Comparing supervised machine learning approaches to automatically code Learning Designs in mobile learning

To understand and support teachers’ design practices, researchers in Learning Design manually analyse small sets of design artifacts produced by teachers. This demands substantial manual work and provides a narrow view of the community of teachers behind the designs. This paper compares the performance of different Supervised Machine Learning (SML) approaches to automatically code datasets of learning designs. For this purpose, we extracted a subset of learning designs (i.e., their textual content) from Avastusrada and Smartzoos, two mobile learning tools. Later, we manually coded it guided by rel-evant theoretical models to the context of mobile learning and used it to train and compare several combinations of SML models and feature extraction techniques. Results show that such models can reliably code learning design datasets and could be used to understand the learning design practices of large communities of teachers in mobile learning and beyond.