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IES Grant

Title: Adapting Lesson Study for Developmental Mathematics Instruction
Center: NCER Year: 2017
Principal Investigator: Edgecombe,Nikki Awardee: Columbia University, Teachers College
Program: Postsecondary and Adult Education      [Program Details]
Award Period: 4 years (07/01/2017–06/20/2021) Award Amount: $1,400,000
Goal: Development and Innovation Award Number: R305A170454
Description:

Co-Principal Investigator: Michelle Hodara (Education Northwest)

Purpose: As more community colleges improve their curricula for development education, postsecondary instructors increasingly need professional development. This project aims to refine Lesson Study—a collaborative-learning model for teacher professional development that has evidence of improving math instruction in K–12 settings—for use among developmental mathematics faculty at community colleges. Such faculty are often part-time or adjuncts and may lack access to pre-service training or campus resources and support, making flexible, responsive professional development models, such as Lesson Study, desirable.

Project Activities: Working with community college administrators and developmental math instructors, the research team will iteratively develop, refine, and test materials for implementing Lesson Study in postsecondary settings. The research team will use qualitative data to determine the usability and feasibility of the materials and will conduct a mixed-method pilot study to determine whether the approach affects teacher and student outcomes.

Products: Researchers will produce a fully developed version of Lesson Study for postsecondary institutions and will disseminate peer-reviewed publications.

Structured Abstract

Setting: The research will be conducted in community colleges in Oregon.

Sample: Approximately 27 college-level math faculty will participate in the development of the intervention, with approximately 225 developmental math students participating in the pilot study.

Intervention: Lesson Study (LS) is a collaborative teacher-learning model that started in Japan and that is becoming more common in the United States for teacher professional development in K–12 settings. In the LS, a team of instructors designs a "research lesson" to address a problem of practice (e.g., how to introduce a particular concept). One of the LS teachers delivers the lesson in a classroom while other team members observe and collect data on student responses and learning. The team analyzes the data, reflects on the lesson and its implementation, and makes improvements to the lesson. One of the LS teachers then delivers the revised lesson to a different class while other teachers observe again. The cycle may be repeated multiple times for continued refinement of the lesson.

Research Design and Methods: In this study, the researchers will introduce LS to postsecondary instructors teaching new quantitative literacy courses, which are courses designed to shorten the developmental math sequence for non-STEM students and prepare them for college-level math. With the instructor and college administrator input, the researchers will iteratively adapt existing LS training materials for use in the community college context. In particular, the researchers will (i) develop materials to facilitate and carry out LS (e.g., a facilitator's guide for sessions, guidelines for selecting a topic for the research lesson, a template for planning the research lesson, and a rubric for evaluating the research lesson), (ii) determine the best time for enacting each LS sessions, (iii) determine the optimal team size and strategies for engaging adjunct faculty, and (iv) create pathways to LS leadership for faculty.

During the development phase (Year 1 and 2), the researchers will conduct focused interviews, observations, and document reviews to improve the training materials and create rubrics for LS implementation as well as determine the usability and feasibly of LS in postsecondary settings. Following this phase, the researchers will conduct a pilot study (Year 3 and 4) using instructors who had not participated in the development phase. The pilot study will use an interrupted time series design, which detects the impact of an intervention (LS) by comparing student outcomes after the intervention is implemented to the outcomes that might have been expected based on pre-intervention outcome trends (i.e., student outcomes from fall 2017 to spring 2020). To address the possibility of historic events affecting the outcome (e.g., changes at the college during the pilot), the pilot study will also collect data from a comparison group of students in developmental English courses at the same colleges during the pilot study.

Control Condition: Because of the interrupted time series design, there is no control group. However, the researchers will check the validity of the results by comparing the pilot students' outcomes to those of a concurrent group of students in developmental English courses and to the outcomes of  2,000 students enrolled in developmental math courses prior to the pilot study.

Key Measures: Teacher outcomes include measures of instructors' knowledge, beliefs, and dispositions as well as markers of professional learning community use of learning resources and tools. Student outcomes include performance on a quantitative reasoning assessment which includes faculty-designed items and items from a standardized assessment, the Quantitative Literacy and Reasoning Assessment, developmental math course grades and pass rates, and enrollment and pass rates in subsequent math courses.

Data Analytic Strategy: Researchers will qualitatively analyze documents, observational field notes, and transcripts from interviews and focus groups during the development phase to refine materials, create rubrics, and determine usability and feasibility. They will also use qualitative analyses for some of the pilot data. The research team will analyze quantitative data from the interrupted time series data using linear, nonlinear, or mean modeling. The model will include college and instructor fixed effects to account for average differences in quality and other unobservable characteristics across colleges and instructors.


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