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Beginning Music Teachers' Self-Reported First-Year Preparation.

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Title: Beginning Music Teachers' Self-Reported First-Year Preparation.
Authors: Shaw, Brian P. (AUTHOR)
Source: Journal of Research in Music Education. Jan2026, Vol. 73 Issue 4, p497-516. 20p.
Subjects: Music teachers, Beginning teachers, Teacher certification, Classroom management, United States. Dept. of Education, Teaching methods, Educational technology, Special education, Preparedness
Abstract: The purpose of this study was to investigate beginning music teachers' (BMTs) self-reported preparation for their first year of teaching using more than 20 years of nationally representative data from the U.S. Department of Education. BMTs reported the strongest first-year preparation for teaching their subject matter, teaching to state content standards, and using a variety of instructional methods. BMTs in the sample reported being less prepared for classroom management, using computers and technology, teaching students with special needs, and teaching English language learners. Self-reported first-year preparation was largely similar between the music teachers (N ≈ 1,080) and the nonmusic teachers (N ≈ 34,850) in the sample. Relative to teachers in other content areas, the music teachers reported much better preparation for teaching their subject matter but lower relative preparation for classroom management, using computers and technology, teaching students with special needs, and teaching English language learners. Although first-year preparation was largely similar across degrees earned, school locale, school grade level, and gender, BMTs who pursued traditional certification reported stronger first-year preparation overall and especially during the pandemic. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Research in Music Education is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Beginning Music Teachers' Self-Reported First-Year Preparation.
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  Data: The purpose of this study was to investigate beginning music teachers' (BMTs) self-reported preparation for their first year of teaching using more than 20 years of nationally representative data from the U.S. Department of Education. BMTs reported the strongest first-year preparation for teaching their subject matter, teaching to state content standards, and using a variety of instructional methods. BMTs in the sample reported being less prepared for classroom management, using computers and technology, teaching students with special needs, and teaching English language learners. Self-reported first-year preparation was largely similar between the music teachers (N ≈ 1,080) and the nonmusic teachers (N ≈ 34,850) in the sample. Relative to teachers in other content areas, the music teachers reported much better preparation for teaching their subject matter but lower relative preparation for classroom management, using computers and technology, teaching students with special needs, and teaching English language learners. Although first-year preparation was largely similar across degrees earned, school locale, school grade level, and gender, BMTs who pursued traditional certification reported stronger first-year preparation overall and especially during the pandemic. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Research in Music Education is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/00224294251351209
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        Text: English
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      – SubjectFull: Teacher certification
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      – SubjectFull: Teaching methods
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              Text: Jan2026
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