Effectively Using Data for Educator Preparation Program Improvement
A volume in the series: Contemporary Issues in Accreditation, Assessment, and Program Evaluation Research in Educator Preparation. Editor(s): Joyce E. Many, Georgia State University.
Wilson (2012) notes that educator preparation programs are ”awash with data.” But how do we most effectively use this data to improve our educator preparation programs? Kowalski and Lasley (2009) observe that “the often heated debate connected to scientifically based practice is not really about whether evidence should be used in decision making and by professionals, but rather how evidence (data) is collected, analyzed, and then subsequently used to guide practice” (page xi). It is these questions—what data is collected, who collects it, how it is analyzed, interpreted, how and to whom it is communicated—that this volume will address.
Of particular interest are the ways in which educator preparation program faculty work together to define goals and measure progress toward those goals. This enterprise is fraught with tensions, including the need to balance internal relevance with external accountability, which Kuh et al. (2015) suggest puts more emphasis on the process of assessment than on its use; the distinctions between research-quality measures and what Bryk (2010) calls “practical measurement” that allows faculty to make real-time modifications; and determining the priority of good evidence practices relative to providing opportunities to students or maintaining positive working relationships with partners. Chapters may document success stories that can serve as examples of good practice, or may explore challenges inherent in the work of data-based improvement.
The goal of this volume is to explore research findings related to the use of data for educator preparation program improvement. The editors invite papers that consider the mechanisms of this work, including involvement and buy-in of faculty, methods of collecting useful data, and the role of technology in managing data-based improvement efforts; as well as papers that explore challenges inherent in the work, including those indicated in the introduction.
• November 1, 2018: Proposal Submission Deadline
• December 1, 2018: Notification of Acceptance
• May 1, 2019: Full chapters Submitted
• June 1, 2019: Review Results Returned
• July 15, 2019: Final Chapter Submission
• August 15, 2019: Final Deadline
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