Project Activities
In this project, the researchers planned to develop an assessment system aimed at grades 5 through 8 that featured both formative and summative assessment of data modeling skills and practices. The formative components of the system was to focus on supporting instruction with items designed for teachers to use as instructional tools and diagnostic aides. The summative components of the system were to profile students' skills and knowledge as it developed over the targeted grades. The researchers aimed to design a system that would evaluate growth over time based on research that showed how students' knowledge and proficiency with these skills characteristically develops.
Structured Abstract
Setting
The schools will be located in three urban sites in Tennessee, Massachusetts, and California.
Sample
All of the schools include diverse and underserved fifth to eighth grade student populations.
Intervention
The researchers will work with teachers to design formative and summative assessments that diagnose students' skills and knowledge in data modeling. The formative assessments feature contexts for instruction as well as observation. Each assessment includes teacher notes that suggest ways to leverage the assessment as an opportunity for instruction. Teacher notes are based on current research-based understandings of student reasoning and on new research that the researchers will conduct. The researchers will employ the Berkeley Evaluation and Assessment Research model to develop construct maps (progress variables) for each of four strands of data modeling: (a) measurement, (b) representation, (c) data structures, and (d) statistical inference. Progress variables are hypothetical developmental trajectories of learning that reflect an emerging research base about how students in this age band typically reason about these concepts. Construct maps guide the development of formative and summative items, which will be tested in fifth through eigth grade classrooms.
Research design and methods
The initial phase of the work will focus on developing progress variables indicating four components of data modeling: measurement, data structures, data display, and statistical inference. The second phase will encompass larger samples of students and teachers to obtain evidence for reliability and validity and to test the viability of the assessment system at a greater scale. Specifically, in the first phase the researchers will conduct small-scale work with teachers to iteratively refine items based on student responses (written, interviews, and think-alouds) and teacher responses to the accompanying scoring guides and teacher notes. During the phase 2, the researchers will transition toward larger sample sizes (500 students at each grade) appropriate to determining psychometric characteristics of item functioning. Finally, a second large-scale data collection will be conducted with the revised items during the following year.
Key measures
Outcome variables will be scaled with a multidimensional item response model.
People and institutions involved
IES program contact(s)
Products and publications
ERIC Citations: Find available citations in ERIC for this award here.
Select Publications:
Book chapters
Lehrer, R., Kim, M.J., Ayers, E., and Wilson, M. (2013). Toward Establishing a Learning Progression to Support the Development of Statistical Reasoning. In J. Confrey, and A. Maloney (Eds.), Learning Over Time: Learning Trajectories in Mathematics Education. Charlotte, NC: Information Age Publishers.
Journal articles
Lehrer, R., Kim, M.J., and Jones, S. (2011). Developing Conceptions of Statistics by Designing Measures of Distribution. ZDM-The International Journal on Mathematics Education, 43(5): 723-736.
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Questions about this project?
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