---
title: "A User Study — paper by Burstein et al. (2013)"
description: "The authors present Language Muse, a web-based teacher professional development (TPD)… 52 references, cited by 4 papers here."
authors: ["Jill Burstein", "John Sabatini", "Jane Shore", "Brad Moulder", "Jennifer Lentini"]
year: 2013
venue: "Proceedings of the Workshop on Natural Language Processing for Improving Textual Accessibility"
source: "https://aclanthology.org/W13-1501/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/burstein-2013-user-study-technology-increase-teachers/"
---
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# A User Study: Technology to Increase Teachers' Linguistic Awareness to Improve Instructional Language Support for English Language Learners

**Authors:** [Jill Burstein](https://md.research.elamur.ai/free/en/authors/jill-burstein/), [John Sabatini](https://md.research.elamur.ai/free/en/authors/john-sabatini/), Jane Shore, Brad Moulder and Jennifer Lentini · **Year:** 2013 · **Venue:** Proceedings of the Workshop on Natural Language Processing for Improving Textual Accessibility · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W13-1501/) · [PDF](https://aclanthology.org/W13-1501.pdf) · [OpenAlex](https://openalex.org/W2252046711)

**Citations:** 8 citations (OpenAlex, 2026-10-14) · [4](https://md.research.elamur.ai/free/en/papers/burstein-2013-user-study-technology-increase-teachers/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Content-area teachers in U.S. K-12 schools increasingly teach English language learners (ELLs) but often lack training in spotting the linguistic features that make classroom texts hard for these students. The authors present Language Muse, a web-based teacher professional development (TPD) application from ETS. Alongside training videos and readings, it has a module that uses natural language processing to automatically flag potentially unfamiliar language at four levels: vocabulary, phrases, sentences and discourse. Teachers then use this feedback to build lesson plans with language-based scaffolding, activities and assessments. The tool rests on two ideas. First, teachers must recognise sources of difficulty before they can support learners. Second, finding such features by hand takes too long, even for linguistically aware teachers. In pilots, teachers took assessments before and after using the tool. According to the authors, their linguistic knowledge and awareness grew, and they became better at designing suitable language instruction. That instruction drew on the system's feedback. The work matters because it applies NLP to support the human reader's teacher, rather than to produce a single readability score.

## Key points
- The ELL population in U.S. public schools grew by 51% between 1997 and 2009. Many of these students sit in mainstream subject classes without extra English instruction.
- Language Muse is a TPD application. Its automated feedback covers vocabulary, phrasal, sentential and discourse levels.
- The design is grounded in research on several ELL difficulties:
  - Tier 1/2/3 academic vocabulary
  - morphology and cognates (e.g., English/Spanish)
  - polysemous content words
  - figurative terms and cultural references
  - long multi-clause sentences
  - mixed discourse relations, anaphora and connectors
- Evaluation used pre-post teacher assessments in a pilot TPD setting. The paper reports gains in teachers' linguistic knowledge and in the quality of their ELL-oriented instruction.
- The related work contrasts the tool with two other systems:
  - REAP, which uses language-model readability to select texts for ESL students
  - WERTi, which highlights problematic forms such as determiners, prepositions, wh-questions and phrasal verbs for learners

## Interesting ideas
- Instead of collapsing difficulty into one grade-level number, the system marks specific features at separate linguistic levels, so a human can see *why* a text is hard.
- Difficulty is treated as relative to the reader: what is easy for a native speaker on grade level may be opaque to an ELL reading below grade level.
- Common words with a specialised domain sense, like "prime" in maths, invite a misleading everyday reading.
- Readers must track reference across pronouns, synonyms, paraphrases and demonstratives like "this" or "those", and connectors such as "because" or "therefore" build cohesion.
- Success is judged by measuring human users before and after they use the tool, not only by system accuracy.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (answer): Mova's readers could produce a level-by-level difficulty report on a school text (word, phrase, clause, discourse) that points to tree and frame evidence, making their reading of the text explainable to a teacher.
- **I2** (learn): evaluation sets could be split by reader profile (e.g., figurative compounds, cultural references, polysemous domain terms) to show which phenomena Mova itself fails on, rather than reporting one average score.
- **I3** (world): the compiled ontology could flag domain-shifted senses from WordNet when a text's topic is maths or science, so word problems do not bind the everyday meaning.
- **I4** (frames): coreference could treat demonstratives that point back to whole ideas as a distinct case, and connectors could be mapped to explicit cause or contrast links between frames.
- **I5** (learn): explanation outputs could be tested by checking whether a person answers text questions better after seeing Mova's tree-and-frame explanation, giving an outcome-based check on explainability.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Educational Testing Service](https://md.research.elamur.ai/free/en/ngos/educational-testing-service/) — Nonprofit, Princeton, United States · 27 works · 2,212 citations

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## Cite

Cite as: Jill Burstein, John Sabatini, Jane Shore, Brad Moulder and Jennifer Lentini. 2013. A User Study: Technology to Increase Teachers' Linguistic Awareness to Improve Instructional Language Support for English Language Learners. Proceedings of the Workshop on Natural Language Processing for Improving Textual Accessibility.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{burstein2013user,
  title = {{A User Study: Technology to Increase Teachers' Linguistic Awareness to Improve Instructional Language Support for English Language Learners}},
  author = {Jill Burstein, John Sabatini, Jane Shore, Brad Moulder and Jennifer Lentini},
  booktitle = {Proceedings of the Workshop on Natural Language Processing for Improving Textual Accessibility},
  year = {2013},
  url = {https://aclanthology.org/W13-1501/}
}
```

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*Updated 1 November 2026*