Effectivity of Affine Transformation Knowledge Training Using Game Mechanics

摘要

The Gamified Training Environment for Affine Transformation (GEtiT) was developed as a demonstrator for the Gamified Knowledge Encoding model (GKE). The GKE is a novel framework that defines knowledge training using game mechanics (GMs). It describes the process of directly encoding learning contents in GMs to allow for an engaging and effective transfer-oriented knowledge training. Overall, GEtiT is developed to facilitate the training process of the complex and abstract Affine Transformation (AT) knowledge. The complexity of the AT makes it hard to demonstrate this learning content thus learners frequently experience issues when trying to develop an understanding for its application. During the gameplay, the application of the AT's mathematical grounded aspects is required and information about the underlying principles are provided. In this article, a short overview over GEtiT's structure and the knowledge encoding process is given. Also, this article presents the results of a study measuring the training effectivity and motivational aspects of GEtiT. The results indicate a training outcome similar to a traditional paper-based training method but a higher motivation of the GEtiT players. Hence, GEtiT yields a higher learning quality.