Restricted accessResearch articleFirst published online 2014-7
From Teacher Professional Development to the Classroom: How NLP Technology Can Enhance Teachers' Linguistic Awareness to Support Curriculum Development for English Language Learners
This article reports on two studies using Language MuseSM (LM), a web-based, teacher professional development (TPD) application designed to enhance teachers' linguistic awareness and to support teachers in the development of language-based instructional scaffolding for English language learners (ELL). In Study 1, in-service teachers enrolled in certification courses learned how to use the natural language processing (NLP) component of LM to support their knowledge, awareness, practice, and application in designing instruction to ELLs. Measurement outcomes from the TPD study indicated that productive use of linguistic feedback in Language Muse led to some increases in teachers' linguistic knowledge and awareness, and in their ability to develop appropriate language-based instruction for ELLs. In Study 2, a school-based study was conducted to evaluate the feasibility of implementing LM in authentic settings. Outcomes from the study demonstrate the feasibility of implementing the Language Muse program in applied school and classroom contexts.
Get full access to this article
View all access options for this article.
References
1.
AdgerC. T.SnowC.ChristianD. (2002). What teachers need to know about language. Washington, DC: Center for Applied Linguistics.
2.
AugustD. (2003). Supporting the development of English literacy in English language learners: Key issues and promising practices (Report No. 61). Baltimore, MD: Johns Hopkins University Center for Research on the Education of Students Placed at Risk.
3.
BarzilayR.LapataM. (2008). Modeling local coherence: An entity-based approach. Computational Linguistics, 43, 1–34.
4.
BeckI. L.McKeownM. G.KucanL. (2008). Creating robust vocabulary: Frequently asked questions and extended examples. New York, NY: Guilford Press.
5.
BerningerV.AbbottR.NagyW.CarlisleJ. (2009). Growth in phonological, orthographic, and morphological awareness in grades 1–6. Journal of Psycholinguistic Research, 39, 141–163.
6.
BursteinJ.SabatiniJ.ShoreJ. (2014). Developing NLP applications for educational problem spaces, Oxford handbook of computational linguistics, MitkovRuslan (Ed.). New York, NY: Oxford University Press.
7.
BursteinJ.SabatiniJ.ShoreJ.MoulderB.LentiniJ. (2013). A user study: Technology to increase teachers' linguistic awareness to improve instructional language support for English language learners. In Proceedings of the Workshop for Improving Textual Accessibility in conjunction with the Annual Meeting of the North American Association for Computational Linguistics, Atlanta, Georgia, June 14, 2013.
8.
BursteinJ.ShoreJ.SabatiniJ.MoulderB.HoltzmanS.PedersenT. (2012). The Language Muse system: Linguistically focused instructional authoring ETS RR-12-21. Princeton, NJ: ETS.
9.
CalderónM. (2007). Teaching reading to English language learners, grades 6–12: A framework for improving achievement in the content areas. Thousand Oaks, CA: Corwin Press.
10.
CalderónM.AugustD.SlavinR.CheungA.DuránD.MaddenN. (2005). Bringing words to life in classrooms with English language learners. In HiebertA.KamilM. (Eds.), Research and development on vocabulary. Mahwah, NJ: Lawrence Erlbaum Associates.
11.
CarloM. S.AugustD.McLaughlinB.SnowC. E.DresslerC.LippmanD. N. (2004). Closing the gap: Addressing the vocabulary needs of English language learners in bilingual and mainstream classrooms. Reading Research Quarterly, 39, 188–215.
12.
ColemanD.PimentelS. (2011). Publishers' criteria for the Common Core State Standards in English Language Arts and Literacy, grades 3–12. Washington, DC: National Governors Association Center for Best Practices and Council of Chief State School Officers.
13.
Collins-ThompsonK.CallanJ. (2004). A language modeling approach to predicting reading difficulty. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (pp. 193–200). Stroudsburg, PA: Association for Computational Linguistics.
14.
Darling-HammondL. (2000). Teacher quality and student achievement: A review of state policy evidence. Education Policy Analysis Archives, 8.
15.
FleschR. (1948). A new readability yardstick. Journal of Applied Psychology, 32, 221–233.
16.
FlinspachS. L.ScottJ. A.SamwayK. D.MillerT. (2008, March). Developing cognate awareness to enhance literacy: Importante y necesario. Paper presented at the annual meeting of the American Educational Research Association, New York, NY.
FrancisD. J.RiveraM.LesauxN.KiefferM.RiveraH. (2006). Practical guidelines for the education of English language learners: Research-based recommendations for instruction and academic interventions. Portsmouth, NH: Research Corporation, Center on Instruction. Retrieved from http://www2.ed.gov/about/inits/ed/lep-partnership/interventions.pdf
19.
GándaraP.Maxwell-JollyJ.DriscollA. (2005). Listening to teachers of English language learners: A survey of California teachers' challenges, experiences, and professional development needs. Sacramento, CA: The Regents of the University of California. Retrieved from http://www.cftl.org/documents/2005/listeningforweb.pdf
20.
GoldenbergC. (2008). Teaching English language learners: What the research does—and does not—say. American Educator, 32, 8–21.
21.
GoldmanS. R.MurrayJ. D. (1992). Knowledge of connectors as cohesion devices in text: A comparative study of native-English and English-as-a-second-language speakers. Journal of Educational Psychology, 84, 504–519.
22.
GoldmanS. R.RakestrawJ. A.Jr. (2000). Structural aspects of constructing meaning from text. In KamilM. L.MosenthalP. B.PearsonP. D.BarrR. (Eds.), Handbook of reading research (Vol. III; pp. 311–335). Mahwah, NJ: Lawrence Erlbaum Associates.
23.
GraesserA. C.McNamaraD. S.KulikowichJ. M. (2011). Coh-metrix: Providing multilevel analyses of text characteristics. Educational Researcher, 40, 223–234.
24.
GraesserA. C.McNamaraD. S.LouwerseM. M. (2003). What do readers need to learn in order to process coherence relations in narrative and expository text? In SweetA. P.SnowC. E. (Eds.), Rethinking reading comprehension (pp. 82–98). New York, NY: Guilford.
25.
GreenC.FooteM.WalkerC.ShumanC. (2010). From questions to answers: Education faculty members learn about English learners. In SzaboS.SampsonM. B.FooteM. M.Falk-RossF. (Eds.), Mentoring literacy professionals: Continuing the spirit of CRA/ALER after 50 years (pp. 113–125). Commerce, TX: Texas A&M University Press.
26.
HeilmanM.ZhaoL.PintoJ.EskenaziM. (2008). Retrieval of reading materials for vocabulary and reading practice. In Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications (pp. 80–88). Stroudsburg, PA: Association for Computational Linguistics.
27.
KiefferM. J.LesauxN. K. (2008). The role of derivational morphology in the reading comprehension of Spanish-speaking English language learners. Reading and Writing, 21, 783–804.
28.
KintschW. (1998). Comprehension: A paradigm for comprehension. Cambridge, UK: Cambridge University Press.
29.
KralJ. M. (2004). The effect of pronoun reference drill and practice on the reading comprehension scores of middle school ELLs. Unpublished master's thesis. Hamline University, St. Paul, MN.
30.
LesauxN. K.KiefferM. J.FallerS. E.KelleyJ. G. (2010). The effectiveness and ease of implementation of an academic vocabulary intervention for linguistically diverse students in urban middle schools. Reading Research Quarterly, 45, 196–228.
31.
MarcuD. (1999). Discourse trees are good indicators of importance in text. In ManiI.MayburyM. T. (Eds.), Advances in automatic text summarization (pp. 123–136). Cambridge, MA: MIT Press.
32.
MartínezJ. F.BaileyA. L.KerrD.HuangB. H.BeauregardS. (2010). Measuring opportunity to learn and academic language exposure for English language learners in elementary science classrooms (CREST Report 767). Los Angeles, CA: National Center for Research on Evaluation, Standards, and Student Testing, Graduate School of Education & Information Studies, UCLA.
33.
MeurersW. D.ZiaiR.AmaralL.BoydA.DimitrovA.MetcalfV. (2010). Enhancing authentic web pages for language learners. In Proceedings of the Fifth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 10–18). Stroudsburg, PA: Association for Computational Linguistics.
34.
MeyerB. J. F. (2003). Text coherence and readability. Topics in Language Disorders, 23, 204–221.
35.
MihalceaR.SinhaR.McCarthyD. (2010). SemEval-2010 task 2: Cross-lingual lexical substitution. In Proceedings of SemEval-2010: Fifth International Workshop on Semantic Evaluations (pp. 9–14). Stroudsburg, PA: Association for Computational Linguistics.
36.
MiltsakakiE. (2009). Matching readers' preferences and reading skills with appropriate web texts. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Demonstrations session (pp. 49–52). Stroudsburg, PA: Association for Computational Linguistics.
37.
NagyW.BeringerV.AbbottR. (2006). Contributions of morphology beyond phonology to literacy outcomes of upper elementary and middle school students. Journal of Educational Psychology, 98, 134–147.
National Governors Association (NGA) Center for Best Practices & Council of Chief State School Officers (CCSSD). (2010). Common Core State Standards for English language Arts & Literacy in History/Social Studies, Science, and Technical Subjects. Appendix A: Research supporting key elements of the Standards. Washington, DC: Author.
PappamihielN. E.LakeV.RiceD. (2005). Adapting a social studies lesson to include English language learners. Social Studies and the Young Learner, 17, 4–7.
42.
PatrickH.RyanA. M.AndermanL. H.MiddletonM.LinnenbrinkL.HrudaL. Z. (1997). Observing Patterns of Adaptive Learning (OPAL): A protocol for classroom observations. Unpublished manuscript. Ann Arbor, MI: University of Michigan. Retrieved from http://www.umich.edu/∼pals/finalopal.pdf
PitlerE.NenkovaA. (2008). Revisiting readability: A unified framework for predicting text quality. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (pp. 186–195). Stroudsburg, PA: Association for Computational Linguistics.
45.
PretoriusE. J. (2005). English as a second language learner differences in anaphoric resolution: Reading to learn in the academic context. Applied Psycholinguistics, 26, 521–539.
46.
ProctorC. P.DaltonB.GrishamD. (2007). Scaffolding English language learners and struggling readers in a multimedia hypertext environment with embedded strategy instruction and vocabulary support. Journal of Literacy Research, 39, 71–93.
47.
RiveraM. O.MoughamianA. C.LesauxN. K.FrancisD. J. (2008). Language and reading interventions for English language learners and English language learners with disabilities. Portsmouth, NH: Research Corporation, Center on Instruction.
SchleppegrellM. J. (2007). The linguistic challenges of mathematics teaching and learning: A research review. Reading and Writing Quarterly, 23, 139–159.
50.
SchleppegrellM. J.de OliveiraL. C. (2006). An integrated language and content approach for history teachers. Journal of English for Academic Purposes, 5, 254–268.
51.
SchwarmS. E.OstendorfM. (2005). Reading level assessment using support vector machines and statistical language models. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 523–530). Stroudsburg, PA: Association for Computational Linguistics.
