Studying Implicit Learning in Impulse

Since there is no known best way for learners to build intuitive understanding of these physics phenomena in games, our research captures the myriad of strategies players develop during gameplay that may reveal tacit knowledge. As a first step of this work, we have identified an initial set of strategic moves that we observe players making in the game Impulse. We use video-recorded play-testing data with dozens of players to code strategic game play moves. With coded clicks as “ground truth,” we then use educational data mining techniques to detect those strategic moves and describe how strategies evolve as players advance in the game.

While the video data allow us to observe and describe the strategic moves that players make during play-testing, these techniques are limited to the samples we can observe directly. To detect these cognitive strategies without video, our first step is to accurately identify their component parts—the strategic moves—from the game log data. Each “click” or touch from the player is recorded, along with a time stamp, anonymous player ID, and the features of the state of the game conditions that may be salient to determining the intent of players’ actions.

We use these data to develop models to detect players’ strategic moves in the data, from which we can look for sequences of those moves as evidence of cognitive strategies players use to succeed in the game that we hypothesize reflect a tacit understanding of Newtonian motion. We can further mine the data to examine the sets of strategies that apply to players who advance farther or more rapidly in the game (expert players), using regression models to identify the combinations of strategies that are characteristic of greater and lesser degrees of advancement.

The EdGE research team plans to build such models and use them in a comparative assessment study with hundreds of high school students. Students will be invited to play the games outside of school time. Some of their teachers will use bridge activities in class, curriculum activities that use examples from the games to illustrate principles of Newtonian motion. A variety of learning outcomes indicating their conceptual understanding of Newton’s first and second laws of motion will be measured and compared among four groups:

  • • Students who play the game and are in a class where teachers use bridge activities
  • • Students who play the game and are in a class where teachers do not use bridge activities
  • • Students who do not play the game and are in a class where teachers use bridge activities
  • • Students who do not play the game and are not in a class where teachers use bridge activities (control group)

We hypothesize that students who play the game extensively (throughout most of the 70 levels) before having bridge activities in class will have better learning outcomes than any of the other groups.

Our hypothesis is that implicit learning in games is a powerful precursor to formal learning and that the connections between implicit and explicit learning cannot be guaranteed. Designers must create conditions in which learners can dwell in the phenomena of interest, establishing tacit understandings, which may go unexpressed. We believe that if game designers can craft games to foster implicit learning, educators can leverage that implicit learning through bridge activities that establish visual and conceptual linkages from game to classroom. We believe this framework provides additional avenues for bridging learning across many settings, building more ubiquitous learning environments that may transcend school, home, game environments, the natural world, and all facets of learners’ lives.

 
Source
< Prev   CONTENTS   Source   Next >