The majority of current research in Automated Essay Scoring (AES) focuses on prompt-specific scoring either the overall quality an essay or with regards to certain traits. In real-world applications obtaining labelled data for a target prompt is often expensive unfeasible, requiring AES system be able perform well when predicting scores essays from unseen prompts. As result, some recent has bee...

Traditional automated essay scoring systems rely on carefully designed features to evaluate and score essays. The performance of such systems is tightly bound to the quality of the underlying features. However, it is laborious to manually design the most informative features for such a system. In this thesis, we develop a novel approach based on recurrent neural networks to learn the relation b...

This paper examined validity evidence for the scores based on the Intelligent Essay Assessor (IEA), an automated essay-scoring engine developed by Pearson Knowledge Technologies. A study was carried out using the validity framework described by Yang, et al. (2002). This framework delineates three approaches to validation studies: examine the relationship among scores given to the same essays by...

Massive open online courses (MOOCs) have emerged as a prominent mode of online education, but the quality of assessments in MOOCs remains inconsistent. There has been a consistent gap between the state of the art in assessment and the state of the practice in both MOOCs and other forms of educational technology. Improving the quality of assessment has the potential to improve their usefulness f...

Two Bayesian models for text classification from the information science field were extended and applied to student produced essays. Both models were calibrated using 462 essays with two score points. The calibrated systems were applied to 80 new, pre-scored essays with 40 essays in each score group. Manipulated variables included the two models; the use of words, phrases and arguments; two app...

Supervised machine learning models for automated essay scoring (AES) usually require substantial task-specific training data in order to make accurate predictions for a particular writing task. This limitation hinders their utility, and consequently their deployment in real-world settings. In this paper, we overcome this shortcoming using a constrained multi-task pairwisepreference learning app...

Automated methods for identifying whether sentences are grammatical have various potential applications (e.g., machine translation, automated essay scoring, computer-assisted language learning). In this work, we construct a statistical model of grammaticality using various linguistic features (e.g., misspelling counts, parser outputs, n-gram language model scores). We also present a new publicl...

In this paper, we present a text evaluation system for students to improve Basque or Spanish writing skills. The system uses Natural Language Processing techniques to evaluate essays by detecting specific measures. The application uses a client-server architecture and both the interface and the application itself are multilingual. The article also explains how the system can be adapted to evalu...

Automated essay scoring is now an established capability used from elementary school through graduate school for purposes of instruction and assessment. Newer applications provide automated diagnostic feedback about student writing. Feedback includes errors in grammar, usage, and mechanics, comments about writing style, and evaluation of discourse structure. This paper reports on a system that ...