Good writing skills are essential in the education system at all levels. However, the evaluation of essays is labor intensive and can entail a subjective bias. Automated Essay Scoring (AES) is a tool that may be able to save teacher time and provide more objective evaluations. There are several successful AES systems for essays in English that are used in large scale tests. Supervised machine learning algorithms are the core component in developing these systems.

In this project four AES systems were developed and evaluated. The AES systems were based on standard supervised machine learning software, i.e., LDAC, SVM with RBF kernel, polynomial kernel and Extremely Randomized Trees. The training data consisted of 1500 high school essays that had been scored by the students' teachers and blind raters. To evaluate the AES systems, the agreement between blind raters' scores and AES scores was compared to agreement between blind raters' and teacher scores. On average, the agreement between blind raters and the AES systems was better than between blind raters and teachers. The AES based on LDAC software had the best agreement with a quadratic weighted kappa value of 0.475. In comparison, the teachers and blind raters had a value of 0.391. However the AES results do not meet the required minimum agreement of a quadratic weighted kappa of 0.7 as defined by the US based nonprofit organization Educational Testing Services.

Jag har utvecklat och utvärderat fyra system för automatisk betygsättning av uppsatser (AES). LDAC, SVM med RBF kernel, SVM med Polynomial kernel och "Extremely Randomized trees" som är standard klassificerarprogramvaror har använts som grunden för att bygga respektivt AES system.