and returning players.
This article was about distributed practice. To learn more about gamification, check out the free course on Gamification Explained.
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