Dataset used in Design Analytics for Mobile Learning: Scaling up theClassification of Learning Designs based onCognitive and Contextual Elements

The following dataset has been used for the paper entitled "Design Analytics for Mobile Learning: Scaling up theClassification of Learning Designs based onCognitive and Contextual Elements".

Abstract

This research was triggered by the identified need in literature for large-scale studies about the kind of designs that teachers create for Mobile Learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analysing designs has been to manually classify them following high-level pedagogically-guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of Supervised Machine Learning (SML) to automatically classify the textual content of m-learning designs, through pedagogically-relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in them. As not all the SML models are transparent, while often researchers need to understand the behaviour behind them, the second goal of this paper considers the trade-off between models’ performance and interpretability in the context of design analytics for m-learning.  To achieve these goals we compiled a dataset of designs deployed through two tools, Avastusrada and Smartzoos. With it, we trained and compared different models and feature extraction techniques.  We further optimized andcompared the best-performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal through an illustrative case. We found that SML can reliably classify designs, with accuracy>0.86and Cohen’s kappa>0.69.