The E-rater® Scoring Engine: Automated Essay Scoring With Natural Language Processing

2003 ◽  
pp. 123-131
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automated Essay Scoring ◽  
Essay Scoring

scholarly journals Automated Essay Scoring using Ontology Generator and Natural Language Processing with Question Generator based on Blooms Taxonomy’s Cognitive Level

2019 ◽  
Vol 9 (1) ◽  
pp. 2448-2457
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Ridge Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Lasso Regression ◽  
Automated Essay Scoring ◽  
Essay Scoring

Essay writing examination is commonly used learning activity in all levels of education and disciplines. It is advantageous in evaluating the student’s learning outcomes because it gives them the chance to exhibit their knowledge and skills freely. For these reasons, a lot of researchers turned their interest in Automated essay scoring (AES) is one of the most remarkable innovations in text mining using Natural Language Processing and Machine learning algorithms. The purpose of this study is to develop an automated essay scoring that uses ontology and Natural Language Processing. Different learning algorithms showed agreeing prediction outcomes but still regression algorithm with the proper features incorporated with it may produce more accurate essay score. This study aims to increase the accuracy, reliability and validity of the AES by implementing the Gradient ridge regression with the domain ontology and other features. Linear regression, linear lasso regression and ridge regression were also used in conjunction with the different features that was extracted. The different features extracted are the domain concepts, average word length, orthography (spelling mistakes), grammar and sentiment score. The first dataset used is the ASAP dataset from Kaggle website is used to train and test different machine learning algorithms that is consist of linear regression, linear lasso regression, ridge regression and gradient boosting regression together with the different features identified. The second dataset used is the one extracted from the student’s essay exam in Human Computer Interaction course. The results show that the Gradient Boosting Regression has the highest variance and kappa scores. However, we can tell that there are similarities when it comes to performances for Linear, Ridge and Lasso regressions due to the dataset used which is ASAP. Furthermore, the results were evaluated using Cohen Weighted Kappa (CWA) score and compared the agreement between the human raters. The CWA result is 0.659 that can be interpreted as Strong level of agreement between the Human Grader and the automated essay score. Therefore, the proposed AES has 64-81% reliability level.

scholarly journals Automated Essay Scoring: A Survey of the State of the Art

2019 ◽  
Author(s):  
Zixuan Ke ◽  
Vincent Ng
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
The State ◽  
Educational Values ◽  
Automated Essay Scoring ◽  
Research Challenges ◽  
Essay Scoring ◽  
Made In

Despite being investigated for over 50 years, the task of automated essay scoring is far from being solved. Nevertheless, it continues to draw a lot of attention in the natural language processing community in part because of its commercial and educational values as well as the associated research challenges. This paper presents an overview of the major milestones made in automated essay scoring research since its inception.

Introduction

2006 ◽  
Vol 12 (2) ◽  
pp. 109-113
Author(s):  
JILL BURSTEIN ◽  
CLAUDIA LEACOCK
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Educational Software ◽  
Intelligent Tutoring ◽  
Classroom Teachers ◽  
Automated Essay Scoring ◽  
Essay Scoring

Researchers and developers of educational software have experimented with natural language processing (NLP) capabilities and related technologies since the 1960's. Automated essay scoring was perhaps the first application of this kind (Page 1966). Over a decade later, Writer's Workbench, a text-editing application, was developed as a tool for classroom teachers (MacDonald, Frase, Gingrich and Keenan 1982). Intelligent tutoring applications, though more in the spirit of artificial intelligence, were also being developed during this time (Carbonell 1970; Brown, Burton and Bell 1974; Stevens and Collins 1977; Burton and Brown 1982; Clancy 1987).

scholarly journals eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing

2019 ◽  
Vol 33 ◽  
pp. 9619-9625 ◽  
Author(s):  
H. Zhang ◽  
A. Magooda ◽  
D. Litman ◽  
R. Correnti ◽  
E. Wang ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Formative Feedback ◽  
Student Writing ◽  
Web Based ◽  
Essay Scoring ◽  
Text Writing

Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubricbased essay scoring to trigger formative feedback messages regarding students’ use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence
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