Automated Essay Evaluation Based on Fusion of Fuzzy Ontology and Latent Semantic Analysis

Darwish, Saad M.; Mohamed, Sherine Kh.

doi:10.1007/978-3-030-14118-9_57

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…They help us determine the degree to which the essay material is semantically coherent on a global and local scale. Positive spatial correlations in an essay suggest that its sections are coherent and that information is presented in a logical progression, as discussed in the author's previous work [53]. Moran's (I), Geary's (C), and Getis's (G) are all unique spatial autocorrelation metrics.…”

Section: Spatial Autocorrelationmentioning

confidence: 82%

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

2023

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Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.

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Section: Spatial Autocorrelationmentioning

confidence: 82%

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

2023

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“…The semantic attribute is used to verify the correctness of the essay, Chandrasekaran, D., and Mago, V. (2020), D. Higgins, J. Burstein, D. Marcu, and C. Gentile (2004), Darwish, S. M., and Mohamed, S. K. (2019).…”

Section: Automated Essay Evaluation Systems and Their Approachesmentioning

confidence: 99%

Optimization of Window Size for Calculating Semantic Coherence Within an Essay

Srivastava*

Dhanda²,

Shrivastava³

2022

ADCAIJ

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Over the last fifty years, as the field of automated essay evaluation has progressed, several ways have been offered. The three aspects of style, substance, and semantics are the primary focus of automated essay evaluation. The style and content attributes have received the most attention, while the semantics attribute has received less attention. A smaller fraction of the essay (window) is chosen to measure semantics, and the essay is broken into smaller portions using this window. The goal of this work is to determine an acceptable window size for measuring semantic coherence between different parts of the essay with more precision.

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“…Fusion of Multidimensional Information. Multidimensional data fusion is the premise of applying big data technology in specific fields, and early behavioral data fusion methods mainly focus on semantics [19] and similarity [20]. Inspired by the multisource data fusion theory, many researchers have integrated various data sources on campus, including data on motion trajectories, student learning, student consumption, and classroom attendance, and used support vector machines (SVM) and machine learning (ML) classifiers, etc.…”

Section: Research On Student Behavior Portrait Based On Deepmentioning

confidence: 99%

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

Wang

Wen

Zhou

et al. 2022

Wireless Communications and Mobile Computing

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With the wide application of information technologies such as big data, the Internet of Things, and cloud computing, college students have accumulated a large amount of personal information and daily behavior data in their daily studies and life. How to dynamically integrate multidimensional information of students to build accurate student portraits, using multi-indicator data of student behavior and comment texts, and finding out students with abnormal behavior from among many students has become an important problem to be solved. This paper proposes an abnormal behavior prediction method integrating multiple indicators of student behavior and text information (ABPM-IMISBTI) for the problem of abnormal behavior prediction of college students in the big data environment. First, given the problems of multidimensionality, timeliness, and dynamics of student behavior information fusion in the construction of student behavior portraits, by integrating students’ objective tags and subjective tags, an optimized K-means algorithm based on a cloud platform environment is proposed. Second, aiming at the problem of insufficient text information analysis in the analysis of students’ abnormal behavior, the ABPM-IMISBTI method is proposed to solve the prediction of students’ abnormal behavior through long and short-term memory networks (LSTM) combined with student behavior multi-index data and text information. Finally, this paper takes student achievement prediction as an example for verification. The experimental results show that, compared with other prediction methods, the ABPM-IMISBTI method proposed in this paper can improve the accuracy of student behavior prediction, and then quickly determine the abnormal behavior of students, to improve the level of education management in universities and promote the development of safe campuses, smart campuses, and smart education.

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Automated Essay Evaluation Based on Fusion of Fuzzy Ontology and Latent Semantic Analysis

Cited by 15 publications

References 17 publications

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

Optimization of Window Size for Calculating Semantic Coherence Within an Essay

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

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