Automated essay scoring with latent semantic analysis (LSA) has recently been subject to increasing interest. Although previous authors have achieved grade ranges similar to those awarded by humans, it is still not clear which and how parameters improve or decrease the effectiveness of LSA. This pa-per presents an analysis of the effects of these parameters, such as text pre-processing, weighting, singular value dimensionality and type of similarity measure, and benchmarks this effectiveness by comparing machine-assigned with human-assigned scores in a real-world case. We show that each of the identified factors significantly influences the quality of automated essay scoring and that the factors are not independent of each other
