東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Employing Progress Monitoring Measur...

Adams, John L.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Employing Progress Monitoring Measures to Create Homogenous Small Groups and Predict Reading Performance among English Language Learners and Non-English Language Learners./
作者:	Adams, John L.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	104 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: A.
Contained By:	Dissertation Abstracts International79-04A(E).
標題:	Educational psychology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10687652
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. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 104 p.

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

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

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.

ISBN: 9780355550467Subjects--Topical Terms:

517650
Educational psychology.

Employing Progress Monitoring Measures to Create Homogenous Small Groups and Predict Reading Performance among English Language Learners and Non-English Language Learners.
LDR:04244nmm a2200349 4500 001 2159828
005 20180703102254.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355550467
035 $a (MiAaPQ)AAI10687652
035 $a (MiAaPQ)nau:11389
035 $a AAI10687652
040 $a MiAaPQ $c MiAaPQ
100 1 $a Adams, John L. $3 3347723
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.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 104 p.
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.
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.
590 $a School code: 0391.
650 4 $a Educational psychology. $3 517650
650 4 $a Reading instruction. $3 2122756
690 $a 0525
690 $a 0535
710 2 $a Northern Arizona University. $b Educational Psychology. $3 3189020
773 0 $t Dissertation Abstracts International $g 79-04A(E).
790 $a 0391
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10687652