An Annotated Dataset for Sentence Classification from Educational Problem Sets

High-quality, annotated datasets are crucial for enhancing the application of natural language processing tasks. Nevertheless, in the educational domain, they are limited compared to the vast amount of learning content. In the context of smart learning environments, game-based learning and artificial intelligence technologies enable the development of interactive and engaging learning experiences. In this paper, we provide an annotated dataset for sentence classification from educational problem sets to automate educational content structuring. The initial collection was extracted from the Vaia platform, including 13 disciplines with 91,590 problem sets. A subset of 1678 problem sets was manually labeled using the Doccano tool. Our annotation schema includes four sentence categories: Background, Given Data, Instruction, and Question, which represent the required components of educational problem sets and support escape game design applications. To validate our dataset, deep learning models are tested on the proposed dataset to provide a comparative study. The results demonstrate that a simple transformer architecture, the BERT classifier, achieved the best overall performance with a Macro F1-score of 0.94.