Assessing implicit science learning in digital games

Building on the promise shown in game-based learning research, this paper explores methods for Game-Based Learning Assessments (GBLA) using a variety of educational data mining techniques (EDM). GBLA research examines patterns of behaviors evident in game data logs for the measurement of implicit learning—the development of unarticulated knowledge that is not yet expressible on a test or formal assessment. This paper reports on the study of two digital games showing how the combination of human coding with EDM has enabled researchers to measure implicit learning of Physics. In the game Impulse, researchers combined human coding of video with educational data mining to create a set of automated detectors of students’ implicit understanding of Newtonian mechanics. For Quantum Spectre, an optics puzzle game, human coding of Interaction Networks was used to identify common student errors. Findings show that several of our measures of student implicit learning within these games were significantly correlated with improvements in external post-assessments. Methods and detailed findings were different for each type of game. These results suggest GBLA shows promise for future work such as adaptive games and in-class, data-driven formative assessments, but design of the assessment mechanics must be carefully crafted for each game.