Induction experiences of novice teachers and their coaches

ABSTRACT

This mixed-methods study explores the experiences and influence of induction on novice teachers. The authors quantitatively analyze survey data from over two thousand novice teachers and a thousand of their coaches through statistical comparisons and multiple linear regression analyses to explore whether structures of induction are associated with how teachers learn and develop in their pedagogy. Qualitative analyses of respondents’ open-ended responses guided by word cluster formations indicate a positive feeling about this induction program but revealed differing areas of focus between novice teachers and their coaches. Results indicate the importance of coaches, curriculum, and the learning management system in creating positive induction experiences. Findings from this study have implications for the influence and structural design of induction programs for novice teacher development.

KEYWORDS:

• Teacher induction
• teacher coaching
• teacher development
• data mining
• mixed methods

Acknowledgments

We first want to thank Barbara Howard, Tonya Almeida, and the CTI program staff for their willingness to share about their invaluable work to the community at large. We want to thank Linda Sanada for her technical expertise and guidance throughout this research. Finally, we would like to thank all Candidates and Coaches for their tireless work in the classrooms.

This material is based on work supported by Riverside County Superintendent of Schools under C-1006110 and The Regents of the University of California. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the Riverside County Superintendent of Schools or The Regents of the University of California.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. CTI coaches are termed ‘Coach’ from here forward.

2. CTI novice teachers are termed ‘Candidate’ from here forward.

3. KH Coder is a free software for quantitative content analysis or text mining available at http://khc.sourceforge.net/en/.

4. Reference to words in the figures are italicized instead of quoted for ease of reading and to separate from full respondent quotes.

5. Unique identifiers were given to each participant.

Additional information

Notes on contributors

Douglas Mitchell

Douglas Mitchell is a professor at the University of California, Riverside. His research and published writings have been on state legislative decision-making, labor relations, teacher incentive systems, public support for public schools, desegregation, class size and school board elections.

Jeffrey Keese

Jeffrey Keese is an assistant professor of Teacher Leadership and director of the Teacher Leadership Program at Mercer University in Atlanta. His research centers on teacher development and avenues of continued teacher support.

Manjari Banerjee

Manjari Banerjee is a PhD candidate in the Department of Teaching, Learning, and Culture at Texas A&M University. Her research focuses on teacher professional development and technology integration.

Debbee Huston

Debbee Huston is a statistician at the University of California, Riverside. Her work focuses on large-scale data analyses on teacher induction and coaching.

Andrew Kwok

Andrew Kwok is an assistant professor in the Department of Teaching, Learning, and Culture at Texas A&M University. He focuses on the preparation, development, and support of classroom management for beginning teachers, particularly those who work in high needs areas.