StoryQ—an Online Environment for Machine Learning of Text Classification


  • William Finzer Concord Consortium
  • Jie Chao Concord Consortium
  • Carolyn Rose Carnegie Mellon University
  • Shiyan Jiang North Carolina State University



Online, K12 Education, Classification, Unstructured Data, Linguistics, AI


The StoryQ environment provides an intuitive graphical user interface for middle and high school students to create features from unstructured text data and train and test classification models using logistic regression. StoryQ runs in a web browser, is free and requires no installation. AI concepts addressed include: features, weights, accuracy, training, bias, error analysis and cross validation. Using the software in conjunction with curriculum currently under development is expected to lead to student understanding of machine learning concepts and workflow; developing the ability to use domain knowledge and basic linguistics to identify, create, analyze, and evaluate features; becoming aware of and appreciating the roles and responsibilities of AI developers;. This paper will consist of an online demo with a brief video walkthrough.




How to Cite

Finzer, W., Chao, J., Rose, C., & Jiang, S. (2022). StoryQ—an Online Environment for Machine Learning of Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12860-12860.