We propose a system for automated essay grading using ontologies and textual entailment. The process of textual entailment is guided by hypotheses, which are extracted from a domain ontology. Textual entailment checks if the truth of the hypothesis follows from a given text. We enact textual entailment to compare students answer to a model answer obtained from ontology. We validated the solution against various essays written by students in the chemistry domain.
The current methods of essay scoring can be categorized into two classes: holistic scoring and rubric-based scoring. In holistic scoring, the essay is assessed and a single score selected from a predefined score range is assigned as an overall score @cite_2 . In the analytical or rubric-based scoring method, essays are assessed on the basis of a certain set of well-defined features @cite_2 . Each feature has a scale associated with it and the final score awarded to the essay is the sum of scores of all the essay rubrics features.
@cite_2
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Abstract Automated Essay Grading (AEG) is defined as a computer technology that evaluates and scores written prose. A number of AEG systems have been developed since the 1960s and in most of them, an ad-hoc or generalized approach is used to grade spelling even though it is an important element of an essay-scoring rubric. Existing approaches do not therefore give an accurate representation or measure of spelling in essays. According to the rubric-based scoring method used in the National Assessment Program “Literacy and Numeracy (NAPLAN) in Australia, spelling is marked in three steps” first, by identifying the correct and incorrect words in the essay; second, by categorizing each word based on the difficulty level into one of four classes: Simple, Common, Difficult or Challenging, and counting the number of correct and incorrect words in each category; finally, by using the pre-defined NAPLAN rubric scale to assign the mark. Only a small number of existing AEG systems can be used for rubric-based scoring, and none can be used to grade spelling according to the NAPLAN rubric. In this paper, we address this shortcoming in the existing literature and present an innovative approach to automatically mark spelling using rubric based scoring. We develop two algorithms based on the rules and heuristics of the English language to formulize the rubric for spelling and then implement these algorithms in Java language and perform a series of evaluations of our system using an essay dataset. Our results are very promising, even though it is the first system of this kind. The main purpose of the study was to investigate the distinctness and reliability of analytic (or multi-trait) rating dimensions and their relationships to holistic scores and e-rater® essay feature variables in the context of the TOEFL® computer-based test (TOEFL CBT) writing assessment. Data analyzed in the study were holistic and multi-trait essay scores provided by human raters and essay feature variable scores computed by e-rater® (version 2.0) for two TOEFL CBT writing prompts. It was found that (i) all of the six multi-trait scores were not only correlated among themselves but also correlated with the holistic score, (ii) high correlations obtained among holistic and multi-trait scores were largely attributable to the impact of essay length on both holistic and multi-trait scoring, and (iii) some strong associations were confirmed between several e-rater variables and multi-trait rating dimensions. Implications are discussed for improving the multi-trait scoring of essays, refining e-rater essay feature variables, and validating automated essay scores. The need for automated grading tools for essay writing and open-ended assignments has received increasing attention due to the unprecedented scale of Massive Online Courses (MOOCs) and the fact that more and more students are relying on computers to complete and submit their school work. In this paper, we propose an efficient memory networks-powered automated grading model. The idea of our model stems from the philosophy that with enough graded samples for each score in the rubric, such samples can be used to grade future work that is found to be similar. For each possible score in the rubric, a student response graded with the same score is collected. These selected responses represent the grading criteria specified in the rubric and are stored in the memory component. Our model learns to predict a score for an ungraded response by computing the relevance between the ungraded response and each selected response in memory. The evaluation was conducted on the Kaggle Automated Student Assessment Prize (ASAP) dataset. The results show that our model achieves state-of-the-art performance in 7 out of 8 essay sets. This study exploits statistical redundancy inherent in natural language to automatically predict scores for essays. We use a hybrid feature identification method, including syntactic structure analysis, rhetorical structure analysis, and topical analysis, to score essay responses from test-takers of the Graduate Management Admissions Test (GMAT) and the Test of Written English (TWE). For each essay question, a stepwise linear regression analysis is run on a training set (sample of human scored essay responses) to extract a weighted set of predictive features for each test question. Score prediction for cross-validation sets is calculated from the set of predictive features. Exact or adjacent agreement between the Electronic Essay Rater (e-rater) score predictions and human rater scores ranged from 87 to 94 across the 15 test questions.