  Automatic grading is not a new approach but the need to adapt the latest
technology to automatic grading has become very important. As the technology
has rapidly became more powerful on scoring exams and essays, especially from
the 1990s onwards, partially or wholly automated grading systems using
computational methods have evolved and have become a major area of research. In
particular, the demand of scoring of natural language responses has created a
need for tools that can be applied to automatically grade these responses. In
this paper, we focus on the concept of automatic grading of short answer
questions such as are typical in the UK GCSE system, and providing useful
feedback on their answers to students. We present experimental results on a
dataset provided from the introductory computer science class in the University
of North Texas. We first apply standard data mining techniques to the corpus of
student answers for the purpose of measuring similarity between the student
answers and the model answer. This is based on the number of common words. We
then evaluate the relation between these similarities and marks awarded by
scorers. We then consider an approach that groups student answers into
clusters. Each cluster would be awarded the same mark, and the same feedback
given to each answer in a cluster. In this manner, we demonstrate that clusters
indicate the groups of students who are awarded the same or the similar scores.
Words in each cluster are compared to show that clusters are constructed based
on how many and which words of the model answer have been used. The main
novelty in this paper is that we design a model to predict marks based on the
similarities between the student answers and the model answer.
