Learning About Learning: What Every New Teacher Needs to Know
This paper examines teacher education textbooks for discussion of research-based strategies that every teacher candidate should learn in order to promote student learning and retention.
Learning About Learning: What Every New Teacher Needs to Know Retrieved from http://www.nctq.org/dmsView/Learning_About_Learning_Report.
Assessment and classroom learning
This paper is a review of the literature on classroom formative assessment.
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in education, 5(1), 7-74.
Formative Assessment: A Meta?Analysis And A Call For Research
This meta-analysis examines the impact of formative assessment.
Kingston, N., & Nash, B. (2011). Formative assessment: A meta?analysis and a call for research. Educational Measurement: Issues and Practice, 30(4), 28-37.
Measurably superior instruction means close, continual contact with the relevant outcome data: Revolutionary!
The chapter looks at the critical importance of how to effectively measure performance to achieve the greatest impact.
Bushell, D., & Baer, D. M. (1994). Measurably superior instruction means close, continual contact with the relevant outcome data: Revolutionary. Behavior analysis in education: Focus on measurably superior instruction, 3-10.
Synthesis of research on reviews and tests.
This study looks at the use of properly spaced reviews and tests as a practice that can dramatically improve classroom learning and retention.
Dempster, F. N. (1991). Synthesis of Research on Reviews and Tests. Educational leadership, 48(7), 71-76.
Using Data-Based Inquiry and Decision Making To Improve Instruction.
This study examines six schools using data-based inquiry and decision-making process to improve instruction.
Feldman, J., & Tung, R. (2001). Using Data-Based Inquiry and Decision Making To Improve Instruction. ERS Spectrum, 19(3), 10-19.
Dealing with Flexibility in Assessments for Students with Significant Cognitive Disabilities
Alternate assessment and instruction is a key issue for individuals with disabilities. This report presents an analysis, by assessment system component, to identify where and when flexibility can be built into assessments.
Gong, B., & Marion, S. (2006). Dealing with Flexibility in Assessments for Students with Significant Cognitive Disabilities. Synthesis Report 60. National Center on Educational Outcomes, University of Minnesota.
Using Student Achievement Data to Support Instructional Decision Making
The purpose of this practice guide is to help teachers and administrators use student achievement data to make instructional decisions.
Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J. A., & Wayman, J. C. (2009). Using Student Achievement Data to Support Instructional Decision Making. IES Practice Guide. NCEE 2009-4067. National Center for Education Evaluation and Regional Assistance.
A Longitudinal Examination of the Diagnostic Accuracy and Predictive Validity of R-CBM and High-Stakes Testing
The purpose of this study is to compare different statistical and methodological approaches to standard setting and determining cut scores using R- CBM and performance on high-stakes tests
Hintze, J. M., & Silberglitt, B. (2005). A longitudinal examination of the diagnostic accuracy and predictive validity of R-CBM and high-stakes testing. School Psychology Review, 34(3), 372.
Effective Behavior Support: A Systems Approach to Proactive School-wide Management
This study describes Effective Behavioral Support, a systems approach to enhancing the capacity of schools to adopt and sustain use of effective processes for all students.
Lewis, T. J., & Sugai, G. (1999). Effective Behavior Support: A Systems Approach to Proactive Schoolwide Management. Focus on Exceptional Children, 31(6), 1-24.
A Theoretical Framework for Data-Driven Decision Making
The purpose of this paper is to provide a model for more effective data-driven decision making in classrooms, schools, and districts.
Mandinach, E. B., Honey, M., & Light, D. (2006, April). A theoretical framework for data-driven decision making. In annual meeting of the American Educational Research Association, San Francisco, CA.
Making sense of data-driven decision making in education.
This paper uses research to show how schools and districts are analyzing achievement test results and other types of data to make decisions to improve student success.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education.
Measuring reading comprehension and mathematics instruction in urban middle schools: A pilot study of the Instructional Quality Assessment
The purpose of this research is to investigate the reliability and potential validity of the ratings of Instructional Quality Assessment..
Matsumura, L. C., Slater, S. C., Junker, B., Peterson, M., Boston, M., Steele, M., & Resnick, L. (2006). Measuring Reading Comprehension and Mathematics Instruction in Urban Middle Schools: A Pilot Study of the Instructional Quality Assessment. CSE Technical Report 681. National Center for Research on Evaluation, Standards, and Student Testing (CRESST).
Data-based Decision Making in Education.
This book scrutinizes research from seven countries to answer the following questions: Why is data use important in schools? How does policy influence data use? Which factors enable effective data use? What are the effects of data use?
Schildkamp, K., Lai, M. K., & Earl, L. (2013). Data-based Decision Making in Education.
Involving teachers in data-driven decision making: Using computer data systems to support teacher inquiry and reflection.
This paper outlines effective practices such as accountability reporting and user-friendly data access in the use of student data.
Wayman, J. C. (2005). Involving teachers in data-driven decision making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Students Placed at Risk, 10(3), 295-308.