NaturalLanguageProcesing4All

This paper presents a pilot study of NaturalLanguageProcessing4All (NLP4All), a Constructionist, low-threshold, XAI learning tool designed to bring Natural Language Processing methods into high school classrooms. Specifically, NLP4All is designed to let nonprogrammers explore different corpora of text through classification activities. Together with a high school Social Studies teacher, I developed a 2-week (6-hour) learning unit focusing on analyzing tweets from political parties to explore the differences and similarities between their policy views and communication styles. In the analysis, I find that text classification shows unexplored promise as a learning activity; that students were able to draw on their prior knowledge to classify tweets; that using NLP4All to collaboratively classify tweets led to productive classroom discussions; and that while students were able to build good machine learning models for classifying tweets, their rationales often focused on identifying one party, rather than distinguishing between parties. Finally, I discuss other educational contexts where NLP andML can be productive for children, and future design features that may be worth exploring.