An automated essay evaluation system using natural language processing and sentiment analysi

An automated essay evaluation system is a machine-based approach leveraging long short-term memory (LSTM) model to award grades to essays written in English language. natural language processing (NLP) is used to extract feature representations from the essays. The LSTM network learns from the extracted features and generates parameters for testing and validation. The main objectives of the research include proposing and training an LSTM model using a dataset of manually graded essays with scores. Sentiment analysis is performed to determine the sentiment of the essay as either positive, negative or neutral. The twitter sample dataset is used to build sentiment classifier that analyzes the sentiment based on the student’s approach towards a topic. Additionally, each essay is subjected to detection of syntactical errors as well as plagiarism check to detect the novelty of the essay. The overall grade is calculated based on the quality of the essay, the number of syntactic errors, the percentage of plagiarism found and sentiment of the essay. The corrected essay is provided as feedback to the students. This essay grading model has gained an average quadratic weighted kappa (QWK) score of 0.911 with 99.4% accuracy for the sentiment analysis classifier

Comparative Study of Techniques used for Automatic Evaluation of Free Text answer

Evaluation in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. Assessment of free text answers have become a necessity ,not only for todays education system but also to erase the load and errors of manual correction.This paper basically presents a comparative study of some of the techniques used to achieve the goal ,the limitation of the respective methods

Survey of Techniques Used for Answer Evaluation using Semantic Network

Automatic Evaluation of Free Text Answers' have become a necessity, not only for better acceptance of online learning, but also to handle the pressure of assessment of a large number of students' responses in a fatigue free pedagogically correct method in traditional learning environments. This work is aimed at developing a model to evaluate free text answers of students based on the semantic similarity it has with the model answers prepared by teachers. The model answers are prepared prior to the evaluation process and through a process of dynamic semantic network building, a model is prepared which is used in evaluation. The proposed technique should allow the flexibility of comparing a student’s answer with two or more model answers and finally evaluating it against the model answer it most closely resembles. DOI: 10.17762/ijritcc2321-8169.15012

Pedagogically-driven Ontology Network for Conceptualizing the e-Learning Assessment Domain

The use of ontologies as tools to guide the generation, organization and personalization of e-learning content, including e-assessment, has drawn attention of the researchers because ontologies can represent the knowledge of a given domain and researchers use the ontology to reason about it. Although the use of these semantic technologies tends to enhance technology-based educational processes, the lack of validation to improve the quality of learning in their use makes the educator feel reluctant to use them. This paper presents progress in the development of an ontology network, called AONet, that conceptualizes the e-assessment domain with the aim of supporting the semi-automatic generation of assessment, taking into account not only technical aspects but also pedagogical ones.Fil: Romero, Lucila. Universidad Nacional del Litoral; ArgentinaFil: North, Matthew. The college of Idabo; Estados UnidosFil: Gutierrez, Milagros. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; ArgentinaFil: Caliusco, Maria Laura. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin

Online tutor for research writing

English is the most prominent second language used in educational programs throughout the world. Unfortunately, there is a limitation of time and skill to guide students with learning the language and for evaluating their writings. Automated Writing Evaluation (AWE) tools would help in addressing this gap. In this thesis, I document a contribution to the field of Automated Writing Evaluation in the form of a new AWE tool called the Research Writing Tutor (RWT). The system design, user interface design, and features of this tool are introduced first, and then the findings obtained from an user evaluation study are reported. The website has been designed and developed to be user friendly. This tool could be of great use to graduate students and undergraduates in writing research reports, articles, and thesis or dissertations. Unlike most studies that concentrate on the accuracy of the AWE systems, this study aims at the usability and utility of the RWT in addition to the trust on automated systems

BiOSS: A system for biomedical ontology selection

In biomedical informatics, ontologies are considered a key technology for annotating, retrieving and sharing the huge volume of publicly available data. Due to the increasing amount, complexity and variety of existing biomedical ontologies, choosing the ones to be used in a semantic annotation problem or to design a specific application is a difficult task. As a consequence, the design of approaches and tools addressed to facilitate the selection of biomedical ontologies is becoming a priority. In this paper we present BiOSS, a novel system for the selection of biomedical ontologies. BiOSS evaluates the adequacy of an ontology to a given domain according to three different criteria: (1) the extent to which the ontology covers the domain; (2) the semantic richness of the ontology in the domain; (3) the popularity of the ontology in the biomedical community. BiOSS has been applied to 5 representative problems of ontology selection. It also has been compared to existing methods and tools. Results are promising and show the usefulness of BiOSS to solve real-world ontology selection problems. BiOSS is openly available both as a web tool and a web service.Instituto de Salud Carlos III; FIS-PI10/02180Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/211Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; ref. 209RT036

DisBot: a portuguese disaster support dynamic knowledge chatbot

This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management.info:eu-repo/semantics/publishedVersio