Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays

Ke, Zixuan; Inamdar, Hrishikesh; Lin, Hui; Ng, Vincent

doi:10.18653/v1/p19-1390

Cited by 23 publications

(15 citation statements)

References 16 publications

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“…com/c/ asap-sas/). Ke et al (2019) used a support vector machine to find the response score. In this method, features like Agreeability, Specificity, Clarity, Relevance to prompt, Conciseness, Eloquence, Confidence, Direction of development, Justification of opinion, and Justification of importance.…”

Section: Classification Based Modelsmentioning

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: Classification Based Modelsmentioning

confidence: 99%

An automated essay scoring systems: a systematic literature review

2021

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“…On the other hand, we do not focus on effectiveness which relates to an emotional appeal and the style of an argument. The previous models for quantitatively assessing the quality of an argument, no matter which dimension of quality is being addressed, predict a score with either feature-based machine learning (Persing et al, 2010;Ng, 2013, 2015;Ghosh et al, 2016;Wachsmuth et al, 2016;Ke et al, 2019;Wachsmuth and Werner, 2020) or neural networks (Ke et al, 2018;Lauscher et al, 2020). In contrast, our method uses a probabilistic model to calculate the validity.…”

Section: Pmentioning

confidence: 99%

Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes

Kondo¹,

Washio²,

Hayashi³

et al. 2021

Proceedings of the 8th Workshop on Argument Mining

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We propose a methodology for representing the reasoning structure of arguments using Bayesian networks and predicate logic facilitated by argumentation schemes. We express the meaning of text segments using predicate logic and map the boolean values of predicate logic expressions to nodes in a Bayesian network. The reasoning structure among text segments is described with a directed acyclic graph. While our formalism is highly expressive and capable of describing the informal logic of human arguments, it is too open-ended to actually build a network for an argument. It is not at all obvious which segment of argumentative text should be considered as a node in a Bayesian network, and how to decide the dependencies among nodes. To alleviate the difficulty, we provide abstract network fragments, called idioms, which represent typical argument justification patterns derived from argumentation schemes. The network construction process is decomposed into idiom selection, idiom instantiation, and idiom combination. We define 17 idioms in total by referring to argumentation schemes as well as analyzing actual arguments and fitting idioms to them. We also create a dataset consisting of pairs of an argumentative text and a corresponding Bayesian network. Our dataset contains about 2,400 pairs, which is large in the research area of argumentation schemes.

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“…Over the years, there has been a fair amount of work done in trait-specific essay grading, in essay traits such as organization (Persing et al, 2010;Taghipour, 2017), coherence (Somasundaran et al, 2014), thesis clarity (Persing and Ng, 2013;Ke et al, 2019), prompt adherence (Persing and Ng, 2014), argument strength (Persing and Ng, 2015;Taghipour, 2017;Carlile et al, 2018), stance (Persing and Ng, 2016), style (Mathias and Bhattacharyya, 2018b), and narrative quality (Somasundaran et al, 2018). Most of these works use feature engineering with classifiers to score the essay traits.…”

Section: Trait-specific Essay Gradingmentioning

confidence: 99%

Can Neural Networks Automatically Score Essay Traits?

Mathias¹,

Bhattacharyya²

2020

Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

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Essay traits are attributes of an essay that can help explain how well written (or badly written) the essay is. Examples of traits include Content, Organization, Language, Sentence Fluency, Word Choice, etc. A lot of research in the last decade has dealt with automatic holistic essay scoring -where a machine rates an essay and gives a score for the essay. However, writers need feedback, especially if they want to improve their writing -which is why traitscoring is important. In this paper, we show how a deep-learning based system can outperform feature-based machine learning systems, as well as a string kernel system in scoring essay traits.

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Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays

Cited by 23 publications

References 16 publications

An automated essay scoring systems: a systematic literature review

An automated essay scoring systems: a systematic literature review

Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes

Can Neural Networks Automatically Score Essay Traits?

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