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Detecting learning in noisy data: the case of oral reading fluency

Authors:

Beata Beigman Klebanov,

Anastassia Loukina,

Van Rynald T. Liceralde,

Jennifer LentiniAuthors Info & Claims

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

Pages 490 - 495

https://doi.org/10.1145/3375462.3375490

Published: 23 March 2020 Publication History

Abstract

In a school context, learning is usually detected by repeated measurements of the skill of interest through a sequence of specially designed tests; in particular, this is the case with tracking improvement in oral reading fluency in elementary school children in the U.S. Results presented in this paper suggest that it is possible and feasible to detect improvement in oral reading fluency using data collected during children's independent reading of a book using the Relay Reader™ app. We are thus a step closer to the vision of having a child read for the story, not for a test, yet being able to unobtrusively assess their progress in oral reading fluency.

References

[1]

Jennifer Balogh, Jared Bernstein, Jian Cheng, Alistair Van Moere, Brent Townshend, and Masanori Suzuki. 2012. Validation of Automated Scoring of Oral Reading. <u>Educational and Psychological Measurement</u> 72, 3 (2012), 435--452.

[2]

D. Bates, M. Mächler, B. Bolker, and S. Walker. 2015. Fitting Linear Mixed-Effects Models Using lme4. <u>Journal of Statistical Software</u> 67, 1 (2015), 1--48.

[3]

Beata Beigman Klebanov, Anastassia Loukina, Nitin Madnani, John Sabatini, and Jennifer Lentini. 2019. Would you? Could you? On a tablet? Analytics of Children's eBook Reading. In <u>Proceedings of the 9th International Conference on Learning Analytics & Knowledge.</u> ACM, Tempe, AZ, USA, 106--110.

[4]

J. Bernstein, J.Cheng, J. Balog, and R. Downey. 2018. Artificial intelligence for scoring oral reading fluency. In <u>Applications of artificial intelligence to assessment</u>, H. Jiao and R. Lissitz (Eds.). Charlotte, NC: Information Age Publisher.

[5]

Lei Chen. 2009. Audio Quality Issue for Automatic Speech Assessment. In <u>Proceedings of SLaTE Workshop.</u> Wroxall, UK, 97--100.

[6]

R.H. Good, R.A. Kaminski, and S. Dill. 2002. DIBELS oral reading fluency and retell fluency. In <u>Dynamic indicators of basic early literacy skills (6th ed.)</u>, R.H. Good and R.A. Kaminski (Eds.). Eugene, OR: Institute for the Development of Educational Achievement.

[7]

J. Hasbrouck and G. Tindal. 2006. Oral Reading Fluency Norms: A Valuable Assessment Tool for Reading Teachers. <u>The Reading Teacher</u> 59 (2006), 636--644.

[8]

Jan Hasbrouck and Gerald Tindal. 2017. <u>An update to compiled ORF norms.</u> Technical Report. Behavioral Research and Teaching, University of Oregon.

[9]

Julia Hirschberg. 2002. Communication and prosody: Functional aspects of prosody. <u>Speech Communication</u> 36, 1-2 (2002), 31--43.

[10]

Roland H. Good III, Deborah C. Simmons, and Edward J. Kame'enui. 2001. The Importance and Decision-Making Utility of a Continuum of Fluency-Based Indicators of Foundational Reading Skills for Third-Grade High-Stakes Outcomes. <u>Scientific Studies of Reading</u> 5, 3 (2001), 257--288.

[11]

Alexandra Kuznetsova, Per B. Brockhoff, and Rune H. B. Christensen. 2017. lmerTest Package: Tests in Linear Mixed Effects Models. <u>Journal of Statistical Software</u> 82, 13 (2017), 1--26.

[12]

A. Loukina, B. Beigman Klebanov, P. Lange, B. Gyawali, and Y. Qian. 2017. Developing speech processing technologies for shared book reading with a computer. In <u>6th International Workshop on Child Computer Interaction.</u> 46--51.

[13]

A. Loukina, B. Beigman Klebanov, P. Lange, Y. Qian, B. Gyawali, N. Madnani, A. Misra, K. Zechner, Z. Wang, and J. Sabatini. 2019. Automated Estimation of Oral Reading Fluency During Summer Camp e-Book Reading with MyTurn-ToRead. In <u>Proceedings of the Annual Conference of the International Speech Communication Association.</u> Gratz, Austria, 21--25.

[14]

A. Loukina, V. Liceralde, and B. Beigman Klebanov. 2018. Towards Understanding Text Factors in Oral Reading. In <u>Proceedings of Annual 2018 Conference of the North American Chapter of the Association for Computational Linguistics.</u> New Orleans, Louisiana, 2143--2154.

[15]

Yi Luan, Masayuki Suzuki, Yutaka Yamauchi, Nobuaki Minematsu, Shuhei Kato, and Keikichi Hirose. 2012. Performance improvement of automatic pronunciation assessment in a noisy classroom. In <u>Proceedings of 2012 IEEE Workshop on Spoken Language Technology (SLT).</u> Miami, FL, 428--431.

[16]

Nitin Madnani, Beata Beigman Klebanov, Anastassia Loukina, Binod Gyawali, Patrick Lange, John Sabatini, and Michael Flor. 2019. My Turn To Read: An Interleaved E-book Reading Tool for Developing and Struggling Readers. In <u>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations.</u> Florence, Italy, 141--146.

[17]

Jack Mostow. 2012. Why and how our automated reading tutor listens. In <u>Proceedings of the International Symposium on Automatic Detection of Errors in Pronunciation Training.</u> Stockholm, Sweden, 43--52.

[18]

Diane Napolitano, Kathleen Sheehan, and Robert Mundkowsky. 2015. Online Readability and Text Complexity Analysis with TextEvaluator. In <u>Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations.</u> Denver, Colorado, 96--100.

[19]

Joseph F. T. Nese, Gina Biancarosa, Daniel Anderson, Cheng-Fei Lai, Julie Alonzo, and Gerald Tindal. 2012. Within-year oral reading fluency with CBM: a comparison of models. <u>Reading and Writing</u> 25, 4 (2012), 887--915.

[20]

Eunice Kennedy Shriver National Institute of Child Health and DHH Human Development, NIH. 2000. Report of the National Reading Panel: Teaching Children to Read: Reports of the Subgroups (00-4754). https://www.nichd.nih.gov/sites/default/files/publications/pubs/nrp/Documents/report.pdf

[21]

University of Oregon. 2019. 8th Edition of Dynamic Indicators of Basic Early Literacy Skills (DIBELS): Administration and Scoring Guide. https://dibels.uoregon.edu/docs/materials/d8/dibels_8_admin_and_scoring_guide_07_2019.pdf

[22]

Lynn Olsen. 2005. Benchmark Assessments Offer Regular Checkups on Student Achievement. <u>Education Week</u> 25, 13 (2005), 13--14.

[23]

Marianne Perie, Scott Marion, and Brian Gong. 2009. Moving Toward a Comprehensive Assessment System: A Framework for Considering Interim Assessments. <u>Educational Measurement: Issues and Practice</u> 28, 3 (2009), 5--13.

[24]

Brian Poncy, Christopher Skinner, and Philip Axtell. 2005. An investigation of the reliability and standard error of measurement of words read correctly per minute using curriculum-based measurement. <u>Journal of Psychoeducational Assessment</u> 23, 4 (2005), 326--338.

[25]

R Core Team. 2019. <u>R: A Language and Environment for Statistical Computing.</u> R Foundation for Statistical Computing. https://www.R-project.org/

[26]

Stephen Raudenbush and Anthony Bryk. 2002. <u>Hierarchical Linear Models: Applications and Data Analysis Methods</u> (second ed.). Sage, Newbury Park, CA.

[27]

Tom Snijders and Roel Bosker. 2012. <u>Multilevel Analysis</u> (2 ed.). Sage, London.

[28]

Miya Miura Wayman, Teri Wallace, Hilda Ives Wiley, Renata Ticha, and Christine A. Espin. 2007. Literature synthesis on curriculum-based measurement in reading. <u>Journal of Special Education</u> 41, 2 (2007), 85--120.

[29]

Laurence White. 2014. Communicative function and prosodic form in speech timing. <u>Speech Communication</u> 63-64 (2014), 38--54.

Cited By

Sabatini JGraesser AHollander JO'Reilly T(2023) A framework of literacy development and how AI can transform theory and practice British Journal of Educational Technology10.1111/bjet.1334254:5(1174-1203)Online publication date: 8-Jun-2023
https://doi.org/10.1111/bjet.13342

Index Terms

Detecting learning in noisy data: the case of oral reading fluency

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Information & Contributors

Information

Published In

cover image ACM Other conferences

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

March 2020

679 pages

ISBN:9781450377126

DOI:10.1145/3375462

General Chairs:
Christoph Rensing
Technische Universität Darmstadt, Germany
,
Hendrik Drachsler
DIPF Leibniz Institute for Research and Information in Education & University of Frankfurt, Germany
,
Program Chairs:
Vitomir Kovanović
University of South Australia, Australia
,
Niels Pinkwart
Humboldt-Universität zu Berlin, Germany
,
Maren Scheffel
Open Universiteit, Netherlands
,
Katrien Verbert
KU Leuven, Belgium

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 March 2020

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LAK '20

LAK '20: 10th International Conference on Learning Analytics and Knowledge

March 23 - 27, 2020

Frankfurt, Germany

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LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Sabatini JGraesser AHollander JO'Reilly T(2023) A framework of literacy development and how AI can transform theory and practice British Journal of Educational Technology10.1111/bjet.1334254:5(1174-1203)Online publication date: 8-Jun-2023
https://doi.org/10.1111/bjet.13342

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