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The Noncognitive Variable System

The Noncognitive Variable System. William E. Sedlacek University of Maryland wsed@umd.edu http://williamsedlacek.info/. Current Issues . Restriction of Range Tests Grade Inflation Diversity Three Musketeers Problem Range of Abilities. Sternberg. Componential

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The Noncognitive Variable System

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  1. The Noncognitive Variable System William E. Sedlacek University of Maryland wsed@umd.edu http://williamsedlacek.info/

  2. Current Issues • Restriction of Range • Tests • Grade Inflation • Diversity • Three Musketeers Problem • Range of Abilities

  3. Sternberg • Componential Ability to interpret information hierarchically in a well defined and unchanging context. Standardized tests. • Experiential Ability to interpret information in changing contexts, be creative. Standardized tests DO NOT measure. • Contextual Ability to adapt to a changing environment, ability to handle & negotiate the system. Standardized tests DO NOT measure.

  4. Noncognitive Variables • Self- Concept • Realistic Self- Appraisal • Handling System/Racism • Long- Range Goals • Leadership • Strong Support Person • Community • Nontraditional Learning

  5. Advantages of Noncognitive Variable System • Research based • Multiple ways to assess • Retention related • Can use at all levels of education • Considers diversity • Tested legally • Revise to fit situation-flexible • No cost • Admissions, financial aid, student services, teaching, advising • Community building • School counselors approve

  6. Gates Millennium Scholars • Administered by the United Negro College Fund with assistance from Hispanic Scholarship Fund, American Indian Graduate Center Scholars, & Asian & Pacific Islander American Scholarship Fund • Awards 1,000 new scholarships to entering freshmen • Last dollar scholarship award • Renewable through graduate school in math, science, engineering, library science, education & public health • Award period–up to 5 years undergraduate & 4 years graduate • 20 year 1.5 billion $ program

  7. Gates Millennium ScholarsSelection Criteria • African American, American Indian/Alaska Native, Asian Pacific Islander American, or Hispanic American • Federal Pell Grant eligible • Citizen/legal permanent resident or national of U.S. • 3.3 High School GPA • Curriculum Rigor • Noncognitive Variables

  8. GMS Outcomes • Over 11,000 Scholars funded • Freshman retention 97%; sophomore 95% • 5 year program retention rate 88% • 5 year graduation rate 78% • Scholar higher education GPA mean = 3.25 • Raters within each racial group trained to evaluate noncognitive variables -Alpha reliability = .92 • Scholars from 50 states & American Samoa, Guam, Federated States of Micronesia, Puerto Rico, Virgin Islands • Scholars in over 1450 colleges and universities • Scholars more likely to attend: selective, private, residential schools

  9. GMS Undergrad Enrollment 2007

  10. GMS Grad School Enrollment 2007

  11. Some of the Other Programs Using Noncognitive Variables • Washington State Achievers • DC Achievers • Oregon State University • Montgomery College • Engineering Vanguard Program (NACME) • Washington State University • University of British Columbia • Texas A & M University • Louisiana State University • De Paul University • Boston College • Lehigh University

  12. References • Sedlacek, W. E. (2004). Beyond the big test: Noncognitive assessment in higher education. San Francisco: Jossey-Bass. • Sedlacek, W. E. & Sheu, H. B. (2004). Academic success of Gates Millennium Scholars. Readings on Equal Education. 20, 181-197. • Sedlacek, W. E. (2004) Why we should use noncognitive variables with graduate and professional students. The Advisor:The Journal of the National Association of Advisors for the Health Professions. 24 (2), 32-39.

  13. References (continued) • Sedlacek, W. E. & Sheu, H. B. (2005). Early academic behaviors of Washington State Achievers. Readings on Equal Education. 21, 207-222. • Sedlacek, W. E. (2005). The case for noncognitive measures. In W. Camara and E. Kimmel (Eds.). Choosing students:Higher education admission tools for the 21st century. (pp. 177-193) Mahwah, NJ: Lawrence Erlbaum. • Sedlacek, W. E., Benjamin, E., Schlosser, L. Z., & Sheu, H. B. (2007). Mentoring in academia: Considerations for diverse populations. In T. D. Allen & L. T. Eby (Eds.), The Blackwell handbook of mentoring: A multiple perspectives approach (pp. 259-280). Malden, MA: Blackwell.

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