Advances in game-based learning and educational data mining enable novel methods of formative assessment that can reveal implicit understandings that students may demonstrate in games but may not express formally on a test. This chapter explores a framework of bridging in game-based learning classes, where teachers leverage and build upon students' game-based implicit learning experiences to support science classroom learning. Bridging was studied with two physics learning games in about 30 high-school classes per game. Results from both studies show that students in bridging classes performed better on external post-tests, when accounting for pre-test scores, than in classes that only played the game or did not play the game at all. These findings suggest the teachers' role is critical in game-based learning classes. Effective bridging includes providing teachers with common game examples along with actionable discussion points or activities to connect game-based learning with classroom content.
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Many learners have rich implicit knowledge—understandings and abilities that they may be unable to articulate explicitly, yet may be fundamental to their learning (Ginsburg, Lee, & Boyd, 2008; Polanyi, 1966; Thomas & Brown, 2011). Implicit knowledge is argued to be foundational to the development of explicit knowledge (Brown, Roediger, & McDaniel, 2014; Kahneman, 2011; Lucariello, Nastasi, Anderman, Dwyer, Ormiston, & Skiba, 2016; Polanyi, 1966). Early examples of implicit learning in various everyday activities include the mathematical abilities of gamblers at the race track (Ceci & Liker, 1986); street children using early algebra skills in their vending of fruit and snacks (Nunes, Schliemann, & Carraher, 1993), and housewives calculating “best buys” at the supermarket (Lave, Murtaugh, & de la Roche, 1984). The measurement of implicit learning is key to understanding how we support explicit learning (Collins, 2010; Kalra, Gabrieli, & Finn, 2019; Thomas & Brown, 2011).
Leveraging learners’ implicit knowledge may be particularly critical for the support of learners with cognitive differences. Knowledge of learners with cognitive differences such as ADD, ASD, and/or dyslexia is often underestimated by traditional assessments (Sternberg, 1996) and often goes unrecognized because assessments contain construct-irrelevant factors such as an unrelated storyline or distracting graphics (Haladyna & Downing, 2004).
An everyday activity that lends itself well to studying implicit learning is digital games. Well-crafted learning games can compel players to persist in complex problem-solving (Asbell-Clarke et al., 2012; Shute, Ventura, & Ke, 2015; Steinkuehler & Duncan, 2008; Qian & Clark, 2016), as well as deep STEM learning (Clark, Nelson, Change, D’Angelo, Slack, & Martinez-Garza, 2011) and game-like digital activities provide a provide a natural and engaging environment that allows actions to inform the assessment of learning (Gee & Shaffer, 2010; Shute & Kim, 2014). The design of stealth assessments (Shute, 2011) within games has helped move researchers away from a traditional assessment design of formal pre/post tests to measure learning. Game-based learning assessments use data generated automatically through gameplay. As players dwell in a physics game, for example, where they grapple with mechanics and puzzles that are grounded in accurate science—their activity may show evidence of their knowledge of the phenomena.