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.

TextEvaluator® Capability

ETS's TextEvaluator® (formerly known as SourceRater) tool represents a new approach for modeling text complexity, designed to help test developers evaluate source material for use in developing new reading comprehension passages and items. The TextEvaluator tool combines a large, cognitively based feature set with advanced psychometric techniques in order to provide text complexity classifications that are highly correlated with classifications provided by experienced educators. This feature set extends beyond the limited dimensions of text complexity assessed by other methods (such as sentence length and vocabulary) to encompass text-level cohesion and account for differences between different text genres.

LanguageMuse Application

LanguageMuse is a web-based, instructional authoring application intended to support K–12 teachers in the development of curricular materials for English-language learners (ELLs). The application offers linguistic feedback that highlights vocabulary, sentence structures and discourse relations found in classroom texts that may be unfamiliar to ELLs. The linguistic feedback supports teachers in creating linguistically-informed lesson plans, texts, activities and assessments with appropriate scaffolding. The LanguageMuse application has been used in formal teacher professional development settings to help teachers cultivate linguistic awareness so that they are better able to create a curriculum that addresses students' English language learning needs. The application contains self-guided professional development, so teachers can complete that portion on their own and continue to use the application in the classrooms to more easily design scaffolded materials appropriate to every K–12 grade level.

Automated Test Item Generation

Another area in which ETS has applied its natural language processing technology is in the automated generation of test items. This includes research both on completely automated generation of items from item models (in order to reduce the cost of item development and control item difficulty) and semi-automated item creation tools to help assessment developers identify appropriate source material for items or create draft items that can be augmented and edited by experienced item writers.

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.






  • 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. Learn more about this publication




  • 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. Learn more about this publication


  • Model Analysis and Model Creation: Capturing the Task-Model Structure of Quantitative Item Domains
    P. Deane, E. A. Graf, D. Higgins, Y. Futagi, & R. Lawless
    ETS Research Report RR-06-11

    This study focuses on the relationship between item modeling and evidence-centered design (ECD); it considers how an appropriately generalized item modeling software tool can support systematic identification and exploitation of task-model variables, and then examines the feasibility of this goal, using linear-equation items as a test case. The first half of the study examines task-model structures for linear equations and their relevance to item difficulty within ECD. The second half of the study presents prototype software, a Model Creator system for pure math items, designed to partially automate the creation of variant item models reflecting different combinations of task-model variables. The prototype is applied to linear equations but is designed to generalize over a range of pure mathematical content types. Learn more about this publication

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