The use of the computer in analyzing student essays

Page, Ellis B.

doi:10.1007/bf01419938

Cited by 89 publications

(47 citation statements)

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“…However, in the broader field of educational technology, there has been quite a lot of research on using language technologies more generally, especially in the areas of automatic essay grading (Burstein et al 1998(Burstein et al , 2001Page 1968;Page and Petersen 1995;Landauer and Dumais 1997;Foltz et al 1998;Laham 2000) and tutorial dialogue systems (Graesser et al 1998;Rosé et al 2001;Aleven et al 2003;Evens and Michael 2003;Litman et al 2006;VanLehn et al 2007). What is different about the work presented here is that we focus on an analysis of the process of conversation rather than the content.…”

Section: Application Of Text Classification Approaches In Csclmentioning

confidence: 99%

Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning

Rosé

Wang

Cui

et al. 2008

Computer Supported Learning

250

163

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In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners' interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in.

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Section: Application Of Text Classification Approaches In Csclmentioning

confidence: 99%

Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning

Rosé

Wang

Cui

et al. 2008

Computer Supported Learning

250

163

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“…Office of Education, Page and Paulus (see Page 1966Page , 1968aPage , 1968b for sununarizations of tbis work) developed techniques for scoring essays and for providing instructional feedback to students through computer analysis of essays. Indices were developed that predicted judgmental scores through a procedure adapted from Diederich's (1974) analytic scoring procedure.…”

Section: Technological Developmentsmentioning

confidence: 99%

The Direct Assessment of Writing Skill: A Measurement Review

Breland

1983

ETS Research Report Series

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“…There is a large body of literature with regards to ATS systems of text produced by nonnative English-language learners (Page, 1968;Attali and Burstein, 2006;Rudner and Liang, 2002;Elliot, 2003;Landauer et al, 2003;Briscoe et al, 2010;Yannakoudakis et al, 2011;Sakaguchi et al, 2015, among others), overviews of which can be found in various studies (Williamson, 2009;Dikli, 2006;Shermis and Hammer, 2012). Implicitly or explicitly, previous work has primarily treated text scoring as a supervised text classification task, and has utilized a large selection of techniques, ranging from the use of syntactic parsers, via vectorial semantics combined with dimensionality reduction, to generative and discriminative machine learning.…”

Section: Introductionmentioning

confidence: 99%

Automatic Text Scoring Using Neural Networks

Alikaniotis¹,

Yannakoudakis²,

Rei³

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

199

182

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Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking. However, in order to achieve good performance, the predictive features of the system need to be manually engineered by human experts. We introduce a model that forms word representations by learning the extent to which specific words contribute to the text's score. Using Long-Short Term Memory networks to represent the meaning of texts, we demonstrate that a fully automated framework is able to achieve excellent results over similar approaches. In an attempt to make our results more interpretable, and inspired by recent advances in visualizing neural networks, we introduce a novel method for identifying the regions of the text that the model has found more discriminative.

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The use of the computer in analyzing student essays

Cited by 89 publications

References 0 publications

Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning

Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning

The Direct Assessment of Writing Skill: A Measurement Review

Automatic Text Scoring Using Neural Networks

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