Using Ontology-based Information Extraction for Subject-based Auto-grading

The procedure for the grading of students’ essays in subject-based examinations is quite challenging particularly when dealing with large number of students. Hence, several automatic essay-grading systems have been designed to alleviate the demands of manual subject grading. However, relatively few of the existing systems are able to give informative feedbacks that are based on elaborate domain knowledge to students, particularly in subject-based automatic grading where domain knowledge is a major factor. In this work, we discuss the vision of subject-based automatic essay scoring system that leverages on semiautomatic creation of subject ontology, uses ontology-based information extraction approach to enable automatic essay scoring, and gives informative feedback to students

A comparison of various machine learning algorithms and execution of flask deployment on essay grading

Students’ performance can be assessed based on grading the answers written by the students during their examination. Currently, students are assessed manually by the teachers. This is a cumbersome task due to an increase in the student-teacher ratio. Moreover, due to coronavirus disease (COVID-19) pandemic, most of the educational institutions have adopted online teaching and assessment. To measure the learning ability of a student, we need to assess them. The current grading system works well for multiple choice questions, but there is no grading system for evaluating the essays. In this paper, we studied different machine learning and natural language processing techniques for automated essay scoring/grading (AES/G). Data imbalance is an issue which creates the problem in predicting the essay score due to uneven distribution of essay scores in the training data. We handled this issue using random over sampling technique which generates even distribution of essay scores. Also, we built a web application using flask and deployed the machine learning models. Subsequently, all the models have been evaluated using accuracy, precision, recall, and F1-score. It is found that random forest algorithm outperformed the other algorithms with an accuracy of 97.67%, precision of 97.62%, recall of 97.67%, and F1-score of 97.58%

A robust methodology for automated essay grading

None of the available automated essay grading systems can be used to grade essays according to the National Assessment Program – Literacy and Numeracy (NAPLAN) analytic scoring rubric used in Australia. This thesis is a humble effort to address this limitation. The objective of this thesis is to develop a robust methodology for automatically grading essays based on the NAPLAN rubric by using heuristics and rules based on English language and neural network modelling

Formative assessment visual feedback in computer graded essays

In this paper we discuss a simple but comprehensive form of feedback to essay authors, based on a thesaurus and computer graphics, which enables the essay authors to see where essay content is inadequate in terms of the discussion of the essay topic. Concepts which are inadequately covered are displayed for the information of the author so that the essay can be improved. The feedback is automatically produced by the MarkIT Automated Essay Grading system, being developed by Curtin University researchers

Use of normalized word vector approach in document classification for an LKMC

In order to realize the objective of expanding library services to provide knowledge managementsupport for small businesses, a series of requirements must be met. This particular phase of a largerresearch project focuses on one of the requirements: the need for a document classificationsystem to rapidly determine the content of digital documents. Document classification techniquesare examined to assess the available alternatives for realization of Library Knowledge ManagementCenters (LKMCs). After evaluating prominent techniques the authors opted to investigate aless well-known method, the Normalized Word Vector (NWV) approach, which has been usedsuccessfully in classifying highly unstructured documents, i.e., student essays. The authors proposeutilizing the NWV approach for LKMC automatic document classification with the goal ofdeveloping a system whereby unfamiliar documents can be quickly classified into existing topiccategories. This conceptual paper will outline an approach to test NWV's suitability in this area

Evaluation of structured questions using modified BLEU algorithm

This paper describes and exemplifies an application of a Structured Exam Questions Test Bank and Evaluation Using Modified Bilingual Evaluation Understudy (BLEU) Algorithm, a software system developed in pursuit of robust computerized marking of free-text answers to open-ended questions.It employs the Information System Development Research Methodology with modified BLEU Algorithm and Expert System for similar words.The system was developed to facilitate in managing and administrating structured questions for client/server architecture based on intranet. The system incorporates a number of processing modules specifically aim at providing an automated marking to reduce spelling errors, calculating scores, managing and administrating an exam.The system was trial-run by a group of students and lecturers, and modifications particularly on the interface have been modified and implemented. Problems and limitations discovered were then discussed and recommendations made to overcome the limitations for the future development of the research

Automated essay grading: an evaluation of four conceptual models

Automated essay grading has been proposed for over thirty years. Only recently have practical implementations been constructed and tested. This paper describes the theoretical models for four implemented system described in the literature, and evaluates their strengths and weaknesses. All four models make use of comparisons with one or many model answer documents that have been previously assessed by human markers. One hybrid system that makes use of some linguistic features, combined with document characteristics, is shown to be a practical solution at present. Another system that makes use of primarily linguistics features is also shown to be effective. An implementation that ignores linguistic and document features, and operates on the ?bag of words? approach, is then discussed. Finally an approach using text categorisation techniques is considered