Machine learning as a feedback tool for the gestalt principle of proximity

Abstract

When teaching students about the underlying structure of images, the Gestalt Principles are frequently used. Students and teachers may not always agree on the Gestalt Principles that an image contains, making assessment activities focusing on this topic very subjective. When working with large class sizes or when using multiple markers, this subjectivity becomes more problematic. A DBSCAN-based feature generation process and a multi-layer perceptron-based model are suggested in this study for categorising images as exhibiting the principle of proximity. When classifying images as containing aspects of proximity, the model displays a high level of accuracy. The model was integrated as a component of a larger process that alters the original input images to highlight areas that demonstrate proximity. The use of these annotated images can aid in giving students feedback. However, as the model was trained on a synthetic data set, its accuracy on real-world student submissions is yet to be tested.