Exploiting collaborative filtering techniques for automatic assessment of student free-text responses

Abstract

The automatic assessment of free-text responses of students is a relatively newer task in both computational linguistics and educational technology. The goal of the task is to produce an assessment of student answers to explanation and definition questions typically asked in problems seen in practice exercises or tests. Unlike some conventional methods which assess the student responses based on only information about their corresponding questions, this paper exploits idea of collaborative filtering to analyze student responses and used an effective collaborative filtering model -- feature-based matrix factorization model to deal with this challenge. The experimental results show that our feature-based matrix factorization model outperforms the baseline models and the model with a re-ranking phase can achieve a better and competitive performance -- 63.6% overall accuracy on the Beetle dataset.