Automated Essay Grading using Machine Learning Algorithm

Ramalingam, V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu

doi:10.1088/1742-6596/1000/1/012030

Cited by 35 publications

(13 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Within this context, using ML algorithms to grade essays can reduce the workload of educators and provide more objectivity during the grading process. A common approach to creating an essay grading algorithm is to first collect a large pool of essays which have characteristics that are computationally measurable (e.g., sentence length, word frequency distributions, grammar, and spelling), and have been scored by humans (Ramalingam et al, 2018). This allows the algorithm to first learn the characteristics which are important for grading an essay.…”

Section: Challenge Descriptionmentioning

confidence: 99%

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

Injadat,

Moubayed,

Nassif

et al. 2021

Preprint

View full text Add to dashboard Cite

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.

show abstract

Section: Challenge Descriptionmentioning

confidence: 99%

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

Injadat,

Moubayed,

Nassif

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…AES is an application that evaluates and predict scores [ written in a text format. The notion of AES [7][8] emerged in 1966 and enhanced by Ellis Batten Page [10] who is widely known as the father of automated essay scoring. Page developed an AES called Project Essay Grader (PEG) to address the huge demand placed on teachers in evaluating essay exams.…”

Section: A Automated Essay Scoringmentioning

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. Moreover, the discernment of the subjectivity of the scoring procedure must also be given into consideration because it may result in different score outcomes.…”

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

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.

show abstract

“…Essay is important in assessing academic excellence by measuring students' ability to connect different ideas (Ramalingam et al, 2018). Essay assessment questions can be divided into two types: long essay and short essay.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the limited number of words in short essay, every word or phrase in each sentence is significant in contributing some marks to the essay assessment. Integrated Essay Assessment (IEA) if proven effective not only will reduce assessment time but the comparison with human assessment will also produce a level that is almost similar (Ramalingam et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Sentence Similarity Measurement Based Onthematic Role and Semantic Networktechniques

Hamza¹,

Aziz²,

Omar³

2019

IJSECS

View full text Add to dashboard Cite

Automated Short Essay Assessment is a subjective assessment that emphasizes important contents more than writing style. Word Order Technique and Syntactic-Semantic Knowledge Technique have been used in previous researches. However, it cannot differentiate sentence pair that is not similar semantically and only proven to produce excellent result on short sentence. Thematic Role annotation for every significant argument seems able to provide information regarding the relations between the word. Wordnet Semantics Network calculates semantic similarities of two synsets (token) by taking into account the depth of semantic relations. This study is conducted on Compiler course set in Malay that comprise of passive, simple, negative, mixed and complex questions. To prove the effectiveness of the techniques, the test result that apply Grammar Pola Technique was used as a benchmark because the study uses the same data set. The average of f-measure test accuracy rate is 93.53% when using Thematic Role and Semantic Network Techniques compared to 82.36% accuracy rate when using Pola Grammar Technique. The result of Thematic Role can be used on research involving Malay linguistic to test sentence structure matching that has verb by considering types of sentence.

show abstract

Automated Essay Grading using Machine Learning Algorithm

Cited by 35 publications

References 1 publication

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

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

Sentence Similarity Measurement Based Onthematic Role and Semantic Networktechniques

Contact Info

Product

Resources

About