Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
A Holistic Review of GenderDifferences in EngineeringAdmissions and Early Retention 15th International Conference for Wome...
Motivation o Purdue’s College of Engineering (COE) has been   working to increase the representation of women in   its fir...
Holistic Review o Subject matter expectations o Overall high school grade point average (GPA), on a 0 – 4.0 scale o Core h...
Research Questions 1.   Are the metrics of women admitted to CoE      statistically higher than those of men admitted to  ...
Admission Years: 2006, 2007, and2008 o Applicants to the College of Engineering (resulting     in 26,396 total records ove...
Demographics of Fall 06, 07, and 08 Applicants to Eng.                                                          2008      ...
Analysis of Metric Medians forApplicant Pool                                 Total     All Applicants                     ...
Boxplot of Overall GPA -Applicants                        Boxplot of Overall GPAs for Men and Women                       ...
Boxplot of SAT Total Scores -Applicants                                 Boxplot of SAT Total Scores for Men and Women     ...
Analysis of Metric Medians forAdmits to Engineering                                    Total All Admits to Engineering    ...
Boxplot of Overall GPA - Admits                        Boxplot of Overall GPAs for Men and Women                          ...
Boxplot of SAT Total Scores - Admits                             Boxplot of SAT Total Scores for Men and Women            ...
Analysis of Metric Medians forDenied Students                                    Total       All Denies                   ...
Boxplot of Overall GPA - Denied                           Boxplot of Mens and Womens Overall GPA                          ...
Boxplot of SAT Total Scores - Denied                                 Boxplot of Mens and Womens SAT Total Scores          ...
Discussion ❍   An unbiased process would result in no statistical     differences in the metrics of the admitted populatio...
Possible Conclusions o Only the highest ability women are encouraged   and/or self-select to apply to the College of   Eng...
Bias at Work? o According to Sevo & Chubin, “In situations   where we evaluate the professional   competence of men and wo...
Bias at Work? o If a policy or tradition of an institution is to   require a certain level of achievement on a   test that...
Motivation: Why study studentretention and success?                                                               o Unders...
Model of Student SuccessStudent Attitudinal Success Instrument (SASI)                                                ( Imb...
Modified Model of Student SuccessStudent Attitudinal Success Instrument (SASI)
Methodso Psychometric properties    • Internal consistency (reliability)        − Cronbach’s coefficient alpha (α > 0.80) ...
Methods, continued o Normative taxonomy: cluster analysis    • McDermott’s 3-stage cluster analysis        − Standard clus...
Results          o Psychometric properties                 • Cronbach’s coefficient alpha values for all constructs and   ...
Results o Normative taxonomy    • 3 clusters indicated for each cohort (2004 – 2007)    • 2004 – 2007 cohorts        − Vis...
Cluster analysis results
Model of Student SuccessStudent Attitudinal Success Instrument (SASI)                                                ( Imb...
Results: Ability to Identify At-RiskStudents             Performance from New Model E’
Results: Important Factors by DifferentMethods
So What!!!!! o Model results provide insight that can be   used institutionally, programmatically, and   individually to m...
Institutional View2004 Cohort, 1 Year Retention                                                           Male (N=3852)   ...
Institutional ViewAggregated 2004-2006 Cohorts – 1 Year Retention                                                   Caucas...
Institutional View   2004 Cohort, 1 Year Retention and 8, 10 Semester GraduationMale (N=1219)                             ...
Programmatic View
Individual View
Update o Used this information for a discussion with   the Admissions office staff o For 2011 Admission process,    • fema...
Acknowledgment The researchers wish to acknowledge the  support provided by a grant from the  National Science Foundation,...
Discussion and Questions
Affective, MultidimensionalConstructso Motivation    • Control, challenge, curiosity, career outlook        − Defined in t...
Affective, MultidimensionalConstructs o Academic Self Efficacy       − “Individuals’ beliefs of their competence affect ev...
Affective, MultidimensionalConstructs o Expectancy-Value    • Community involvement, employment       opportunities, persi...
Admissions Process (thru Fall ‘08)Application                                                                             ...
Upcoming SlideShare
Loading in …5
×

ICWES15 - A Holistic Review of Gender Differences in Engineering Admissions and Early Retention. Presented by Dr PK Imbrie, Purdue University, United States and Dr Teri Reed-Rhodes, Purdue University, United States

726 views

Published on

Presentation from ICWES 15 Conference - July 2011, Australia

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

ICWES15 - A Holistic Review of Gender Differences in Engineering Admissions and Early Retention. Presented by Dr PK Imbrie, Purdue University, United States and Dr Teri Reed-Rhodes, Purdue University, United States

  1. 1. A Holistic Review of GenderDifferences in EngineeringAdmissions and Early Retention 15th International Conference for Women Engineers and Scientists Adelaide Australia, 19-22 July, 2011 Beth M. Holloway P.K. Imbrie Teri Reed-Rhoads School of Engineering Education Purdue University West Lafayette, Indiana, USA
  2. 2. Motivation o Purdue’s College of Engineering (COE) has been working to increase the representation of women in its first year class for many years. o Over the last 5 years, we have seen a 46% increase in the number of applications received from women, but only a 24% increase in the number of women admitted. o At the same time, casual analysis seems to indicate that admitted women have higher metrics, on average, than admitted men.
  3. 3. Holistic Review o Subject matter expectations o Overall high school grade point average (GPA), on a 0 – 4.0 scale o Core high school GPA (GPA of English, math, science, foreign language, and speech classes only.), on a 0 – 4.0 scale o High school class rank, in percentile o Standardized test scores o Overall grades in academic coursework o Grades related to intended major o Strength of student’s overall high school curriculum o Trends in achievement o Ability to be successful in intended major o Personal background and experiences o Time of year student applies o Space availability in intended program
  4. 4. Research Questions 1. Are the metrics of women admitted to CoE statistically higher than those of men admitted to CoE? 2. To what extent do affective and cognitive measures from the Student Access and Success Instrument (SASI) model differences of success as measured by retention and graduation based on sex?
  5. 5. Admission Years: 2006, 2007, and2008 o Applicants to the College of Engineering (resulting in 26,396 total records over the 3 cohort years) o Applicants who are considered “Beginners”. (Transfer students, for example, were filtered out) (25,587 total records remaining) o Applicants for the Fall semester ( 25,361 total records remaining) o Applicants with complete applications (incomplete applications were filtered out) o 23,068 total records remaining
  6. 6. Demographics of Fall 06, 07, and 08 Applicants to Eng. 2008 2007 2006 Demographics of Applicants Women Men Women Men Women Men Number % Number % Number % Number % Number % Number % Total Number of Records 1632 5964 1652 6430 1369 6021 Caucasian, Non-Hispanic 1116 74.5% 4269 78.9% 1037 71.4% 4262 77.0% 926 74.3% 4023 76.8% African American, Non-Hispanic 106 7.1% 214 4.0% 113 7.8% 206 3.7% 95 7.6% 259 4.9% Hispanic American 73 4.9% 229 4.2% 76 5.2% 244 4.4% 66 5.3% 219 4.2% Race /Ethnicity Native American 15 1.0% 44 0.8% 6 0.4% 46 0.8% 5 0.4% 44 0.8% Aisan American / Pacific Islander 160 10.7% 531 9.8% 178 12.3% 637 11.5% 135 10.8% 562 10.7% Other 20 1.3% 93 1.7% 32 2.2% 92 1.7% 19 1.5% 89 1.7% Not Reported 8 0.5% 29 0.5% 10 0.7% 46 0.8% 1 0.1% 43 0.8% All Domestic 1498 91.8% 5409 90.7% 1452 87.9% 5533 86.0% 1247 91.1% 5239 87.0%Residency Indiana (% of Domestic) 334 22.3% 1510 27.9% 318 21.9% 1624 29.4% 272 21.8% 1715 32.7% International 134 8.2% 555 9.3% 200 12.1% 897 14.0% 122 8.9% 782 13.0%
  7. 7. Analysis of Metric Medians forApplicant Pool Total All Applicants Women Men p-value Median 3.9 3.7 Overall GPA 0.0000 N 4457 17441 Median 3.74 3.48 Core GPA 0.0000 N 4603 18113 Median 93 86 Class Rank 0.0000 N 3029 11346 Median 620 600 SAT Verbal 0.0000 N 4611 18148 Median 670 680 SAT Math 0.0000 N 4611 18148 Median 1300 1280 SAT Total 0.0000 N 4611 18148
  8. 8. Boxplot of Overall GPA -Applicants Boxplot of Overall GPAs for Men and Women All Applicants to Engineering 4.0 3.5 3.0 Overall GPA 2.5 2.0 1.5 Women Men
  9. 9. Boxplot of SAT Total Scores -Applicants Boxplot of SAT Total Scores for Men and Women All Applicants to Engineering 1750 1500 SA T Tot a l Sco re 1250 1000 750 500 Women Men
  10. 10. Analysis of Metric Medians forAdmits to Engineering Total All Admits to Engineering Women Men p-value Median 4.0 3.8 Overall GPA 0.0000 N 3829 12790 Median 3.80 3.60 Core GPA 0.0000 N 3935 13201 Median 94 90 Class Rank 0.0000 N 2558 7963 Median 630 620 SAT Verbal 0.0000 N 3911 13127 Median 680 700 SAT Math 0.0000 N 3911 13127 Median 1320 1330 SAT Total 0.0100 N 3911 13127
  11. 11. Boxplot of Overall GPA - Admits Boxplot of Overall GPAs for Men and Women All Admits to Engineering 4.0 3.5 Overall GPA 3.0 2.5 2.0 Women Men
  12. 12. Boxplot of SAT Total Scores - Admits Boxplot of SAT Total Scores for Men and Women All Admits to Engineering 1700 1600 1500 1400 SAT Total Score 1300 1200 1100 1000 900 800 Women Men
  13. 13. Analysis of Metric Medians forDenied Students Total All Denies Women Men p-value Median 3.4 3.2 Overall GPA 0.0000 N 241 2071 Median 3.06 2.91 Core GPA 0.0000 N 255 2202 Median 75 66 Class Rank 0.0000 N 171 1485 Median 490 510 SAT Verbal 0.0002 N 277 2324 Median 550 590 SAT Math 0.0000 N 277 2324 Median 1050 1110 SAT Total 0.0000 N 277 2324
  14. 14. Boxplot of Overall GPA - Denied Boxplot of Mens and Womens Overall GPA Denied Students 4.0 3.5 3.0 Ov e ra ll GPA 2.5 2.0 1.5 Women Men
  15. 15. Boxplot of SAT Total Scores - Denied Boxplot of Mens and Womens SAT Total Scores Denied Students 1500 1250 SA T Tot a l Sco re 1000 750 500 Women Men
  16. 16. Discussion ❍ An unbiased process would result in no statistical differences in the metrics of the admitted populations. ❍ SAT/ACT are intended to be a predictor of first year college grades, not academic achievement. ❍ Research shows that high school metrics are a better predictor of first year college grades than SAT (correlation coefficient of 0.42 vs. 0.36) Adding the two together gives a correlation coefficient of 0.52. ❍ 37 studies have shown a consistent gender bias in standardized tests. One study showed a 35 point bias in favor of males on the SAT math section.
  17. 17. Possible Conclusions o Only the highest ability women are encouraged and/or self-select to apply to the College of Engineering, and men with a much wider range of academic ability are encouraged and/or self-select to do so. o Women are held to a higher standard than men with regard to their high school performance. o The admissions counselors put more weight on test scores than high school performance in the admissions process.
  18. 18. Bias at Work? o According to Sevo & Chubin, “In situations where we evaluate the professional competence of men and women, and where there is much room for interpretation, men will have significant advantage due to unconscious assumptions. Our schema for males is a better fit for professional success, and especially for high-intensity scientific and engineering careers.”
  19. 19. Bias at Work? o If a policy or tradition of an institution is to require a certain level of achievement on a test that is know to disadvantage a certain group, institutional bias exists.
  20. 20. Motivation: Why study studentretention and success? o Understand the factors that Retention in Engineering for Several Large and Mid-Size Institutions impact students’ persistence in 100 engineering. Such information 90 could provide: 80 • provide a bases to assess the impact of% Retention 70 program/institution-level decisions aimed 60 at attracting students to engineering as 50 well as student retention and success. 40 • evaluate the influences of current 30 classroom pedagogical practices; and 20 modify those deemed less effective; and 10 • more thoughtfully develop targeted 0 interventions aimed at retaining students 1 2 3 4 5 6 7 8 9 10 11 12 who otherwise have a propensity to leave Semester engineering; Male Female Minority o Improve current retention modeling methods that are used to predict engineering students’ retention in engineering.
  21. 21. Model of Student SuccessStudent Attitudinal Success Instrument (SASI) ( Imbrie, Lin & Malyscheff 2008; Reid 2009 )
  22. 22. Modified Model of Student SuccessStudent Attitudinal Success Instrument (SASI)
  23. 23. Methodso Psychometric properties • Internal consistency (reliability) − Cronbach’s coefficient alpha (α > 0.80) • Spearman-Brown formula used for subfactors with < 10 items 2o Exploratory Factor Analysis (EFA) • Used to establish subfactor structure or verify structure if pre- defined • SAS proc factor, promax rotationo Confirmatory Factor Analysis (CFA) • LISREL fit indices − χ2 − Goodness of Fit (GFI > 0.90) − Comparative Fit Index (CFI > 0.95) − RMS Error Approximation (RMSEA < 0.08 for acceptable fit)
  24. 24. Methods, continued o Normative taxonomy: cluster analysis • McDermott’s 3-stage cluster analysis − Standard cluster analysis of mutually exclusive groups − Combining clusters from individual groups − Review to determine if individual data points actually fit within a different cluster − Cattell’s between cluster similarity coefficient – rp > 0.95 excellent similarity, rp < 0.7 poor similarity 1 • Determines the number of groups based on normalized z- scores of overall constructs
  25. 25. Results o Psychometric properties • Cronbach’s coefficient alpha values for all constructs and subfactors > 0.80 − Spearman-Brown formula used to extrapolate subfactors to 10 items − Exceptions: – Self-worth construct (0.69, 2007 cohort) – Team vs. Individual / Individual orientation subfactor (0.74, 2006 cohort) o Exploratory Factor Analysis (EFA) • Subfactor structure verified or defined for each construct o Confirmatory Factor Analysis (CFA) • Subfactor structure verified for each construct • Fit indices 2,3,4 in all cases showed excellent fit* − GFI>0.90, CFI>0.95*RMSEA < 0.05 for excellent fit, <0.08 for acceptable fit
  26. 26. Results o Normative taxonomy • 3 clusters indicated for each cohort (2004 – 2007) • 2004 – 2007 cohorts − Visual inspection and − Values of Cattell’s between cluster similarity coefficient again show three distinctly different clusters.
  27. 27. Cluster analysis results
  28. 28. Model of Student SuccessStudent Attitudinal Success Instrument (SASI) ( Imbrie, Lin & Malyscheff 2008; Reid 2009 )
  29. 29. Results: Ability to Identify At-RiskStudents Performance from New Model E’
  30. 30. Results: Important Factors by DifferentMethods
  31. 31. So What!!!!! o Model results provide insight that can be used institutionally, programmatically, and individually to make informed decisions that will enhance undergraduate Engineering Education as well as provide a more personal learning experience for our students.
  32. 32. Institutional View2004 Cohort, 1 Year Retention Male (N=3852) SAT_V TeamInd 1 SAT_M Female (N=823) Efficacy 0.8 SEM_ENGL 0.6 Motivation AVG_ENGL 0.4 0.2 Major SEM_MATH 0 Leader AVG_MATH Surface SEM_SCI Deep AVG_SCI Meta Expect
  33. 33. Institutional ViewAggregated 2004-2006 Cohorts – 1 Year Retention Caucasian,Asi- Am,Other (N=4217) Underrepresented SAT_V 1 Minority (N=178) TeamInd SAT_M Efficacy 0.8 SEM_ENGL 0.6 Motivation AVG_ENGL 0.4 0.2 Major SEM_MATH 0 Leader AVG_MATH Surface SEM_SCI Deep AVG_SCI Meta Expect
  34. 34. Institutional View 2004 Cohort, 1 Year Retention and 8, 10 Semester GraduationMale (N=1219) 8 Semester Graduation 1 Year Retention SAT_VFemale (N=289) SAT_V TeamInd 1 SAT_M TeamInd 1 SAT_M Efficacy SEM_EN… Efficacy SEM_EN… Motivati… 0.5 AVG_EN…Motivati… 0.5 AVG_EN… Major SEM_M… 0 Major SEM_M… Leader AVG_M… 0 Leader AVG_M… Surface SEM_SCI 10 Semester Graduation Deep AVG_SCI Surface SEM_SCI SAT_V Meta Expect TeamInd 1 SAT_M Deep AVG_SCI 0.8 Meta Expect Efficacy SEM_ENGL 0.6 Motivation 0.4 AVG_ENGL 0.2 Major SEM_MATH 0 Leader AVG_MATH Surface SEM_SCI Deep AVG_SCI Meta Expect
  35. 35. Programmatic View
  36. 36. Individual View
  37. 37. Update o Used this information for a discussion with the Admissions office staff o For 2011 Admission process, • female applicants are up an additional 11% (Now 55% over the past 6 years) • Female admits are up 19% o Also presented to Presidential Scholarship Committee prior to selections • Female awards up from 28 to 51% • Female yield is up 33% (Headcount of 489)
  38. 38. Acknowledgment The researchers wish to acknowledge the support provided by a grant from the National Science Foundation, Division of Engineering Education and Centers (Award No. 0416113).
  39. 39. Discussion and Questions
  40. 40. Affective, MultidimensionalConstructso Motivation • Control, challenge, curiosity, career outlook − Defined in terms of one’s pursuit of an activity for its own sake » Pintrich & Schunk, 1996o Metacognition • Planning, self-checking, cognitive strategy, awareness − Strategies for planning, monitoring and modifying one’s own cognitions. » Pintrich & DeGroot, 1990o Propensity towards Deep and/or Surface Learning • Deep: Motive, strategy: Surface: Studying, memorization − Propensity of a student within a learning environment to adjust their learning style (deep or surface) to achieve the learning goal. » Biggs, Kember and Leung, 2001
  41. 41. Affective, MultidimensionalConstructs o Academic Self Efficacy − “Individuals’ beliefs of their competence affect everything they do, and proposes that self-efficacy should prove to be an excellent predictor of their choice and direction of behavior. “ » Bandura, 1993 − Studies have related self efficacy to retention » Besterfield-Sacre et al., 1999; Pajares, 1996; House, et al., 1995; Bandura, 1986; Lent, Brown and Larkin, 1986 o Leadership • Motivation, planning, self-assessment, teammates − The student’s self appraisal of their leadership abilities was identified as a noncognitive characteristic effecting student retention » Tracy & Sedlacek, 1984; Hayden & Holloway, 1985; Ting, 2000 o Team vs. Individual Orientation • Individual, team dynamic − Industry continues to seek graduates who can function as a team member and leader » McMaster, 1996
  42. 42. Affective, MultidimensionalConstructs o Expectancy-Value • Community involvement, employment opportunities, persistence, social engagement − Perception of the expectancy and value of academic, social and employment expectancies » Wigfield & Eccles, 2000; Besterfield-Sacre et al., 1999; Hayden & Holloway, 1985; Schaefers et al., 1997 o Major Decision • Certainty of decision, difficulty in decision, personal issues, urgency of decision, independence − Related to student success » Schaefers et al., 1997; Smith & Baker, 1987; Haislett & Hafer, 1990; Osipow, 1999
  43. 43. Admissions Process (thru Fall ‘08)Application Admit App Yes Clear No Send to Clear Yesarrives in student to E. complete? Admit? designated Admit?ADMS data Code as “A”.processing ADMS counselor. No Send request No Yes for more info. Make Code as “I”. recommendation Admit and send to student to E. committee Code as “A”. Hold decision. Code student as “E” or “P” Committee Admit meets and student to E. agrees Code as “A”. Deny admission to student. Code as “D” Admit student to 2 nd choice. Code as “A”.

×