Analysis of student essays in an introductory physics course using natural language processing

We analyzed the essays that were written on various topics in an introductory physics course using two unsupervised machine learning algorithms. One of them was Latent Dirichlet Allocation (LDA). This algorithm is used for extracting abstract topics from a collection of text documents. The other algorithm was Non-negative Matrix Factorization (NMF). It is used for similar purposes but also in other domains such as image recognition. We applied these two algorithms to the dataset that consisted of N = 683 student essays. Although there were some built-in, important differences between LDA and NMF, they both found similar topics in our data by large. This offers instructors a promising and productive way of accessing useful information about their students' written work, especially in large-enrollment classes.