Friday, November 17, 2017
The RP Group

Multiple Measures Assessment Project (MMAP)

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Timeline: 2014-2017


Overview

The Multiple Measures Assessment Project (MMAP) is a collaborative effort led by the RP Group and Educational Results Partnerships’ Cal-PASS Plus system to develop, pilot, and assess implementation of a statewide placement tool using multiple measures. This project is an integrated effort of the California Community College’s Common Assessment Initiative (CAI). Visit the CAI website >

For over two decades, California’s community colleges have been required to assess and place students in the curriculum through means other than a single test score. However, until recently, test scores have dominated the placement process across the California system. Yet, growing evidence indicates that multiple measures, such as high school transcripts and noncognitive variables, can greatly improve the accuracy of the placement process, and in turn, can facilitate student movement into and through college-level coursework.

As part of the CAI, the Multiple Measures Assessment Project aims to contribute to the advancement of the approach across the state’s system. MMAP has three primary objectives:

  1. Development of a data warehouse: includes creation of a secure, large, and robust data warehouse to collect, store, and analyze multiple measures which will include high school transcript and test data, as well as MIS and placement test data for each community college.

  2. Creation of a comprehensive analytical model: includes identification, analysis, and validation of known multiple measures data points, drawing directly from research obtained through the Student Transcript-Enhanced Placement Study pilot, and leveraging of predictive analytic software to identify new data points that can serve as effective multiple measures.

  3. Development of user tools for assessment and placement using multiple measures: includes the engagement of pilot colleges throughout the process to assist in development of the analytic tools and user interface, and to test the tools and models using local college data supplied through the data warehouse.

Pilot Colleges

MMAP is engaged with over 60 pilot colleges from across the state that are providing feedback on predictive models and user tools to help inform the adoption of the multiple measures approach. Visit pilot colleges page > 

For more information...

Contact Ken Sorey, Educational Results Partnership, or Mallory Newell, RP Group, for information on the MMAP. 

Contact Jennifer Coleman, Butte College, for information on the CCC-CAI. 

Project Director

Mallory Newell

Project Team

Peter Bahr, PhD, Rachel Baker, Craig Hayward, PhD, Nathan Pellegrin, Terrence Willett, MS

Educational Results Partnership

John Hetts, Daniel Lamoree, Ken Sorey

Funders 

Butte-Glenn Community College District 

Partners

California Community Colleges Chancellor's Office, California Community Colleges Common Assessment Initiative, Educational Results Partnership

Pilot College Resources 

Pilot College Resources

Find information for pilot colleges, including resources for getting started on your campus; decision rules and analysis code; guides for implementing multiple measures, non cognitive variables, and self-reported data; creating a research plan; and data submission.

Publications

Publications

Review white papers, technical reports, updates, and frequently asked questions (FAQs) produced by the MMAP team.

Presentations and Webinars

Access presentations and webinars conducted on a range of topics related to implementing and assessing the impact of multiple measures assessment. 

Note:  While these resources are generally for pilot colleges participating in the Multiple Measures Assessment Project, they are available to any California community college stakeholder interested in learning more about how to adopt this approach.

News and Events 

There are no News and Events scheduled at this time. Please check back soon. Also, you can visit our archived Resources for pertinent information to Multiple Measures.