This paper presents a Game-Based Assessment model (GBA) designed to capture relevant information on play and testing whether it can constitute reliable evidence of learning. A central challenge for videogames research in education is to demonstrate evidence of player learning. Assessment designers need to attend to the ways in which game-play itself can provide a powerful new form of assessment. The GBA model has two layers: a semantic template that determines which click-stream data events could be indicators of learning; and learning telemetry that captures data for analysis. This study highlights how the GBA was implemented in a stem-cell science learning game, and shows how the GBA demonstrates a relationship between kinds of failure and learning in the game.
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