Enacting textual entailment and ontologies for automated essay grading in chemical domain

Groza, Adrian; Szabo, Roxana

doi:10.1109/cinti.2015.7382926

Cited by 6 publications

(3 citation statements)

References 22 publications

(22 reference statements)

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“…In 2001, Anderson and Krathwohl published the revised taxonomy that is composed of (1) remembering, (2) understanding, (3) applying, (4) analyzing, (5) starting from the lowest level which is (1) remembering that simply means recalling the significant knowledge from long term memory, (2) understanding is a level of learning making sense of the material you have learned, (3) applying which is basically using the knowledge gained in new ways, (4) analyzing is breaking the concept into parts and understand how each part is related to one another, (5) evaluating means making judgements based on a set of guidelines, and is (6) creating which means putting information together in a more innovative way.…”

Section: B Cognitive Level Of Blooms Taxonomymentioning

confidence: 99%

“…Even though the use of essay questions is beneficial to student's learning and assessment [6] the difficulties in the part of the assessors or the teachers were identified. The checking of the essay exams manually consumes a large amount of teacher's precious time [7] as the grading of essay type exam consumes a lot of time and a tedious task particularly in a huge population of students.…”

Section: Introductionmentioning

confidence: 99%

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

Contreras*¹,

Hilles²,

Abubakar³

2019

IJEAT

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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.

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Section: B Cognitive Level Of Blooms Taxonomymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Contreras*¹,

Hilles²,

Abubakar³

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…This scoring process is difficult to guarantee quality if it is done manually. Moreover, each class is taught by a different teacher can cause inequality in the value obtained by students because of the influence of differences in teacher experience [1], [2]. Therefore, an automated answer assessment research was developed.…”

Section: Introductionmentioning

confidence: 99%

Automatic Scoring Using Term Frequency Inverse Document Frequency Document Frequency and Cosine Similarity

Yulita

Untoro

Praseptiawan

et al. 2023

SJI

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Purpose: In the learning process, most of the tests to assess learning achievement have been carried out by providing questions in the form of short answers or essay questions. The variety of answers given by students makes a teacher have to focus on reading them. This scoring process is difficult to guarantee quality if done manually. In addition, each class is taught by a different teacher, which can lead to unequal grades obtained by students due to the influence of differences in teacher experience. Therefore the purpose of this study is to develop an assessment of the answers. Automated short answer scoring is designed to automatically grade and evaluate students' answers based on a series of trained answer documents.Methods: This is ‘how’ you did it. Let readers know exactly what you did to reach your results. For example, did you undertake interviews? Did you carry out an experiment in the lab? What tools, methods, protocols or datasets did you use The method used is TF-IDF-DF and Similarity and scoring computation. Theword weight used is the term Frequency-Inverse Documents Frequency -Document Frequency (TF-IDF-DF) method. The data used is 5 questions with each question answered by 30 students, while the students' answers are assessed by teachers/experts to determine the real score. The study was evaluated by Mean Absolute Error (MAE).Result: The evaluation results obtained Mean Absolute Error (MAE) with a resulting value of 0.123.Value: The word weighting method used is the Term Frequency Inverse Document Frequency DocumentFrequency (TF-IDF-DF) which is an improvement over the Term Frequency Inverse Document Frequency (TF-IDF) method. This method is a method of weighting words that will be applied before calculating the similarity of sentences between teachers and students.

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Essay Scoring Systems Using AI and Feature Extraction: A Review

Shetty¹,

Guruvyas²,

Patil³

et al. 2022

Proceedings of Third International Conference on Communication, Computing and Electronics Systems

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Enacting textual entailment and ontologies for automated essay grading in chemical domain

Cited by 6 publications

References 22 publications

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

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

Automatic Scoring Using Term Frequency Inverse Document Frequency Document Frequency and Cosine Similarity

Essay Scoring Systems Using AI and Feature Extraction: A Review

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