Natural language processing in an intelligent writing strategy tutoring system

McNamara, Danielle S.; Crossley, Scott A.; Roscoe, Rod D.

doi:10.3758/s13428-012-0258-1

Cited by 138 publications

(125 citation statements)

References 31 publications

(28 reference statements)

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“…If a student is hesitant to publish its work or to write to someone can easily use this tool.Some online services are available like ginger 6 , grammarly 7 , bhashagiri 8 for Hindi etc. to auto correct grammar.…”

Section: E Autocorrecting Grammarmentioning

confidence: 99%

“…[6] are associated with support and evaluation of content knowledge acquisition [13]- [15]. The goal of intelligent tutoring is to help students work through problem sets in various domains (e.g, physics).…”

Section: Intelligent Tutoring Systemsmentioning

confidence: 99%

“…The artificial intelligent system provides writing strategy training with its main characteristics lies in natural language processing algorithms to assess essay quality and guide feedback to students. The natural language algorithms must undertake various linguistic, contextual and rhetorical features as writing in generally distinguished and highly subjective that differs from a person to person [6]. [7]provided with a CIRCSIM-Tutor which is NLP based intelligent tutoring system.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Natural Language Processing in Education

2017

IJAERD

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Section: E Autocorrecting Grammarmentioning

confidence: 99%

Section: Intelligent Tutoring Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of Natural Language Processing in Education

2017

IJAERD

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“…In order to come up with valid results, the study uses three corpora that represent three different proficiency levels. As far as the study's instrument is concerned, Coh-Metrix is acknowledged to provide accurate indices for the characteristics of texts on multiple levels of analysis, one of which is sentence and discourse relationships (McNamara, Crossley & Roscoe, 2013), which are the types of associations analysed in the study. Nevertheless, the validity of this automated tool lends itself to the purpose and the design of the paper.…”

Section: Meronymymentioning

confidence: 99%

Research Practices in Cohesive Devices’ Studies: Benefiting from Chaos

Abu-Ayyash

2017

TPLS

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Abstract-This study has as its main goal exploring the possibility of benefiting from the chaotic status that permeates discourse analysis research done on cohesive devices since their inception by Halliday and Hasan (1976). These research studies have been categorised based on the foci of the research questions and the genres to which the analysed texts belong. The research practices of the reviewed papers have been analysed, compared and contrasted, and evaluated apropos the research design, methodology, literature review and findings. The analysis of the research practices found that the dominant feature of the reviewed studies is incompatibility. On a cautious note, while it is acknowledged that variance is imbued with research purposes, variance can be the product of certain flaws in the research design. This paper seeks to serve three purposes: 1) To critically survey the empirical studies conducted on cohesive devices, 2) to identify the problematic practices that led to the chaotic status, and 3) to propose a host of practices that can be followed in future research done on cohesive devices. Concomitant with this review a brief account of the cohesive devices model that was espoused by the majority of the studies addressed in this paper.

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“…For instance, Writing Pal [53] is a system, trained on a corpora of essays, rated by a group of experts, to predict human rating of an essay, based on wide range of linguistic, rhetorical, and contextual features as predictors in a process of stepwise regression. Another team of researchers [54] focused on developing a system capable of ranking the readability of text based on multilevel linguistic features -as in features from word, semantics, syntax and cohesion levels.…”

Section: Machine Learning Approaches In Educational Content Analysismentioning

confidence: 99%

On Usage of Machine Learning for Natural Language Processing Tasks as Illustrated by Educational Content Mining

Melnikov¹,

Botov²,

Klenin³

2017

Ontology of Designing

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In this paper, we review most popular approaches to a variety of natural language processing (NLP) tasks, primarily those, which involve machine learning: from classics to state-of-the-art technologies. Most modern approaches can be separated into three rough categories: ones based on distributional hypothesis, those extracting information from graph-like structures (such as ontologies) and the ones that look for lexico-syntactic patterns in text documents. We focus mainly on the former of the three. Before the analysis can even begin, one of the important steps in preparation stage of NLP is the task of representing words and documents as numeric vectors. There exists a variety of approaches from the most simplistic Bag-of-Words to sophisticated machine learning methods, such as word embedding. Today, in the task of information retrieval the best quality for both English and Russian languages is achieved by approaches based on word embedding algorithms, trained on carefully picked text corpora in conjunction with deep syntactic and semantic analysis using various deep neural networks. A big variety of different machine learning algorithms is being applied for NLP tasks such as Part-of-Speech-tagging, text summarization, named entity recognition, document classification, topic and relation extraction and natural language question answering. We also review possibilities of applying these approaches and methods to educational content analysis, and propose the novel approach to utilizing NLP and machine learning capabilities in analyzing and synthesizing educational content in a form of a decision support systems.

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Natural language processing in an intelligent writing strategy tutoring system

Cited by 138 publications

References 31 publications

Influence of Natural Language Processing in Education

Influence of Natural Language Processing in Education

Research Practices in Cohesive Devices’ Studies: Benefiting from Chaos

On Usage of Machine Learning for Natural Language Processing Tasks as Illustrated by Educational Content Mining

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