Blended Professional Development: Toward a Data-Informed Model of Instruction

abstract: Data and the use of data to make educational decisions have attained new-found prominence in K-12 education following the inception of high-stakes testing and subsequent linking of teacher evaluations and teacher-performance pay to students' outcomes on standardized assessments. Altho...

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Other Authors: Nelson, Andrew Nelson (Author)
Format: Doctoral Thesis
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/2286/R.I.44028
id ndltd-asu.edu-item-44028
record_format oai_dc
spelling ndltd-asu.edu-item-440282018-06-22T03:08:16Z Blended Professional Development: Toward a Data-Informed Model of Instruction abstract: Data and the use of data to make educational decisions have attained new-found prominence in K-12 education following the inception of high-stakes testing and subsequent linking of teacher evaluations and teacher-performance pay to students' outcomes on standardized assessments. Although the research literature suggested students' academic performance benefits were derived from employing data-informed decision making (DIDM), many educators have not felt efficacious about implementing and using DIDM practices. Additionally, the literature suggested a five-factor model of teachers' efficacy and anxiety with respect to using DIDM practices: (a) identification of relevant information, (b) interpretation of relevant information, (c) application of interpretations of data to their classroom practices, (d) requisite technological skills, and (e) comfort with data and statistics. This action research study was designed to augment a program of support focused on DIDM, which was being offered at a K-8 charter school in Arizona. It sought to better understand the relation between participation in professional development (PD) modules and teachers' self-efficacy for using DIDM practices. It provided an online PD component, in which 19 kindergarten through 8th-grade teachers worked through three self-guided online learning modules, focused sequentially on (a) identification of relevant student data, (b) interpretation of relevant student data, and (c) application of interpretations of data to classroom practices. Each module concluded with an in-person reflection session, in which teachers shared artifacts they developed based on the modules, discussed challenges, shared solutions, and considered applications to their classrooms. Results of quantitative data from pre- and post-intervention assessments, suggested the intervention positively influenced participants' self-efficacy for (a) identifying and (b) interpreting relevant student data. Qualitative results from eight semi-structured interviews conducted at the conclusion of the intervention indicated that teachers, regardless of previous experience using data, viewed DIDM favorably and were more able to find and draw conclusions from their data than they were prior to the intervention. The quantitative and qualitative data exhibited complementarity pointing to the same conclusions. The discussion focused on explaining how the intervention influenced participants' self-efficacy for using DIDM practices, anxiety around using DIDM practices, and use of DIDM practices. Dissertation/Thesis Nelson, Andrew Nelson (Author) Buss, Ray R (Advisor) Preach, Deborah (Committee member) Buchanan, James (Committee member) Mertler, Craig A (Committee member) Arizona State University (Publisher) Education Educational leadership Teacher education Blended Professional Development Data-Driven Decision Making Data-Informed Decision Making Professional Development eng 121 pages Doctoral Dissertation Leadership and Innovation 2017 Doctoral Dissertation http://hdl.handle.net/2286/R.I.44028 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2017
collection NDLTD
language English
format Doctoral Thesis
sources NDLTD
topic Education
Educational leadership
Teacher education
Blended Professional Development
Data-Driven Decision Making
Data-Informed Decision Making
Professional Development
spellingShingle Education
Educational leadership
Teacher education
Blended Professional Development
Data-Driven Decision Making
Data-Informed Decision Making
Professional Development
Blended Professional Development: Toward a Data-Informed Model of Instruction
description abstract: Data and the use of data to make educational decisions have attained new-found prominence in K-12 education following the inception of high-stakes testing and subsequent linking of teacher evaluations and teacher-performance pay to students' outcomes on standardized assessments. Although the research literature suggested students' academic performance benefits were derived from employing data-informed decision making (DIDM), many educators have not felt efficacious about implementing and using DIDM practices. Additionally, the literature suggested a five-factor model of teachers' efficacy and anxiety with respect to using DIDM practices: (a) identification of relevant information, (b) interpretation of relevant information, (c) application of interpretations of data to their classroom practices, (d) requisite technological skills, and (e) comfort with data and statistics. This action research study was designed to augment a program of support focused on DIDM, which was being offered at a K-8 charter school in Arizona. It sought to better understand the relation between participation in professional development (PD) modules and teachers' self-efficacy for using DIDM practices. It provided an online PD component, in which 19 kindergarten through 8th-grade teachers worked through three self-guided online learning modules, focused sequentially on (a) identification of relevant student data, (b) interpretation of relevant student data, and (c) application of interpretations of data to classroom practices. Each module concluded with an in-person reflection session, in which teachers shared artifacts they developed based on the modules, discussed challenges, shared solutions, and considered applications to their classrooms. Results of quantitative data from pre- and post-intervention assessments, suggested the intervention positively influenced participants' self-efficacy for (a) identifying and (b) interpreting relevant student data. Qualitative results from eight semi-structured interviews conducted at the conclusion of the intervention indicated that teachers, regardless of previous experience using data, viewed DIDM favorably and were more able to find and draw conclusions from their data than they were prior to the intervention. The quantitative and qualitative data exhibited complementarity pointing to the same conclusions. The discussion focused on explaining how the intervention influenced participants' self-efficacy for using DIDM practices, anxiety around using DIDM practices, and use of DIDM practices. === Dissertation/Thesis === Doctoral Dissertation Leadership and Innovation 2017
author2 Nelson, Andrew Nelson (Author)
author_facet Nelson, Andrew Nelson (Author)
title Blended Professional Development: Toward a Data-Informed Model of Instruction
title_short Blended Professional Development: Toward a Data-Informed Model of Instruction
title_full Blended Professional Development: Toward a Data-Informed Model of Instruction
title_fullStr Blended Professional Development: Toward a Data-Informed Model of Instruction
title_full_unstemmed Blended Professional Development: Toward a Data-Informed Model of Instruction
title_sort blended professional development: toward a data-informed model of instruction
publishDate 2017
url http://hdl.handle.net/2286/R.I.44028
_version_ 1718701405968007168