The focus of school improvement strategies has shifted from reforming whole schools to improving the quality of instruction in individual classrooms.
- School-based data collections
- Educational instrument design
- Large-scale evaluations
Sheila Heaviside is an experienced director and technical advisor with expertise in designing surveys and collecting high quality data in evaluations for large-scale education surveys.
She currently directs the Impact Evaluation of Departmentalized Instruction in Elementary Schools and the Impact Study of Feedback for Teachers Based on Classroom Videos, guiding instrument development and the collection of surveys, rosters, informed consent, and classroom video recordings. Heaviside served as the survey director for other notable educational studies, including the survey and data collection components of the evaluation of the Teacher Incentive Fund (TIF); a project for the U.S. Department of Education to examine the effectiveness of TIF grants, program models, and features; and the Data Driven Instruction study that gathered data from teachers, principals, and districts. In addition, Heaviside directed the student and classroom observation collection for the Mathematica Curricula and Educational Technology evaluation.
Before joining Mathematica, Heaviside held survey and research positions at Westat, Moss Survey Research Center, and the U.S. Census Bureau. She is the author of many papers and presentations and holds a master’s degree in sociology from Catholic University of America.
Impact Evaluation of Departmentalized Instruction in Elementary Schools
Study of Feedback for Teachers Based on Classroom Videos
This evaluation is examining whether video-based observations and feedback help novice and early career teachers enhance classroom practices and student achievement.
Study of Teaching Residency Programs
This study looked at characteristics of federally funded Teaching Residency Programs, including applicants and participants, by measuring program length, required coursework, characteristics of mentor teachers, selection criteria for participants, and retention rates.
Evaluation of Support for Using Student Data to Inform Teachers' Instruction
Mathematica is conducting an experimental impact evaluation of the effects of data-driven instruction (DDI) on student achievement. This involves the implementation of high quality DDI professional development and estimating its effects on student achievement.
Support for Data-Driven Instruction Comes Up Short in New Study
Although most school districts help teachers use data to improve student learning, a new Mathematica study shows that providing schools with data coaches and professional development to support their efforts did not result in increased data use by teachers.