Leveling Up: Measuring and Leveraging Implicit STEM learning in Games

Games provide an important vehicle for educators to promote and study learning. This symposium will examine research on measuring implicit game-based learning and teachers leveraging its relationship for explicit (e.g. school-based) STEM learning. The authors have developed a series of learning games that simulate authentic scientific phenomena, providing a learning mechanic for players to dwell in that phenomena and build their implicit understandings. The data logs generated through digital gameplay were mined to understand the patterns of play that may be related to implicit learning—the development of knowledge that is not yet explicitly formalized. Teachers used examples from games to help bridge implicit game-based learning to explicit STEM concepts taught in class. Presentation 1: Framing of Implicit Learning in Games Jodi Asbell-Clarke & Elizabeth Rowe Educational Gaming Environments group at TERC The theoretical framing that guides the research is based in a model of implicit learning, explored more commonly in psychology, philosophy, and sociology (e.g., Collins, 2010; Polanyi, 1966; Reber, 1993). Implicit knowledge is, by definition, largely unexpressed by the learner. Explicit knowledge is what educators typically attempt to measure in learning assessments. Implicit learning is considered foundational to all knowledge (Polanyi, 1966), but has not made headway in educational research because until now, it has been particularly difficult to measure. This framework differentiates between explicit knowledge, what we can express, from implicit knowing, what we are able to do. Cook and Brown (1999) argue that implicit and explicit knowledge can aid one another, claiming that a dynamic affordance of the interaction between acquisition and usage of knowledge such that knowledge and knowing (the doing that is associated with knowledge) are linked. A classic example used in implicit knowledge literature is learning to ride a bicycle. One does not need to formalize the physics to ride a bike, but familiarity with the sensations of riding often help students learn the physics. Games present a rich opportunity to support and measure implicit learning (Thomas & Brown, 2011). Players are often immersed in problem-solving situations where they experiment with the mechanics to understand the rule system, using trial and error with helpful feedback and rewards for motivation and sustained engagement (NRC, 2011). Reber (1993) suggests that experimental procedures to measure implicit learning should be a) novel to the learner, b) complex enough to not be “cracked” easily, c) emotionally neutral to the learner, and c) synthetic and arbitrary. Many games fulfill these criteria quite nicely. We argue, however, that games must be designed with attention to learning and measurement. Plass and his colleagues (2011) suggest designers must carefully identify and align the game mechanics, learning mechanics, and assessment mechanics. Game mechanics are what the player does in the game, learning mechanics are the activities through which the player learns a construct, and assessment mechanics are the diagnostics that provide evidence of that learning. Our work considers game, learning and assessment mechanics as part of the overall game design. Our games use simple game mechanics found in many popular games (e.g., get a ball to a goal without crashing into other balls; or solving puzzles to point lasers to hit a target) within a scientifically accurate simulation. By creating increasingly complex situations in which a player must grapple with the consequences of scientific laws and phenomena, we are creating an environment in which the game and learning mechanics are well aligned. Presentations 2 and 3 describe our methods for developing the in-game assessment mechanics, relying on observed play patterns rather than predefined metrics.