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Of the many inequities in the education system, gaps in access to strong teaching have proven to be among the most stubborn. That is not to say that there are not excellent teachers in high-poverty schools. Research shows an indisputable and wide-spread pattern in schools and districts across the country: Low-income children and children of color do not have the same access to strong, consistent teaching as their White and higher-income peers. Although district and school leaders make many of the decisions about recruiting, hiring, assigning, and supporting teachers, state education officials also have a critical role to play in addressing disparities in teaching quality. This report presents five ways, drawn from promising equity-focused initiatives, state education leaders can incentivize and support leaders in districts--both traditional and charter--to remedy inequities in access to strong teachers. [For the executive summary to this report, "Tackling Gaps in Access to Strong Teachers: What State Leaders Can Do. Executive Summary," ED578854. For the review of this report, "NEPC Review: "Tackling Gaps in Access to Strong Teachers: What State Leaders Can Do (The Education Trust, October 2017)," see ED578792.]

Although many program managers look to data to inform decision-making and manage their programs, high-quality program data may not always be available. Yet such data are necessary for effective program implementation. The use of high-quality data facilitates program management, reduces reliance on anecdotal information, and ensures that data are available for decision-making purposes. Both research and program experience have shown that decision-support data systems are effective tools for gathering high-quality data. In an effort to expand what is known about decision-support data systems, Child Trends conducted a review of research on this topic. Child Trends also collected data on decision-support data systems as part of a study on the role of organization-level activities in the effective implementation of out-of-school time programs. This brief presents findings from that study and the research review and links these findings to effective strategies for using decision-support data systems in out-of-school time programs. (Contains 1 footnote.) [For Part 5, see ED510680.]

A field study tested the application of the CONSULT-I (R) program, which uses artificial intelligence with statistical pattern recognition in constructing a diagnosis and recommending treatment of reading difficulties. Participants in the field study came from 10 southern and central Indiana school districts, both public and parochial, and one Massachusetts school district. Participants included one high school dean in charge of the tutoring program, five teachers working with middle school learners, and 21 elementary school personnel. A total of 70 learners were taught by the 23 teachers who eventually completed the required data collection. Teachers completed learner profile sheets, carried out the recommendations of the CONSULT-I (R) program, and completed questionnaires at the end of the 5-month implementation period. Results indicated a positive reaction to most of the CONSULT-I (R) program: the focus in individualization and the strategies suggested were judged to be very helpful; and the negative responses showed a need for more written guidance in several aspects of the program. Findings suggest that data based diagnostic and treatment recommendations do work, and that through the use of the program, educators are able to provide an answer to the inconsistency of treatment predications for classroom teachers and reading specialists. Recommendations include: continue trials of the program in graduate and undergraduate classes; offer the program to area schools on a fee basis; and provide more written instructions. (Contains four tables of data.) (RS)