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Employing Progress Monitoring Measures to Create Homogenous Small Groups and Predict Reading Performance among English Language Learners and Non-English Language Learners.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Employing Progress Monitoring Measures to Create Homogenous Small Groups and Predict Reading Performance among English Language Learners and Non-English Language Learners./
Author:	Adams, John L.
Description:	1 online resource (104 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.
Subject:	Educational psychology. -
Online resource:	click for full text (PQDT)
ISBN:	9780355550467

Employing Progress Monitoring Measures to Create Homogenous Small Groups and Predict Reading Performance among English Language Learners and Non-English Language Learners.
Adams, John L.

Employing Progress Monitoring Measures to Create Homogenous Small Groups and Predict Reading Performance among English Language Learners and Non-English Language Learners. - 1 online resource (104 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.

Thesis (Ph.D.)--Northern Arizona University, 2017.

Includes bibliographical references

Progress monitoring and language proficiency data from a large urban school district in the southwest were used to explore differences between ELLs and non-ELLs (n = 2,865) on reading growth, predict reading outcomes, and create homogenous groups with respect to reading ability. The AzMERIT English Language Arts score for Spring 2016 was used as a criterion variable for reading proficiency. DIBELS Next progress monitoring measures and School City Benchmark scores were explored as potential predictors of reading proficiency. School City Benchmark test scores did not significantly correlate with AzMERIT scores, and were excluded from the analyses. A composite of the Listening and Speaking Scores for the AZELLA was created to represent language proficiency for ELLs.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355550467Subjects--Topical Terms:

555103
Educational psychology.
Index Terms--Genre/Form:

554714
Electronic books.

Employing Progress Monitoring Measures to Create Homogenous Small Groups and Predict Reading Performance among English Language Learners and Non-English Language Learners.
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245 1 0 $a Employing Progress Monitoring Measures to Create Homogenous Small Groups and Predict Reading Performance among English Language Learners and Non-English Language Learners.
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300 $a 1 online resource (104 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.
500 $a Advisers: Robert A. Horn; Kathy J. Bohan.
502 $a Thesis (Ph.D.)--Northern Arizona University, 2017.
504 $a Includes bibliographical references
520 $a Progress monitoring and language proficiency data from a large urban school district in the southwest were used to explore differences between ELLs and non-ELLs (n = 2,865) on reading growth, predict reading outcomes, and create homogenous groups with respect to reading ability. The AzMERIT English Language Arts score for Spring 2016 was used as a criterion variable for reading proficiency. DIBELS Next progress monitoring measures and School City Benchmark scores were explored as potential predictors of reading proficiency. School City Benchmark test scores did not significantly correlate with AzMERIT scores, and were excluded from the analyses. A composite of the Listening and Speaking Scores for the AZELLA was created to represent language proficiency for ELLs.
520 $a L1 students were compared to Spanish-Speaking English Language Learners using data from the 2014--2015 and 2015--16 school years. Groups were compared on DIBELS Next Oral Reading Fluency Words Read Correctly (DORF WRC), DIBELS Next Retell, DIBELS Next Retell Quality, DIBELS Next Retell Accuracy, and DORF WRC growth across the two year period. Groups were compared using data from the Spring 2016 testing phase, and using an exploratory Sum Scores derived from the raw scores across all testing phases for the two school years.
520 $a L1 students performed significantly higher than Spanish Speaking ELLs on all measures, except DORF WRC growth. The results were consistent for both the Spring 2016 iteration and the DIBELS Next Sum variable analyses. For L1 Students, DORF WRC was the strongest predictor of the AzMERIT ELA score. Retell, Retell Quality, and DORF Accuracy contributed to the prediction of the AzMERIT above and beyond DORF WRC. For Spanish Speaking non-ELLs, only Retell Accuracy predicted AzMERIT above and beyond DORF WRC for the Spring 2016 iteration. Retell Accuracy and Retell Quality predicted AzMERIT score above and beyond DORF WRC in the DIBELS Next Sum Score iteration. For Spanish Speaking ELLs, Retell Quality and the Listening/Speaking Composite predicted AzMERIT score above and beyond DORF WRC for both iterations.
520 $a Two-Step Cluster analyses were performed using a categorical Listening/Speaking variable, DORF WRC, and Retell Quality. The Spring 2016 iteration yielded four clusters, and the DIBELS Next Sum Score Iteration yielded five clusters. Follow up analyses showed significant differences among clusters on DORF WRC growth for both iterations. Chi Square tests revealed significantly different performance levels on the AzMERIT among clusters.
520 $a These results of the regression analyses indicate that the DORF WRC is highly predictive of the AzMERIT for both ELLs and non-ELLs, though bias in prediction was observed. These results also show that the Retell Quality score and Listening/Speaking Composite add to the prediction for Spanish Speaking ELLs. The results of the cluster analysis indicate that this procedure was successful in producing groups that differed from one another on both DORF WRC growth and overall performance on the AzMERIT. Implications for practice and future research are discussed.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Educational psychology. $3 555103
650 4 $a Reading instruction. $3 1148440
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0535
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Northern Arizona University. $b Educational Psychology. $3 1187572
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