Building motivation from scratch:
a theoretical guideline on how to create better motivational frameworks for Smart Learning Environments
Introduction
Imagine sitting in your high-school history class. You have troubles staying awake, because your teacher is holding an hour-long monologue over a historical period you are not interested in. Your thoughts drift off repeatedly and you cannot help but look at your friend’s phone lighting up next to you. He is playing Farmville, a Facebook game, in which the player needs to accumulate resources to build up a prospering farm and compete with his friends. Even though farming does not particularly interest you either, it grasps your attention. When you finally look up from the screen, the bell rings and you see more than half of the class awakening from a slumber, putting their phones in their pockets and getting ready to go to their next class.
This example describes a situation one can experience in many of our class rooms today. Engaging students in the class room has always been a problem, but this problem has recently intensified (Elam, Stratten & Gibson, 2007). The reason for this is that the millennial generation, children born after the year 2000, differs from preceding ones. Virtually all children brought up in the digital age are gamers, who play games in social settings, like SingStar or Wii Fit in their living rooms, or in online environments (Lenhart et al., 2008). The continuous consumption of games and other forms of media has changed students learning styles and increased their need for fast and diverse informational input. Traditional learning systems, which cannot adapt to these changes fail to engage students and satisfy their learning needs (Elam et al., 2007). There are different, more active, school types, e.g. Montessori schools, which try to adapt to advances in pedagogy and psychology and that accept the premise that learning is more effective in authentic environments (Vygotsky, 1978). Problems with these schools are that they only reach a small portion of the population and that, despite their attempt to stay on top of changes in modern science, they cannot keep up with advances in technology. This leads to a disconnect between how students use technology at home and in school and causes frustration to those students, who want to utilize the benefits of technology to study (Project Tomorrow, 2011).
If we, as a society, want to benefit from the advances of modern science and at the same time use technology to its maximum potential, we need to build smarter learning environments. Smart learning environments (SLE) differ from traditional learning environments in the way that they utilize technology to support the student to take advantage of a learning opportunity whenever it occurs (e.g. through guidance, feedback, hints or tools). The system adapts to the individual learner’s needs, which the systems learns by analyzing the learner’s way of learning, their performance and through the online and real-world contexts in which they are placed (Hwang, 2014).
The technological support for these systems could come in the near future from technologies like smart glasses, in combination with the technological tools already built into our smartphones. A conceivable example of an SLE application could be a scenario in which a student, who has not yet succeeded at learning the necessary vocabulary for his Spanish class, i.e. the name of certain groceries, learns these through the help of a smart learning system. The smart learning system observes the need of the student, learning the vocabulary, and displays the necessary words on the screen of the smart glasses the next time the student enters a supermarket. The student then repeats the words out loud and the sound is picked up by his smartphone, which evaluates the correctness of his pronunciation. Generally, there are three key features to SLE: Firstly, it needs to be context aware, which means that it recognizes the learner’s online and real-world status and contributes relevant information based on that data. Furthermore, the system should be flexible enough to accustom changes in the student’s performance, learning behavior or personal factors and, lastly, it needs to have an interface, which presents the information to the user in a meaningful way (Hwang, 2014).
There are several benefits to smart learning systems. Firstly, they could take us away from the “one-size-fits-all, centralized, static, top-down and knowledge-push model of traditional learning” and bring us towards a more “personalized, social, open, dynamic, emergent and knowledge-pull model” (Chatti et al., 2010). Secondly, through the continuous availability of these smart learning systems, they also improve the learner’s chance of remembering the word by learning it in a relevant context (Mernill, 2002). And thirdly, they provide each student with the necessary resources to spark an interest in learning. Students then can use these interests to create their own learning opportunities and by themselves spread their interest to other fields, growing their knowledge base and exploring their own possibility space in the process (Gros, 2016). Ultimately, this could create a fundamental shift in education, which has the potential to advance our human, economic and cultural development (Chatti et al, 2010).
At its current state of development, smart learning systems still have several issues. A major psychological challenge of SLE’s is to be accepted by its users, as global systematic changes often lead to resistance in some people, in the same way it arouses curiosity in others. Once the students participate in the design they need to stay motivated until they can fully utilize the learning environment to their advantage and benefit from its tools (Gros, 2016). This is where games could come into play. Games are designed primarily to engage and entertain people (Deterding, 2015b). As one saw in the example in the beginning, games are also often more engaging than traditional classroom lessons, even though the students know that these lessons are essential for their future well-being. The attempt of translating the benefits of games onto non-game activities is called gamification. Gamification uses game design to create a framework around the content, which aims to make the content more enjoyable, while engaging people in the process (Petkov et al., 2011).
What if we could utilize the gamification approach to extract game mechanics from games like ‘Farmville’ and use these to engage people during the lesson instead of distracting them from it? Could games become part of the solution instead of the problem? To find an answer to this, this article will investigate how gamification can help to build a motivational framework for Smart Learning Environments?
Smart Learning Environments and gamification
To be able to answer this question, we first need to get a general overview of SLE’s and understand how they work. As can be seen in figure 1, an SLE consists of a Human Learning Interface (HLI), which focuses mainly on the learner and his behavior, but beyond that it can gather information and update its status based on other things in its physical vicinity, including the statuses and actions of other people and available objects and resources. In our example, the HLI was part of a system of digital devices, i.e. smart glasses and smartphone technology. The first function of the HLI, observation, uses the shared modalities of these devices to gather enough information to make relevant observations and inform the HLI about the physical and digital environment of the learner. The second function is objectives, which helps the student to set appropriate goals in cooperation with a teacher. Intervention, the third and last function, combines the learner’s status and his goals to design meaningful interventions. These then help the student to direct his behavior towards the achievement of his/her learning goals. Interventions can take on many forms. It could be a question, which is posed to the learner. It could also be a task to fulfill, e.g. learning some vocabulary, or it could be the access to something, e.g. learning material. The intervention part also involves relevant feedback loops, which inform the learner about his progress and give incentives.
The interface is the part, which connects the student to the system. A good interface can keep the student engaged and facilitate the learning process (Koper, 2014). This is where gamification could be applied. Games excel at making seemingly irrelevant tasks relevant. They often give the player quests in which he/she need to collect resources, e.g. gather 15 pieces of wood, and then they make him/her do these tasks over and over again in a slightly varied fashion, e.g. collect 10 pieces of wolf skin, collect 20 diamonds, etc.. At the same time these quests are designed in a way that makes them still rewarding after hundreds of hours. The standard way of using this game design is via game mechanics. Game mechanics are a set of rules or constructs, like levels or leaderboards, which give feedback to the user. Through the feedback the player learns how to interact with the software and explore its properties (Cook, 2006). A leaderboard, for example, could show the user how well he/she is doing within the game and motivate him/her to do more to climb up the ranks of the system. Through gamification these game mechanics can also be applied to non-game contexts. However, there are warning remarks within the gamification community, because if one wants to apply gamification in a meaningful way it is not sufficient to throw game mechanics, like points, badges and leaderboards at the problem. Instead, one needs to thoroughly analyze human motivation and learn from game design to create a motivation framework, which can engage people in the same way games do (Bogost, 2011).
Theories of motivation and lessons from game design:
To understand modern motivational research in psychology, one first needs to grasp the classical distinction between extrinsic and intrinsic motivation (Deci, 2000). Extrinsic motivation is normally unrelated to the content, e.g. money for a good grade or a job well done. These kinds of motivators are simply means to an end and because of this the motivation usually vanishes after the reward is gone (Harlen & Crick, 2003). Intrinsic motivation, in contrast, is an innate need and one of the best predictors of academic success (Deci and Ryan, 2001, Wilson & Corpus, 2005). It is the need to minimize the discrepancy between what we predict with our existing knowledge and what we actually experience and often inspires self-directed learning (Piaget & Cook,1952).
To better understand the contemporary constructs of motivation and to additionally integrate them with game-design theories, a three-level approach to motivational design is proposed in this article (figure 2), which can help to maneuver the complexities of human motivation and game design. This approach can help educators to create a motivation framework, which in the future might successfully engage students in Smart Learning Environments. The first two levels of this design tackle the problem of how we can use extrinsic motivation in a way that it triggers student’s individual motives, without undermining their intrinsic motivation. It shows a way of how game design and motivational research can be fused to keep up intrinsic motivation and support continued learning. The third level strives to create an optimal learning experience for students by utilizing Csikszentmihaly’s research into flow states. The first two levels of the design are then updated to accommodate the requirements for flow states and support them. The image below is a sketch of these three levels and their subfunctions.
The next article will go deeper into these three levels and their functioning and additionally illuminate how to design a system to satisfy the conditions that originate from these three levels.
To be continued…