Automated Grading Of Short Text Answers: Preliminary Results In A Course Of Health Informatics

Giovanni De Gasperis, Stefano Menini, Sara Tonelli, Pierpaolo Vittorini

Students learning Health Informatics in the degree course of Medicine and Surgery of the University of L’Aquila (Italy) are required — to pass the exam — to submit solutions to assignments concerning the execution and interpretation of statistical analyses. The paper presents a tool for the automated grading of such a kind of solutions, where the statistical analyses are made up R commands and outputs, and the interpretations are short text answers. The tool performs a static analysis of the R commands with the respective output, and uses Natural Language Processing techniques for the short text answers. The paper summarises the solution regarding the R commands and output, and delves into the method and the results used for the automated classification of the short text answers. In particular, we show that through FastText sentence embeddings and a tuned Support Vector Machines classifier, we obtained an accuracy of 0.89, Cohen’s K=0.76, and F1 score of 0.91 on a binary classification task (i.e. pass or fail). Other experiments including additional linguistically-motivated features, whose goal was to capture lexical differences between the students‘ answer and the gold standard sentence, did not yield any significant improvement. The paper ends with a discussion of the findings and the next steps to be taken in our research.