Approaches For Automation In Assisting Evaluator For Grading Of Answer Scripts: A Survey

Patil, Rudragouda G.; Ali, Syed Zakir

doi:10.1109/ccaa.2018.8777664

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The essential thought of naïve Bayes strategy is to discover the probabilities of categories related to messages by utilizing the joint probabilities of classes and words. It is given by (4).…”

Section: Naïve Bayes Classification Evaluationmentioning

confidence: 99%

“…𝑃(𝑥) (4) In (4), 𝑃(𝑦|𝑥) is the prosterior probability of class (𝑦) given predicator (𝑥), 𝑃(𝑦) is the prior probability of the class, 𝑃(𝑥) is the prior probability of the predicator and 𝑃(𝑥|𝑦) is the likelihood which is the probability of predicator for the given class. The dependent feature vector (x1 ,x2 ,……,xn ) and the class Y being given.…”

Section: 𝑃(𝑦|𝑥) = 𝑃(𝑥|𝑦)𝑃(𝑦)mentioning

confidence: 99%

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

Sadanand

Guruvyas

Patil

et al. 2022

IJECE

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<span lang="EN-US">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. <a name="_Hlk108785338"></a>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.</span>

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Section: Naïve Bayes Classification Evaluationmentioning

confidence: 99%

Section: 𝑃(𝑦|𝑥) = 𝑃(𝑥|𝑦)𝑃(𝑦)mentioning

confidence: 99%

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

Sadanand

Guruvyas

Patil

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…A more intelligent grading system based on machine learning and domain-specific knowledge base would result in enhanced valuation. In the recent years the focus of research has shifted to grading of descriptive/subjective questions of an open-ended nature [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Recent Trends in Answer Script Evaluation – A Literature Survey

Maya¹,

Javed²,

Muralidhara³

2021

Atlantis Highlights in Computer Sciences

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Assessment of answer scripts is an integral part of an examination and education system. A fair, consistent, unbiased, and correct valuation ensures the integrity of an examination system and is important for all education institutions. Since manual valuation is cumbersome and can be biased or influenced by the perception/mood of the evaluator, automatic grading of scripts has become very relevant. Automatic short answer grading (ASAG) techniques have been widely researched in the last decade and have assumed increased relevance because of online teaching and examinations during the Covid-19 pandemic. This review paper focuses on the recent works in the area of automatic answer grading and compares the techniques, methodologies employed, and the consequent results to evaluate their effectiveness. It discusses the advantages and limitations of the techniques by systematically categorizing the questions into both long/short as well as open-ended/close-ended questions and suggests a new model for improving the grading outcomes.

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Automated Grading of Essays: A Review

Borade

Netak

2021

Intelligent Human Computer Interaction

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Approaches For Automation In Assisting Evaluator For Grading Of Answer Scripts: A Survey

Cited by 5 publications

References 15 publications

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

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

Recent Trends in Answer Script Evaluation – A Literature Survey

Automated Grading of Essays: A Review

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