Automatically Grading Essays with Markit©

The demand of scoring natural language responses has created a need for new computational tools that can be applied to automatically grade student essays. Systems for automatic essay assessment have been commercially available since 1990's. However, the progress in the field was obstructed by a lack of qualitative information regarding the effectiveness of such systems. Most of the research in automatic essay grading has been associated with English writing due to its widespread use and the availability of more learner collection and language processing software for the language. In addition, there is large number of commercial software for grading programming assignments automatically. In this work, we investigate document semantic similarity based on Latent Semantic Analysis (LSA) and on Latent Dirichlet Allocation (LDA). We use an open-source Python software, Gensim, to develop and implement an essay grading system able to compare an essay to an answer-key and assign to it a grade based on semantic similarity between the two. We test our tool on variable-size essays and conduct experimental tests to compare the results obtained from human grader (professor) and those obtained from the automatic grading system. Results show high correlation between the professor grades and the grades assigned by both modeling techniques. However, LSA-based modeling showed more promising results than the LDA-based method.

Standardized testing such as the SAT often requires students to write essays and hires a large number of graders to evaluate these essays which can be time and cost consuming.Using natural language processing tools such as Global Vectors for word representation (GloVe), and various types of neural networks designed for picture classification, we developed an automatic grading system that is more time-and cost-efficient compared to human graders.We applied our application to a set of manually graded essays provided by a previous competition on Kaggle in 2012 on automated essay grading and conducted a qualitative evaluation of the approach.The result shows that the program is able to correctly score most of the essay and give an evaluation close to that of a human grader on the rest.The system proves itself to be effective in evaluating various essay prompts and capable of real-life application such as assisting another grader or even used as a standalone grader.

The amount of time teachers spend grading essays has increased over the past decade, prompting the development of systems that are able to lighten the workload. Many systems have thus far used linear regression or semi-supervised methods towards this objective. This paper discusses some of the main Automated Essay Grading systems, highlighting some of their strengths and weaknesses, in addition to providing a brief overview of Text Mining and meta-data annotation techniques that could be used to facilitate the process of grading essays through an automated system.

Exam essay is an evaluation of learning in the form of essay questions that have answers more variable than multiple choice questions. Variations of these answers provide its own difficulties for teachers in assessing response. Essay grading system is built to be one solution that can speed up and simplify the process of grading. Essay grading system in this study was done by measuring the similarity of responses of the students and teacher answer key. This research use winnowing algorithm. Winnowing algorithm is an algorithm for text similarity measure. Winnowing algorithm produces fingerprint of text that will represent the answer to the calculation of similarity with jaccard coeficient equation. Testing was conducted to determine the ability of the algorithm winnowing to provide an essay grading using n-gram and window value changes of the winnowing algorithm. The test results showed the use of the value of n-grams and window on the method of winnowing effect on fingerprint similarities were found. The more similarities fingerprint found, the value of the resulting system also be higher. The accuracy of the grading system showed better results on text answers that have the answer sentence structure same with key answer.

Automated Essay Grading (AEG) systems that are currently available use different techniques to extract specific written dimensions features to assess the written prose. Several Neuro-Fuzzy approaches have been applied to try to solve the short essay AEG problem. The main idea behind this approach is to identify the number of main keywords (5 inputs) each of which has 4 synonyms based on specific constraints. These inputs have been processed by developing two general models including; Artificial Neural Network Back-propagation optimization technique and Subtractive Clustering technique. Furthermore a third general model have developed to incorporate more main keywords by processing double the number of previous models input, single output with specific constraints by combining Subtractive clustering and Neuro Fuzzy techniques. The Sub-Clustering Neuro Fuzzy model could be used for a variable number of inputs. More than 900 generated datasets have been used representing questions related to information technology field. The correlation coefficient have been used to measure the agreements between the actual and predicted marks of the models, and used two error measures including; Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) to indicate the performance of the developed models. The average results obtained from all three general models indicates the suitability and adequacy of the developed models to solve the short essay AEG problem and shows that the preliminary results are promising.

Imbalanced Learning Techniques for Improving the Performance of Statistical Models in Automated Essay Scoring

BackgroundIn diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression.MethodsThe system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK’s National Screening Committee guidelines.ResultsExternal evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2–94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed.ConclusionsWe demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme.

The Windsor Yearbook of Access to Justice is proud to publish issue 32 (1). This issue features a special section highlighting the scholarship of graduate students. While it is always a pleasure to read promising work by newer scholars in the fields of law and social justice, we are certain that this collection of articles represents some of the finest and thought-provoking scholarship stemming from current graduate students in law. The articles stem from a graduate student essay contest that WYAJ held in 2013 and for which we received many submissions.The collection of selected papers offers a view of legal and interdisciplinary research examining issues that are topically diverse but which are all of deep, long-term importance to the world of access to justice. A reader of the special section on Graduate Student Scholarship will find explorations of access to justice from the perspectives of equality rights, discretion, adjudication and methods of legal service delivery, to name a few.A prize was offered to two papers judged to be of exceptional quality. I am very pleased to announce that the winners of those two prizes are Andrew Pilliar, for his article “Exploring a Law Firm Business Model to Improve Access to Justice” and Blair A. Major, for his contribution, “Religion and Law in R v NS: Finding Space to Re-think the Balancing Analysis”.The Editorial Board thanks all those who submitted papers to the contest and to this final special issue of the Windsor Yearbook of Access to Justice.Another notable feature of this issue is the introduction of a section called Research Notes. The Yearbook will periodically publish peer-reviewed research notes that present the findings of empirical (quantitative, qualitative or mixed method) research studies. This section aims to contribute to the growing and important body of empirical scholarship within the realm of access to justice socio-legal research.We hope that you enjoy this issue.Laverne Jacobs, Co-Editor-in-Chief, Windsor Yearbook of Access to Justice, Issue 32 (1)

Evaluating writing skills in secondary education requires accurate, automated approaches that capture both semantic meaning and linguistic quality. Traditional methods often rely on surface-level features such as grammar, vocabulary, or word count, which fail to assess deeper coherence and contextual relevance in student essays. Existing approaches struggle with subjectivity, limited generalization, and insufficient semantic understanding. To address these challenges, this study proposes a Semantic Embedding Model using a Siamese Neural Network (SNN) framework, which measures semantic similarity between student responses and reference texts. The model leverages contextual embeddings to effectively capture meaning, coherence, and writing quality beyond syntactic measures. The proposed method can be applied for essay grading, skill assessment, and providing feedback to students and educators. Experimental results demonstrate improved accuracy, fairness, and consistency in evaluating writing skills compared to traditional evaluation systems.

The disciplines of ethics, science, and the law often conflict when it comes to determining the limits and boundaries of embryo research. Under current Australian law and regulations, and in various other jurisdictions, research conducted on the embryo in vitro is permitted up until day fourteen, after which, the embryo must be destroyed. Reproductive technology and associated research is rapidly advancing at a rate that contests current societal and ethical limits surrounding the treatment of the embryo. This has brought about the question of the adequacy of the fourteen-day rule and whether it is necessary for it be reconsidered and reformed. This paper will highlight some of the tensions that exist in ethics, science, and the law in relation to the extension of the rule. It will be concluded that any move to extend the rule must be accompanied by close consultation with the public as the ultimate stakeholders in how the future of reproductive technology is created, constructed, and contested.

One of the research interests in educational technology has been in the automating the text assessment of the students' answer. The main reason for such interest is due to an overwhelming of marking scripts need to be graded within a time limit. Among many forms of answering format, an essay-type answer is the most difficult format for an automated system to assess. In this paper, we describe the architecture of our system which assigns a grade to an essay prepared by the students for the history subject. The system is built on modules that perform preprocessing of the document and query, prepare model answers and assign grades to the students' answer. We show the output of the system as the results of applying this system on the samples of data which are the students' answer for high school syllabus

The goal of this proposed work is to develop an AI-powered system for automated essay grading. The system will utilize natural language processing and Graph based techniques to analyze, and grade written essays. It not only checks the syntax, semantics and grammar but also grades according to the similarity of sentences using a Graph based approach. The system will be trained on a dataset of labelled essays and will be able to accurately grade new essays based on their content and writing quality. The system can be able to integrate with existing learning management systems. The goal is to provide a more efficient and accurate essay grading process, so the teachers can provide valuable feedback to students. The proposed work aims to develop an automated essay grading system using AI technology. The system will be able to analyze, and grade written essays by using natural language processing and machine learning techniques. The system will be trained on a dataset of labelled essays, which will be used to teach the system to recognize patterns and characteristics of high-quality writing. This will enable the system to accurately grade new essays based on their content and writing quality.

Essay Rating is the process of assigning grades to the essays written for professional purposes. Automating this process implies the use of specified machine learning algorithms over the standard manual methods to evaluate the essays through Natural Language Processing. Essays can be termed as a large set of textual entities. Grading these essays involves distributing them into a fewer number of distinct categories based on certain constraints and standards. These categories are then used to assign the possible grades. Because of numerous factors like cost, time, biasness, and delay in results, there is a need to switch from manually checking the essays to automated grading. It is a very tedious task to evaluate a bulk of essays while probing every kind of error. Henceforth, this model of Automated Essay Grading System models seems quite assuring. It takes text input from the user and generates grades conforming to the pre-defined standards. After evaluating its useful attributes, an output is obtained in the form of a grade for the given essay. This project comprises of grammar-based Checking (Checking Unique Word count, Sentence Count, Grammatical Mistakes, Spelling Errors) and Context Based Checking (Application of NLP to categorize the essays).

Now the traditional composition score required a large number of manpower material resources. And the marking quality is greatly influenced by subjective factors. Combined with the present popular technology-Machine Learning and the Text Classification, studied the middle school essay grading system based on support vector machine (SVM). The software system use the crawler to climb a scale middle school students' composition as the training set and the test set from the website, then participle, segmentation sentences and extract the features, use the support vector machine algorithm train the grading model and build a classifier by features, then we use the classification model to predict the test sample or Judge an unknown level article. The experiment results show that this method has a bit feasibility and effectiveness.

This paper explained a research that was conducted to find the best methodology for automatic essay grading. The Winnowing method was used in this paper with specific application for Japanese language exam. With Japanese language input, romanization was implemented to change the input that were in hiragana, katakana, or kanji into romaji. After the system was developed, it still needed to find the best parameters with the highest accuracy or closer even similar to the results from human rater. From testing the short essay Japanese language exam answers with 100% of score from human rater, several testing show that the best parameter for this system is with n=2, w=2, and p=2 and the average accuracy of total score gathered for all students is 86.86%. Since the average accuracy is above 80%, it could be concluded that winnowing algorithm is suitable for Japanese language examination term.