Automated Essay Scoring with Ontology based on Text Mining and NLTK tools

Contreras, Jennifer O.; Hilles, Shadi M. S.; Abubakar, Zainab Binti

doi:10.1109/icscee.2018.8538399

Cited by 23 publications

(10 citation statements)

References 2 publications

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“…With these features, the Ridge regression model was used, and the accuracy they got 0.887. Contreras et al (2018). Proposed Ontology based on text mining in this model has given a score for essays in phases.…”

Section: Rq3 Which Are the Evaluation Metrics Available For Measuring The Accuracy Of Algorithms?mentioning

confidence: 99%

An automated essay scoring systems: a systematic literature review

2021

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Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.

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Section: Rq3 Which Are the Evaluation Metrics Available For Measuring The Accuracy Of Algorithms?mentioning

confidence: 99%

An automated essay scoring systems: a systematic literature review

2021

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“…For Chinese words, we used JIEBA, 1 a popular module that segments each sequence, as applied in several previous studies (Day and Lee 2016;Li et al 2018;Jiang et al 2019). Natural Language Tool kit (hereafter NLTK), 2 a Python-based tool for processing human language data (Bird et al 2009;Contreras et al 2018), represents each document with comments as a vector of words, useful for word segmentation and stop-word removal in processing English text.…”

Section: Data Pre-processingmentioning

confidence: 99%

Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

Yang

Hsu

Löfgren

et al. 2021

Soc. Netw. Anal. Min.

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While the salience of social media platforms on modern interactive communication between diverse social actors has been demonstrated, less academic attention has been paid to comparisons between framed topics and user interactions across social media platforms, such as Twitter and Weibo. This article suggests text mining and natural language processing tools for cross-platform comparative social media studies, based on Latent Dirichlet Allocation (LDA) and social network analysis. This study illustrates how the suggested topic models and data processing algorithms can be applied to a real-life example (U.S.-China trade war discourse on social media), and experimented the methods on social media text mining data, revealing differences between user interactions on Twitter, predominantly "Western," and Weibo, largely representing Chinesespeaking users. We discuss the strengths and weaknesses of the suggested machine learning algorithms for comparative social media studies.

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“…Experiment results showed that the addition of LSA over syntactic features improves the scoring performance of AES (Omar & Mezher, 2016). Many contents similarity-based AES used LSA or any of its variations in deriving grades or scores (Awaida et al, 2019;Amalia et al, 2019;Alghamdi, et al, 2014;Contreras et al, 2018;Ong et al, 2011;Shehab et al, 2018).…”

Section: Content Similarity Frameworkmentioning

confidence: 99%

“…These were the essays' surface features, and they are found to be useful in grading essays (Ong et al, 2011). In addition, there were works reported to facilitate external knowledge bases such as WordNet (Omar & Mezher, 2016;Shehab et al, 2018) and ontology (Contreras et al, 2018) to improve grading efficacy.…”

Section: Content Similarity Frameworkmentioning

confidence: 99%

A Comprehensive Review of Automated Essay Scoring (AES) Research and Development

Lim¹,

Bong²,

Wong³

et al. 2021

JST

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Automated Essay Scoring (AES) is a service or software that can predictively grade essay based on a pre-trained computational model. It has gained a lot of research interest in educational institutions as it expedites the process and reduces the effort of human raters in grading the essays as close to humans’ decisions. Despite the strong appeal, its implementation varies widely according to researchers’ preferences. This critical review examines various AES development milestones specifically on different methodologies and attributes used in deriving essay scores. To generalize existing AES systems according to their constructs, we attempted to fit all of them into three frameworks which are content similarity, machine learning and hybrid. In addition, we presented and compared various common evaluation metrics in measuring the efficiency of AES and proposed Quadratic Weighted Kappa (QWK) as standard evaluation metric since it corrects the agreement purely by chance when estimate the degree of agreement between two raters. In conclusion, the paper proposes hybrid framework standard as the potential upcoming AES framework as it capable to aggregate both style and content to predict essay grades Thus, the main objective of this study is to discuss various critical issues pertaining to the current development of AES which yielded our recommendations on the future AES development.

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Automated Essay Scoring with Ontology based on Text Mining and NLTK tools

Cited by 23 publications

References 2 publications

An automated essay scoring systems: a systematic literature review

An automated essay scoring systems: a systematic literature review

Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining

A Comprehensive Review of Automated Essay Scoring (AES) Research and Development

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