Design Rationale
Cognitive Load Theory
The main theory that drives my design is Cognitive Load Theory (CLT; Sweller et al., 2011). It integrates current knowledge of human cognitive structure and instructional design principles.
Cognitive Load Theory draws on Geary’s (2008) distinction between biologically primary knowledge and secondary knowledge. The former is the knowledge we have evolved to acquire, such as learning to recognize faces or speak a first language; biologically secondary knowledge, in contrast, is cultural knowledge that we have to acquire deliberately with conscious effort, such as reading and writing. Cognitive load theory is concerned primarily with biologically secondary information.
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Based on theories concerning working memory and long-term memory, cognitive load is divided into three categories: intrinsic, extraneous and germane cognitive load. Intrinsic cognitive load is determined by interacting elements that are intrinsic to the task, the higher interactivity, the higher intrinsic cognitive load. Extraneous cognitive load is caused by inappropriate instructional designs that require working memory resources to process elements unrelated to learning. Extraneous cognitive load can be reduced by revising the instructional design. Germane cognitive load refers to ‘effective’ working memory resources devoted to knowledge acquisition. The purpose of instructional design is to reduce extraneous cognitive load to make room for intrinsic cognitive load and thus increase germane cognitive load (Pass & Sweller, 2014).
My game “Use the Force” optimizes players’ use of working memory by both reducing extraneous cognitive load and managing intrinsic cognitive load.
How “Use the Force” reduces extraneous cognitive load
1. Signaling principle
The signaling principle is that people learn more deeply when cues are added that highlight the essential material (Mayer & Fiorella, 2014). The theoretical rationale for this principle is that it directs the learner’s attention toward essential material, thereby enabling more available cognitive capacity to process essential material rather than extraneous material.
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My game uses visual cues to direct players’ attention when a new element is introduced or when specific elements interactivity requires their processing:
When Net Force is first introduced, the whole scene is shaded except for the controller button which shows the vector addition. Extraneous materials, such as motion of the square, are filtered out, so players can focus on the concept of net force.
When players are asked to observe the square’s velocity and discover what Constant Velocity is, the velocity line thickens and flickers. This visual signal shines a spotlight on the key element of the problem, thereby helping with problem solving and reducing cognitive load.
2. Spatial contiguity principle
The spatial contiguity principle is that people learn more deeply when corresponding contents (words and pictures) are presented near rather than far from each other on the screen (Mayer & Fiorella, 2014). The theoretical rationale for this principle is that it reduces the effort of scanning back and forth between the related contents.
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Since my game is about how force affects velocity, I utilizes this principle by attaching the velocity vector and force vectors to the moving square, which is the visual focus of the game scene:
This design keeps the interacting elements together and thus draws more attention to the relation between force and velocity. In addition, grouping the forces and the square conveys the idea that they are a “whole”, which embeds the idea “force only exists as a result of an interaction”.
3. Temporal contiguity principle
The temporal contiguity principle is that people learn more deeply when corresponding contents are presented simultaneously rather than successively. The theoretical rationale is that it ensures that corresponding representations are in working memory at the same time, thereby eliminating the effort to hold them for an extended period of time (Mayer & Fiorella, 2014).
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This principle guides an important change to my game. At first, the game has a “pause state” for players to edit forces, and a “move state” where objects resume motion and the forces added by players are truly applied:
I realized that the time lag between “adding a force” and “seeing the effect” makes it hard for players to observe the causal relationships, and switching between move/pause states causes more extraneous cognitive load. So I removed the “pause state” and added a “controller button”, through which players add real-time forces.
How “Use the Force” manages intrinsic cognitive load
1. Segmenting principle
The segmenting principle is that people learn more deeply when the lesson is broken into learner-paced segments rather than as a continuous unit (Mayer & Pilegard, 2014). The theoretical rationale is that it slows the pace of the lesson to a level that enables learners to carry out essential processing.
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Following this principle, I reorganized the levels until each level introduces one new thing at most. So now the first level is for experiencing how force affects motion, the second level is for discovering constant velocity and the third level introduces gravity.
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2. Packing speed and direction into one vector
“Multiple elements can be combined into a single element during learning, resulting in a reduction of intrinsic cognitive load” (Paas & Sweller, 2014, p.37). The argument resonates with White’s (1983) findings about students’ sources of difficulty in understanding Newtonian dynamics, as one of the causes is “students thinking in terms of speed and direction... instead of just velocity as captured by a vector” (p.43).
These inspired me to redesign how velocity was represented in my game. At first, I used a speed bar and a direction pointer. Such representation constrains players to pay attention to two factors and contradicts with the learning goal of “thinking with vectors”. So I replaced the two with a single velocity vector--the line attached to the moving square:
Embodied Cognition
Pass and Sweller (2012) argued that the acquisition of biologically secondary information can be assisted by the use of biologically primary knowledge, including sensorimotor experiences such as gesture and object manipulation, which leads to the case of embodied cognition.
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Embodied cognition assumes that cognitive processes are grounded in perception and bodily actions rather than computation on abstract symbols (Barsalou, 2008). Ample evidence shows that embodied cognition supports learning with not only construction of higher-quality cognitive schemas (Broaders et al., 2007; Cook et al., 2008), but also less cognitive load (Ping & Goldin-Meadow, 2010; Hu et al., 2015).
We interact with digital devices with finger gestures all the time. A systematic review of touch-based educational technology comes to the conclusion that finger gestures used on a touch screen can support learning when it is closely aligned with what is being learnt (Agostinho et al., 2016). The effect has been supported by multiple findings: “Dragging” practices support students’ geometrical understanding and heuristics (Arzarello et al., 2002); pointing and tracing gestures (Hu et al., 2015) and making manipulations through mouse movements (Bokosmaty et al., 2017) in geometry learning both lead to higher performance and lower cognitive load; pointing and tracing temperature line graphs on iPads increases students’ performance on transfer tasks (Agostinho et al., 2015).
Understanding vectors
My project “Use the Force” is a touch-screen game where players manipulate force vectors by touching and dragging. While the player’s finger stays on the “controller button”, a vector is drawn from the center of the circle to the finger’s position. The dragging interaction embodies both the vector’s magnitude (the distance the finger travels from the center) and direction (the direction the finger moves from the center). As “cognitive and sensorimotor processes are closely intertwined” (Pass & Sweller, 2012, p.36), the dragging interaction may give players a better view of what vectors are.
Understanding vector addition
The forces in the game are “accumulative”, which means that starting from the second force, each new force will be added to the sum of previous forces. The vector addition procedure shows three vectors: the current force being added, the sum of all previous forces, and a new sum (result of the vector addition). The interaction embeds the parallelogram law of vector addition, which players will be using unawarely throughout the game:
According to Vygotsky (1978), externally oriented tools can be internalized through semiotic processes. Falcade et al. (2007) further explains the internalization process. In terms of my game, “the controller button” first serves as an externally oriented tool that evokes semiotic actions (adding vectors) to accomplish a specific task (steering the target square), then the actions with the button can foster an internalization process and produce a new psychological tool (mental model of vector addition).
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“Mechanics need to be aligned with the learning goals to be effective” (Plass et al.. 2015, p.267). My game not only infiltrates “vector addition” into players’ actions, but also repeats the interactions throughout the game: Since players have to carefully steer the target square, they will always keep a close eye on the vector addition and observe how their actions affect it, such as two vectors close to each other will create a “longer” sum while two far apart ones will create a “shorter” one. The repeated practices can help automate the procedure of vector addition.
Situated learning
Activity and situations are integral to cognition and learning, contrasting with the abstract and out-of-context formal concepts taught by traditional schools (Lave, 1988; Brown et al., 1989; Lave & Wenger, 1991; Wenger, 1998). Games inherently provide opportunities for situated learning: Games can be a meaningful and relevant context for learning by “providing information at the precise moment when it will be the most useful to the learner” (Plass et al., 2015, p.265). According to the review of Dondlinger (2007), “the authentic, situated context affords greater content mastery and transfer of knowledge than a traditional classroom learning”.
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“Use the Force” is crafted for well-situated learning experiences. Each level has a goal of “getting the flag” which defines the problem and gives players a clear motive. During their exploration of the problem, hints and tools are provided at the time when needed the most. For example, the “cancel force” button is not introduced until the level where the player has to pass a gate with constant velocity by cancelling the force acting on the target square. Similarly, the explanations for Gravity only appear after the player first presses the “gravity” button and sees how it affects the objects.
The game also provides tailored feedback based on the number of failed attempts toward a problem. The “constant velocity gate” will remind the player to observe the objects that can pass the gate and see what they have in common in terms of motion, after a couple of unsuccessful attempts. As the player fails more, the feedback gradually narrows down, till the instruction to cancel the force. Such multi-leveled feedback can:
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Provide an appropriate amount of information at a precise moment, which avoids to overwhelm the player or spoil the fun of exploring the answer;
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Customize the game experience -- players who are new to Newtonian physics receive more instructions, while those who already master the knowledge can skip the part by doing it right.
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Make failure productive (Kapur, 2008) -- everytime players fail they can learn something new, which is designed to be a necessary step in the learning process.
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All in all, by providing learners with information at the right moment, based on the problem they are solving and how well they are handling the problem, my game situates physics learning in a “tangible” context where players easily see how physics concepts are used, just like learning to use a hammer.
Motivation
Motivation is the drive that moves people to do something (Ryan & Deci, 2000, p.54). Motivation is usually distinguished between intrinsic motivation, which refers to doing something because it is inherently interesting or enjoyable, and extrinsic motivation, which refers to doing something for instrumental reasons, such as receiving a reward (Ryan & Deci, 2000). Intrinsic motivation has emerged as an important phenomena for educators as it results in high-quality learning and creativity (Ryan & Deci, 2000).
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Motivation is a significant characteristic of educational games. When players are motivated to play an educational game, their interactions with the game will foster cognitive processing of the game content, thereby improving learning (Plass et al., 2015; Delacruz, 2012). Effective design utilizes both extrinsic and intrinsic motivation (Dondlinger, 2007), which include incentive structures (points, leaderboards, trophies, etc) and game mechanics and activities that players enjoy (Plass et al., 2015). However, most game designers will agree that it is more desirable to make game mechanics in themselves interesting than to “sugar coat” dull mechanics with game features. Waraich’s argument (2004) resonates with this idea from the perspective of instructional design: “intrinsic rewards are based on a high congruence between the material being taught and the motivational techniques used” (p.98). Learning can be decreased if the learning goal and the motivating methods are detached.
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My game seeks to maximize players’ intrinsic motivation through digging up the inherent fun of problem solving. Solving problems, is intrinsically motivating -- nothing can be as rewarding as that “Aha” moment. Such moments are placed near the end of each level -- with reference to the “recency effect” (Murdock, 1962), doing so makes these moments easier to recall; and these moments are accompanied by particles (or surprising effects) which highlights the moment visually.
Apart from cherishing the aha moments, my game also motivates players by providing an “optimal level of challenge” (Plass et al., 2015). The idea is based on Malone’s argument about “optimal level of informational complexity” (1981): a learner’s curiosity can be evoked by environments that are novel and surprising, but not completely incomprehensible. To provide an optimal challenge, each puzzle in my game is novel but also relevant to others, so players have some expectations about what will happen but still can be surprised. Each new knowledge component is followed by a simple puzzle where players try to use knowledge. Then the new knowledge will be used in combination with prior knowledge to solve later puzzles. For example, in the level of gravity, players first learn what gravity is through a simple button-pressing task, then they pass through several “constant velocity” gates by both canceling forces (learned in the previous level) and switching gravity on/off. In this practice, players explore the concepts of “gravity” and “constant velocity” in combination, which may reinforce existing knowledge and facilitate transfer of learning. My game will keep exploring different combinations of more knowledge components, to create subtle puzzles that challenge players and bring out their curiosity.
Theoretical framework
The design rationale wraps up with a theoretical framework adapted from Plass’ “design framework of game-based and playful learning” (2015). The framework lists what kinds of player engagement learning games facilitate, the corresponding game design elements, and the theoretical foundations for these game design elements.
The design is based on four theories of two domains: cognition and motivation. The cognition domain contains:
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Cognitive Load Theory. I use the principles related to cognitive load theory for information design: the visual representation of game information and learning content. These representations are adjusted to minimize extraneous cognitive load and manage intrinsic cognitive load.
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Situated Learning. It is used for designing when and how to show hints or feedback so the information will be the most useful to the player and learning takes place in a meaningful context.
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Embodied Cognition. It is used for embedding the concept of vectors in the gestures players use to interact with the game for a better understanding of vector and vector arithmetic.
The motivation domain focuses on intrinsic motivation. With a goal of maximizing players’ intrinsic motivation, the level design highlights the fun of problem solving and keeps challenging the players.
The design elements are for eliciting player engagement. According to Plass (2015), player engagement includes cognitive engagement (i.e., mental processing and metacognition), affective engagement (i.e., emotion processing and regulation), behavioral engagement (i.e., gestures, embodied actions and movement), and sociocultural engagement (i.e., social interactions embedded within a cultural context). My game involves two of them: cognitive engagement and behavioral engagement.
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Cognitive engagement: “Information design” and “hints & feedback” enables players to process information more efficiently and thus can enhance cognitive engagement.
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Behavioral engagement: “Gestures” engages players behaviorally by requiring players’ finger movements as input.
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Lastly, “Level Design” motivates players generally so it boosts both cognitive engagement and behavioral engagement.
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Plass (2015, p.260) argues that “the goal of all these types of engagement, however, is to foster cognitive engagement of the learner with the learning mechanic”. In my game, as discussed before in Embodied Cognition, behavioral engagement facilitates cognitive engagement by aligning the gestures with how players percept vectors. Thus, the game design elements can lead to higher cognitive engagement, thereby better learning outcomes.
References
Arzarello, F., & Edwards, L. (2005). RF02 Gesture and the construction of mathematical meaning. In PME conference (Vol. 29, p. 1). No. 1
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Arzarello, F., Olivero, F., Paola, D., & Robutti, O. (2002). A cognitive analysis of dragging practises in Cabri environments. Zentralblatt für Didaktik der Mathematik, 34(3), 66e72.
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Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.
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Bokosmaty, S., Mavilidi, M. F., & Paas, F. (2017). Making versus observing manipulations of geometric properties of triangles to learn geometry using dynamic geometry software. Computers & Education, 113, 313-326.
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Brown, J.S., Collins, A. & Duguid, S. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42.
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Broaders, S., Cook, S. W., Mitchell, Z., & Goldin-Meadow, S. (2007). Making children gesture reveals implicit knowledge and leads to learning. Journal of Experimental Psychology. General, 136, 539–550.
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Cook, S. W., Mitchell, Z., & Goldin-Meadow, S. (2008). Gesture makes learning last. Cognition, 106, 1047–1058.
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Delacruz, G. C., & National Center for Research on Evaluation, Standards, and Student Testing. (2012). Impact of incentives on the use of feedback in educational videogames (CRESST Report No. 813). Los Angeles, CA: National Center for Research on Evaluation, Standards, and Student Testing.
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diSessa, A. A. (2000). Changing minds: Computers, learning, and literacy. Cambridge, Mass.: MIT Press.
Falcade, R., Laborde, C., & Mariotti, M. A. (2007). Approaching functions: Cabri tools as instruments of semiotic mediation. Educational Studies in Mathematics, 66, 317–333. doi:10.1007/s10649-006-9072-y.
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Geary, D. (2008). An evolutionarily informed education science. Educational Psychologist, 43, 179-195. http://dx.doi.org/10.1080/00461520802392133
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Kapur, M. (2008). Productive Failure. Cognition and Instruction, 26(3), 379-425. Retrieved May 9, 2020, from www.jstor.org/stable/27739887
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Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, MA: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511815355
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Malone, T. W. (1981). Toward a theory of intrinsically motivating instruction. Cognitive science, 5(4), 333-369.
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Mayer, R. E., & Fiorella, L. (2014). Principle for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, spatial contiguity, and temporal contiguity principles. In The Cambridge handbook of multimedia learning (2nd ed., pp. 279-315). Cambridge: Cambridge University Press.
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Mayer, R. E., & Pilegard, C. (2014). Principles for managing essential processing in multimedia learning: Segmenting, pre-training, and modality principles. In The Cambridge handbook of multimedia learning (2nd ed., pp. 316-344). Cambridge: Cambridge University Press.
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Murdock Jr, B. B. (1962). The serial position effect of free recall. Journal of experimental psychology, 64(5), 482.
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Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24, 27-45. http://dx.doi.org/10.1007/s10648-011-9179-2.
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Paas, F., & Sweller, J. (2014). Implications of cognitive load theory for multimedia learning. The Cambridge handbook of multimedia learning, 27, 27-42.
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Ping, R., & Goldin-Meadow, S. (2010). Gesturing saves cognitive resources when talking about non-present objects. Cognitive Science, 34, 602–619
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Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1), 54-67.
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Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer.
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Vygotsky, L. S. (1978). Mind and society: The development of higher psychological processes. Cambridge: Harvard University Press.
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Waraich, A. (2004). Using narrative as a motivating device to teach binary arithmetic and logic gates. ACM SIGCSE Bulletin, 36(3), 97-101.
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Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge, MA: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511803932