scholarly journals How Benchmark Is Used in Third Grade Reading Instruction

2021 ◽  
Vol 2 (1) ◽  
pp. p85
Author(s):  
Dana Bartlett ◽  
Michael Vinella ◽  
Sunddip Panesar-Aguilar

Third grade reading teachers at the local setting are not consistently using formative benchmark data to improve student reading performance, creating a gap in practice. This gap in practice may be due to teachers’ lack of capacity to use the data to make changes to their instructional practices. The purpose of this qualitative study was to explore how third grade reading teachers are using data from reading benchmark assessments to improve student reading performance. This research study was guided by two Research Questions (RQs). RQ 1 addressed how third grade teachers are using reading benchmark assessment data to improve student reading performance. RQ 2 addressed specific instructional strategies that third grade teachers are using from reading benchmark assessment data to effectively improve student reading performance. Data-driven decision making (DDDM) was the conceptual framework that was the foundation for this study. This basic qualitative design for this research study included 13 participants. Data were collected through open-ended semistructured interviews, and qualitative analyses were conducted through open coding and thematic analysis. According to the findings of this study, immediately analyzing data, collaboration, and data driven instruction were the themes that emerged guided by RQ 1. Emerging themes for RQ 2 included test taking strategies, modeling, and guided reading. Leadership in this district may use these findings to make decisions about the effectiveness of teachers’ use of these benchmark assessments or the data gathered from the assessments to impact student reading proficiencies. This research may provide specific instructional strategies used through the DDDM process that increases student reading proficiency. The findings could possibly yield results that have positive social change implications for reading achievement.

1984 ◽  
Vol 7 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Terry L. Rose ◽  
Lee Sherry

An alternating-treatments design was used to investigate the relative effects of two oral reading previewing procedures: (a) silent: the student reads silently the assigned reading passage prior to reading it aloud, and (b) listening: the teacher reads the assigned selection aloud with the student following along silently prior to the student reading the passage aloud. Five junior-high school learning disabled students, four boys and one girl, participated in the study. In four of five cases results showed that systematic prepractice procedures were related to higher performance levels than was baseline (no prepractice). Differential effects were noted: the listening procedure was related to higher rates of words read correctly than was the silent procedure. The findings are discussed in terms of their implications for research and instructional procedures, especially as these relate to adolescent learners.


Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2910
Author(s):  
Andreas Andreou ◽  
Constandinos X. Mavromoustakis ◽  
George Mastorakis ◽  
Jordi Mongay Batalla ◽  
Evangelos Pallis

Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.


2010 ◽  
Vol 85 (2) ◽  
pp. 226-245 ◽  
Author(s):  
Leslie Nabors Oláh ◽  
Nancy R. Lawrence ◽  
Matthew Riggan

1982 ◽  
Vol 7 (4) ◽  
pp. 325-326
Author(s):  
Maurine A. Fry ◽  
Marilyn J. Haring ◽  
Joyce H. Crawford

2005 ◽  
Vol 26 (4-5) ◽  
pp. 401-432 ◽  
Author(s):  
Suzanne H. Carreker ◽  
Paul R. Swank ◽  
Lynn Tillman-Dowdy ◽  
Graham F. Neuhaus ◽  
Mary Jo Monfils ◽  
...  

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