Educational Applications of Natural Language Processing (NLP)

Besides scoring applications, ETS's Natural Language Processing (NLP) expertise has also resulted in other advanced capabilities to support student learning and assessment.

The Language Muse® Activity Palette

The Language Muse® Activity Palette is a web-based application designed to support English Learners (ELs). Aligned with reading standards, the tool automatically generates customizable activities aimed to help ELs build the academic language skills needed for deeper reading comprehension in content areas. The language-based activities are intended to support content comprehension and language skills development through activities that afford practice with vocabulary, sentence structures, discourse and summary writing. Teachers can use the tool to create and administer a "palette" of online activities for classroom texts that students can complete, and are scored online. Paper-and-pencil assignments are also available. Activities can be used for classroom discussion, independent or group work. While the tool targets ELs, activities may be useful for all students. Teachers can use their own texts, or the library of texts provided with the tool. This work has been funded by the Institute of Education Sciences (IES), United States Department of Education (R305A140472).

TextEvaluator® Capability

TextEvaluator® is a fully-automated, web-based technology tool designed to help teachers, textbook publishers and test developers select texts for use in instruction and assessment. TextEvaluator incorporates a patented measurement approach that goes beyond traditional readability dimensions of syntactic complexity and vocabulary difficulty to address complexity variation due to cohesion, concreteness, academic orientation, level of argumentation, degree of narrativity and degree of adherence to an interactive/conversational style. Resulting feedback about the complexity characteristics of texts can be expressed at either of two levels of granularity:

  • as a single, overall text complexity score aligned with the grade-by-grade text complexity guidelines specified in the Common Core State Standards
  • as a profile of eight component scores constructed to highlight the aspects of text variation expected to be most challenging within any specified text

Featured Publications

Below are some recent or significant publications that our researchers have authored on the subject of educational applications of natural language processing technology.

2017

  • Generating Language Activities in Real-Time for English Learners Using Language Muse®
    J. Burstein, N. Madnani, J. Sabatini, D. McCaffrey, K. Biggers, & K. Dreier. Proceedings of the Fourth Annual ACM Conference on Learning at Scale, Cambridge, MA.

    The paper describes the Language Muse® Activity Palette, a web-based language-instruction application that uses NLP algorithms and lexical resources to automatically generate language activities and support English language learners' content comprehension and language skills development. Pilot studies conducted using the application are discussed as well.

2016

2015

2014

2013

  • Measuring Cohesion: An Approach That Accounts for Differences in the Degree of Integration Challenge Presented by Different Types of Sentences
    K. M. Sheehan
    Educational Measurement: Issues and Practice, v32 n2 pp. 28–37, Win 2013

    This article will first review previous cohesion research, distinguishing between studies focused on the validity of proposed metrics, and studies that merely summarize the cohesion scores obtained for different types of texts and then will describe two general approaches for measuring cohesion. View the full article

  • A Two-Stage Approach for Generating Unbiased Estimates of Text Complexity
    K. M. Sheehan, M. Flor, & D. Napolitano
    Proceedings of the Second Workshop on Natural Language Processing for Improving Textual Accessibility (NLP4ITA), pp. 49–58, Atlanta, Ga. Association for Computational Linguistics.

    This paper presents a two-stage estimation technique that successfully addresses the tendency of automated text complexity tools to overestimate the complexity levels of informational texts, while simultaneously underestimating the complexity levels of literary texts. View citation record

  • A User Study: Technology to Increase Teachers' Linguistic Awareness to Improve Instructional Language Support for English Language Learners
    J. Burstein, J. Sabatini, J. Shore, B. Moulder, & J. Lentini
    In Proceedings of the Workshop for Improving Textual Accessibility in conjunction with the Annual Meeting of the North American Association for Computational Linguistics, Atlanta, Ga., June 14, 2013

    This paper discusses user study outcomes with teachers who used LanguageMuse, a web-based teacher professional development (TPD) application designed to enhance teachers' linguistic awareness, and support teachers in the development of language-based instructional scaffolding (support) for their English-language learners (ELL). View citation record

  • Lexical Tightness and Text Complexity
    M. Flor, B. Beigman Klebanov & K. M. Sheehan
    In Proceedings of the 2nd Workshop of Natural Language Processing for Improving Textual Accessibility (NLP4ITA), 2013, pp. 29–38

    This paper presents our methodology for building word association profiles for texts, it defines the measure of lexical tightness (LT) and describes the datasets used in the study, it presents our study of the relationship between LT and text complexity, describes application to poetry, evaluates an improved measure (LTR) and reviews related work. View the full paper

2012

2010

  • Generating Automated Text Complexity Classifications that are Aligned with Targeted Text Complexity Standards
    K. M. Sheehan, I. Kostin, Y. Futagi & M. Flor
    ETS Research Report No. RR-10-28

    Three approaches for generating improved measures of text complexity are discussed: expanding construct coverage, selecting more appropriate criterion scores, and accounting for genre effects. A text complexity measure that incorporates all three improvements is introduced. Validity analyses suggest that text complexity classifications generated via the proposed tool are closely aligned with complexity classifications provided by human experts. View full report

  • Automated Grammatical Error Detection for Language Learners
    C. Leacock, M. Chodorow, M. Gamon, & J. Tetreault
    Monograph in Synthesis Lectures on Human Language Technologies
    Morgan & Claypool

    This volume describes the types of constructions English-language learners find most difficult — constructions containing prepositions, articles and collocations — and it provides an overview of the automated approaches to identifying and correcting such learner errors. View citation record

2009

  • Opportunities for Natural Language Processing in Research Education
    J. Burstein
    Computational Linguistics and Intelligent Text Processing 10th International Conference, CICLing 2009, Mexico City, Mexico, March 1–7, 2009. Proceedings
    Springer

    This paper discusses emerging opportunities for natural language-processing researchers in the development of educational applications for writing, reading and content knowledge acquisition. View citation record

2008

2007

  • The Automated Text Adaptation Tool
    J. Burstein, J. Shore, J. Sabatini, Y. Lee, & M. Ventura
    Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), pp. 3–4
    Association for Computational Linguistics

    This paper introduces the Automated Text Adaptation Tool v.1.0 (ATA v.1.0), an innovative, educational tool that automatically generates text adaptations similar to those teachers might create. View citation record

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