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Background: GMS Program

Success and Attrition Factors for High Achieving Underrepresented / Underserved Students Barry Nagle , GMS/UNCF Senior Research Associate Jin Liu, Research Analyst National Scholarship Providers Association Annual Conference Pittsburgh, PA October 2014. Background: GMS Program.

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Background: GMS Program

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  1. Success and Attrition Factors for High Achieving Underrepresented/ Underserved StudentsBarry Nagle, GMS/UNCF Senior Research AssociateJin Liu, Research AnalystNational Scholarship Providers Association Annual ConferencePittsburgh, PAOctober 2014

  2. Background: GMS Program The Gates Millennium Scholars (GMS) program, established in 1999, is a 1.6 billion dollar initiative funded by grant from the Bill & Melinda Gates Foundation. The goal of GMS is to promote academic excellence and to provide an opportunity for 20,000 outstanding students with significant financial need to reach their full potential.

  3. Background: Program Partners • UNCF- the United Negro College Fund is the administrator of the GMS initiative and has partnered with the following organizations: • Asian & Pacific Islander American Scholarship Fund • American Indian Graduate Center Scholars • Hispanic Scholarship Fund

  4. Topics • Success/Attrition Factors • Demographic Characteristics • Institution Characteristics • Student Education Characteristics • High School GPA • Nomination Composite Scores • STEM Major Status • Deferment • In Development: Engagement • Applying the Knowledge • Discussion

  5. Study Purpose • Identify students potentially at-risk of not attaining graduation

  6. Success Factors: Demographics

  7. Demographics: Cohort • Overall graduation rates are approximately 90% (Scholars in Cohorts 1-8)

  8. Demographics: Gender • Females earn an undergraduate degree at higher rates than males • Females have a 91% graduation rate. Males have a 89% graduation rate. X2= 18.8366, df=1, p<.000

  9. Demographics: Primary Ethnicity • Three Primary Ethnicity groups have graduation rates of 90% or more (X2= 576.0241, df=3, p<.000) • African American: 93.4% • American Indian: 72.0% • Asian & Pacific Islander: 95.8% • Hispanic American: 92.5%

  10. Demographics: Gender and Primary Ethnicity • For every ethnicity, females have higher graduation rates than males (X2= 595.1700, df=7, p<.000). Female-Male differences: • African American: 3.3% • American Indian: 2.9% • Asian & Pacific Islander: 0.4% • Hispanic American: 2.1%

  11. Demographics: Other • Graduation Rates for Students with Known Status • First-generation college students • First-Generation: 90.2% • Non First-Generation: 89.5% • Dependency Status • Dependent: 90.6% • Independent: 87.1%

  12. Success Factors: Institution Characteristics

  13. Institution Characteristics: Top Graduation Rates

  14. Institution Characteristics: Private vs. Public • Scholars that attend private institutions graduate at higher rates than Scholars that attend public institutions • Private school attendees have a 93.9% graduation rate compared to the public school attendee graduation rate of 89.3% (X2= 81.2958, df=1, p<.000)

  15. Institution Characteristics: Private/Public and Gender • Females and males that attend private institutions graduate at higher rates than Scholars that attend public institutions (X2= 105.8424, df=3, p<.000)

  16. Institution Characteristics: Private/Public and Primary Ethnicity • Scholars that attend private institutions in all PE groups have greater graduation rates than public school attendees (X2= 618.804, df=7, p<.000). Private-Public differences: • African American: 1.8% • American Indian: 6.1% • Asian & Pacific Islander: 0.9% • Hispanic American: 4.8%

  17. Institution Characteristics: Private/Public, Gender and Primary Ethnicity • For all primary ethnicities, females and males that attend private institutions have higher graduation rates than Scholars that attend public institutions (X2= 642.6317, df=15, p<.000). • AA, AP, and HA Scholars that attend private institutions have graduation rates of 90% or higher • AI Scholars that attend private institutions have graduation rates of 70% or higher

  18. Success Factors: Student Education Characteristics

  19. Student Characteristics: High School GPA • Scholars that earn a degree have higher High School GPAs than Scholars that do not earn a degree • Degree earners have a 3.77 mean HS GPA compared to 3.70 for non-degree earners (t= 11.3729, df=924.425, p<.000)

  20. Student Characteristics: High School GPA by Gender • Female and male Scholars that earn a degree have higher High School GPAs than female and male Scholars that do not earn a degree, respectively (F=48.55, df=3, p<.000) • Significant TukeyHSD contrasts: • Female Earned Degree HS GPA (3.78) vs. Female Non-Earned Degree HS GPA (3.67) • Female Earned Degree HS GPA (3.78) vs. Male Non-Earned Degree HS GPA (3.70) • Male Earned Degree HS GPA (3.77) vs. Male Non-Earned Degree HS GPA (3.70) • Male Earned Degree HS GPA (3.77) vs. Female Non-Earned Degree HS GPA (3.67)

  21. Student Characteristics: High School GPA by Primary Ethnicity • For each Primary Ethnicity group, the HSGPA of the earned degree group was higher than the non-degree earning group (F=68.95, df=7, p<.000) • Sixteen TukeyHSD contrasts were statistically significant

  22. Student Characteristics: High School GPA by Gender and Primary Ethnicity • For each Primary Ethnicity and Gender group, the HSGPA of the earned degree group was higher than the non-degree earning group (F=68.95, df=7, p<.000) • 54 (of 120) TukeyHSD contrasts were statistically significant

  23. Student Characteristics: Nomination Scores • Scholars that earn a degree have higher nomination composite scores than Scholars that do not earn a degree • Degree earners have a 77.49 mean composite score compared to 76.10 for non-degree earners (t= 5.3582, df=799.943, p<.000)

  24. Student Characteristics: Nomination Scores by Gender • Female and male Scholars that earn a degree have higher nomination composite scores than female and male Scholars that do not earn a degree, respectively (F=15.61, df=3, p<.000) • Significant TukeyHSD contrasts: • Female Earned Degree score (77.49) vs. Female Non-Earned Degree HS GPA (76.26) • Female Earned Degree HS GPA (77.49) vs. Male Non-Earned Degree HS GPA (75.86) • Male Earned Degree HS GPA (77.50) vs. Male Non-Earned Degree HS GPA (75.86) • Male Earned Degree HS GPA (77.50) vs. Female Non-Earned Degree HS GPA (76.26)

  25. Student Characteristics: Nomination Scores by Primary Ethnicity • AP and HA Scholar degree earners have nomination scores higher than non-degree earners. AA and AI degree earner nomination scores are slightly lower than non-degree earners (F=86.6, df=7, p<.000) • Fifteen TukeyHSD contrasts were statistically significant

  26. Student Characteristics: Nomination Scores by Gender and Primary Ethnicity • The following Gender/PE degree earner groups have nomination scores higher than non-degree earners: AA males, AI males, AP females, HA males, HA females. (F=41.40, df=15, p<.000) • 53 (of 120) TukeyHSD contrasts were statistically significant

  27. Student Characteristics: Nomination Score Areas • Cognitive • Curriculum rigor • Overall academic achievement • Structure of/use of language in essays • Non-Cognitive • Positive self-concept/self-esteem • Realistic self-appraisal • Understanding and navigation of social and organizational systems • Preference for long-term over immediate need • Successful leadership experience • Community service • Non-traditional, Self-directed acquisition of knowledge or skill • Evidence of strong support person

  28. Student Characteristics: Nomination Score Correlation Results • Significant Cognitive Correlations • Curriculum rigor (r=0.1445, p <.000) • Overall academic achievement (r=0.1369, p<.000) • Structure of/use of language in essays (r=0.769, p<.000) • Cognitive composite (r=.1634,p<.000) • Significant Non-Cognitive Correlations • Positive self-concept/self-esteem (r=0.0349, p=.007) • Understanding and navigation of social and organizational systems (r=0.0312, p=0.16) • Preference for long-term over immediate need (r=0.0441, p=.001) • Non-Cognitive composite (0.0260, p=.045)

  29. Student Characteristics: Nomination Score Differences by Area

  30. Student Characteristics: Nomination Score t-test Results • Significant Cognitive Mean Differences • Curriculum rigor (d= 0.37, t=9.439, p <.000) • Overall academic achievement (d= 0.36, t=9.438, p <.000) • Structure of/use of language in essays (d= 0.22, t=5.152, p <.000) • Cognitive composite (d= 0.94, t=10.47, p <.000) • Significant Non-Cognitive Mean Differences • Positive self-concept/self-esteem (d= 0.08, t=2.227, p =.026) • Understanding and navigation of social and organizational systems (d=0.10, t=2.114, p =.035) • Preference for long-term over immediate need (d=0.11, t=2.950, p =.003) • Non-Cognitive composite (d=0.35, t=1.555, p =.120)

  31. Student Characteristics: Nomination Scores Cognitive Areas Yes means the correlation between the area and degree attainment was statistically significant at the .05 level

  32. Student Characteristics: Nomination Scores Non-Cognitive Areas Yes means the correlation between the area and degree attainment was statistically significant at the .05 level

  33. Student Characteristics: STEM Major • Scholars that are STEM majors graduate at higher rates than non-STEM majors • STEM majors have 92.9% graduation rate compared to 89.9% for non-STEM majors (X2= 11.3737, df=1, p<.000)

  34. Student Characteristics: STEM Major by Gender • STEM and Non-STEM females have higher graduation rates than STEM and NON-STEM males (X2= 260.1778, df=3, p<.000)

  35. Student Characteristics: STEM Major by Primary Ethnicity • Graduation rates for all PE groups are higher for STEM majors than non-STEM majors except for HA Scholars (X2= 219.9267, df=7, p<.000)

  36. Student Characteristics: Deferment Types • Deferment Types • Academic • Personal Hardship • Medical • Service • Emergency • Personal (Administrative): Includes only Scholars that were given this deferment prior to Senior year • Scholars Considered • Year Confirm: 2000-2007 • Freshman Start Point

  37. Student Characteristics: Graduation Rates by Deferment Type ^May be low due to lack of information on graduation status

  38. Student Characteristics: Graduation Rates by Deferment Type and PE Group ^May be low due to lack of information on graduation status *Less than 30 Scholars

  39. Student Characteristics: Graduation Rates by Deferment Time

  40. Factor Being Developed: Student Engagement

  41. Engagement Index • Annual GMS Engagement Survey • Five Engagement Areas • Academic Engagement (Degree goal, Study habits, Class Preparation) • Campus Engagement (Activity participation, Campus service usage) • Community Engagement (Volunteer/Public Service) • GMS Program Engagement (Knowledge/use of program resources, Program activity participation, Scholar engagement) • Non-Engagement (Non-academic related employment/time) • Individual index areas are combined for an overall index

  42. Engagement Index • Engagement level is defined as high, moderate, low, or no engagement in each area. • Overall Engagement Formula: • Academic + Campus + Community + Program–Non-engagement

  43. Engagement Index: Preliminary Results All Institutions

  44. Engagement Index: Preliminary Results Campus Engagement Manager Institutions

  45. Engagement Index: Comparison of High Engagement Levels

  46. Engagement Index: Questions for Next Steps • Engagement Scores by Level: Do we adjust the cut-scores for each level? • Weighting: Do we weight engagement levels differently when developing the overall engagement score? • Outcomes: What outcomes are appropriate to link to engagement?

  47. In Development: Applying this Knowledge

  48. Applying this Knowledge • Goal is to use this information to develop a dashboard in the following areas • Graduation: Due to high graduation rates, valid prediction model can not be developed. Instead will look at success/risk level in each area • Graduate school in program funded area: Completing a logistic regression model for this outcome

  49. Applying this Knowledge: Graduation • Graduation: To inform on graduation likelihood, index will be created for each Scholar • Decisions to be made: Areas to include, risk level for each area, how risk areas will be combined.

  50. Applying this Knowledge: Graduate School • Program funds students for graduate school in these areas: • Computer Science • Education • Engineering • Library Science • Mathematics • Public Health • Science • Independent Variables being evaluated: Gender, PE, Major, GPA, Institution, First-generation status, Engagement. • Decisions: Other variables to be considered

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