The Importance of Teacher Bridging in Game-Based Learning Classrooms

The Importance of Teacher Bridging in Game-Based Learning Classrooms

Jodi Asbell-Clarke (TERC, Inc, USA), Elizabeth Rowe (TERC, Inc, USA), Erin Bardar (TERC, Inc, USA) and Teon Edwards (TERC, Inc, USA)
Copyright: © 2020 |Pages: 29
DOI: 10.4018/978-1-7998-2015-4.ch010


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.

Key Terms in this Chapter

Game Mechanic: The actions that a player is intended to take to succeed in the game.

Assessment Mechanic: The evidence of learning that can be measured through gameplay activity.

Bridging: Actions taken by a teacher in the classroom to connect implicit game-based learning to explicit classroom learning.

Learning Mechanic: The learning that is intended to be the as a result of players’ activity in the game.

Educational Data Mining (EDM): An emerging research discipline that provides a suite of methods and research approaches from statistics, machine learning, and data mining to analyze data collected during teaching and learning.

Implicit Learning: The development of foundational knowledge and skills that are not necessarily expressed explicitly by the learner, and thus may not be captured on traditional tests and schoolwork.

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