Analyzing interaction and player experience of game based learning using feature importance based clustering
DOI:
https://doi.org/10.31763/businta.v8i2.772Keywords:
digital game based learning, feature importance clustering, in-game experienceAbstract
This study explores the dynamics of the gaming experience and its impact on learning efficiency through digital game-based learning (DGBL). Leveraging the Fingerstroke Level Model-GOMS (FLM-GOMS) for interaction analysis and the In-Game Experience Questionnaire (iGEQ) for player experience assessment, we examine the relationship between game-play mechanics and educational outcomes. Our research incorporates a comprehensive dataset, focusing on 40 features encompassing motivation and efficiency outcomes. Through clustering, we identify distinct player groups exhibiting signif-icant variations in efficiency outcomes and game experiences. We utilized the feature selection technique to identify the crucial features that differentiate groups of students who excel in implementing DGBL from those who do not. Through the Random Forest feature importance method, we have found that FLM-GOMS features and positive player in-game feedback play a pivotal role in determining the effectiveness of DGBL.
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