Talking about Leaving Revisited: What do we know about why

6/4/15 Talking about Leaving Revisited:
What do we know about why
undergraduates leave the
sciences?
Elaine Seymour, University of Colorado at Boulder,
Joseph J. Ferrare, University of Kentucky
Gardner Ins/tute Symposium on Student Reten/on, Asheville, NC, June 8-­‐9, 2015 Should We Still Be Talking About Leaving?
A National Portrait of Switching Using the
Beginning Postsecondary Students Survey
2004/2009 Cohort
NOTE:
All findings are preliminary and should not be cited. 1 6/4/15 National & Institutional Team
Joseph J. Ferrare, University of Kentucky
You Geon Lee, University of Wisconsin-Madison
Tim Weston, University of Colorado Boulder
Among first time beginning students who began their
postsecondary education in a bachelor’s degree program…
2 6/4/15 Switching rates for those who initially declared a major falling
outside of STEM and switched to another “non-STEM” major
Preliminary Findings from Multivariate Analysis
•  The analysis of non-STEM majors suggests that
the gender and racial switching patterns
observed in STEM majors are unique even
when controlling for a wide variety of
covariates.
•  Women are 1.51 times more likely than men to
switch from STEM majors even when
controlling for academic ability, family
background, undergraduate experiences, and
type of institution.
•  The odds of switching for men and women in
non-STEM majors are statistically identical.
3 6/4/15 Preliminary Findings from Multivariate Analysis
•  Whereas Black men were much less likely to switch
into STEM than White men, Black women were more
likely to switch into STEM majors than White women
and even significantly more likely than Black men.
•  In contrast, Black women were significantly more
likely to switch out of STEM than their male
counterparts, which is consistent with the fact that, on
average, women were more likely than men to switch
out of STEM.
•  In short, Black women were more likely to switch into
STEM majors, but they were also more likely to switch
out.
NOTE: Even though this finding was statistically significant, it was generated
from a very small sample size and should be interpreted with caution.
The Persistence Study
Team members: University of Colorado at Boulder:
Elaine Seymour, Anne-Barrie Hunter, Heather
Thiry, Dana Holland, and Raquel Harper
Coding and Hypotheses from the Switcher
interviews.
Note: No findings are, as yet, available.
4 6/4/15 The Interview Sample
311 students were interviewed at the six study sites.
98 (31.5%) switchers and 213 (68.5%) non-switchers.
Both samples were subdivided by sex, race/ethnicity,
discipline, and (for non-switchers) low “math
readiness” scores on college entry (30% of all nonswitching seniors).
Overall, women are 60.5% of the sample and men are
39.5%.
Students of color are 40.2% of the sample and white
students are 59.8%.
The Codebook: deductive and inductive codes.
.
Code categories. Factors contributing
to switching and
persistence for both switchers and non-switchers:
•  Reasons for choice of majors and career aspira1ons: how well grounded in interest, understanding, relevant experience; who influences choices; influence of the economy and job markets. •  High school prepara1on: in sciences and math, and in college-­‐
level skills (study, independent learning, /me management). •  Issues of transi1on to STEM majors and college level work. •  Aspects of students’ learning experiences and their consequences (both in STEM and non-­‐STEM majors). •  Classroom climate. Compe//on/collabora/on, belonging, responses to weedout experiences. Gender and race/ethnicity: aXtudes, beliefs, experiences, explana/ons. 5 6/4/15 •  Sources of academic and personal support; their
significance for persistence: Faculty, TAs, advisors,
peers, campus groups, family, etc.
•  Learning identities, behaviors, attitudes: taking
responsibility for learning and problems;
motivation, survival strategies.
•  How financing college: influences on persistence
•  Parental influences: choice, financing, responses to
students’ concerns and switching decisions.
•  Switching processes: push, pull, conflicts, stages.
•  Consequences of switching; benefits & gains; costs
& losses.
Emergent Hypotheses HIGH SCHOOL PREPARATION & COLLEGE TRANSITION
Under-preparation in high school— in math and the sciences,
study habits or time management—creates switching risks.
Deficiencies not identified and addressed quickly prompt early
switching. Students of color, working class or first generation
students from under-resourced high schools may be at
enhanced risk. All students may be at risk where high schools
award high grades for modest effort and students fail to
develop study skills and work habits required in STEM majors.
Overcoming high school deficiencies and adjusting quickly to
college modes of work make the period of transition from high
school to college critical for survival in STEM majors.
6 6/4/15 CHOICE
•  Unexamined or under-informed major or career
aspirations put students at risk of switching.
Conversely, a well-grounded, driving interest in the
major and related careers supports persistence.
•  Some aspects of our data suggest a culturallysupported shift to parental approval of STEM majors
and careers for daughters. Noted in: forced choices,
discounting of non-STEM aspirations, strong
preference for careers perceived--sometime
erroneously--as high-paying; unsupportive parental
attitudes towards difficulties in STEM majors, and
negative responses to switching decisions.
GRADES AND IDENTITY
Difficulty in overcoming an internalized perfectionism
that ties identity to high scores poses persistence risks.
Noted in TAL-1, this may now be a stronger trend
where high school grades, achieved with moderate
effort by talented students, promote high expectations
by parents and a sense of entitlement in students.
Some interviewees describe letting go of high grade
expectations; others find it difficult to disentangle their
identity from their grades. Presumptions that their
grades are ‘poor’ also pose survival risks in majors
where traditions of low grades and curve grading
make it harder for students to know how they are
progressing. These effects may be stronger among
women.
7 6/4/15 UNINTENDED CONSEQUENCES OF WEEDOUT CLASSES
Weed-out class practices may prompt losses of particular
student groups from STEM majors. Departments and faculty
who organize and teach weed-out classes have no way of
knowing what students they are weeding out and why.
o  Gatekeeper classes (often in chemistry, physics, and
calculus) may disproportionately weed out students who
are non-white, working class, first generation, especially
those from under-resourced high schools. Inadequate high
school preparation may be too great to overcome where
weed-out classes are encountered early. The role of
advisors in steering under-prepared students around such
courses until their skills have been built up may be critical
to their survival.
o  Through their negative impact on GPAs, weedout
classes may redirect students’ aspirations away
from careers that entail competitive professional
school entry (e.g., medical, vetinerary, dental, law)
to careers seen as less desirable, but more attainable
with a reduced GPA. Such shifts may be notable in
majors serviced by weed out classes, such as the life
sciences or engineering.
As in TAL-1, we have identified high achieving
switchers: this may be one such group.
8 6/4/15 Pedagogical Practice in Scientific Purgatory:
An Analysis of Gateway Courses NOTE:
All findings are preliminary and should not be cited. Gateway Team
University of Kentucky
•  Joseph J. Ferrare
•  Amy Mitchell
University of Wisconsin-Madison
•  Ross Benbow
•  Erika Vivyan
•  Mark Connolly
•  Jenny Vandenberg
University of Colorado Boulder
•  Tim Weston
•  Anne-Barrie Hunter
9 6/4/15 Gateway Study Overview
•  71 introductory and mid-level gateway courses
–  Physics, chemistry, biology, mathematics, computer science, and
engineering
•  E.g., General Physics, General Chemistry, General Biology, Calculus 1 – 3,
Data Structures, Mechanics
•  The following data were collected:
– 
– 
– 
– 
73 interviews (~90 minutes each) with instructors of record
146 hours of classroom observations (2 observations/course)
57 student focus groups (n=246 students)
1,433 SALG surveys
Instructor Characteristics
N
% of total sample
48
25
66%
34%
56
5
2
0
1
9
77%
7%
3%
0%
1%
12%
19
14
13
11
9
7
26%
19%
18%
15%
12%
10%
26
16
14
6
5
2
2
2
36%
24%
19%
8%
7%
3%
3%
3%
Gender:
Male
Female
Racial-Ethnic Identity:
White
Asian or Pacific Islander
Latin@ or Hispanic
Black or African American
American Indian or Alaska Native
Not reported
Field of Study:
Chemistry
Mathematics
Physics
Engineering
Biology
Computer Science
Job Title:
Lecturer or Instructor
Associate Professor
Professor
Assistant Professor
Senior Lecturer or Senior Instructor
Visiting Professor
Teaching Assistant
Other
!
10 6/4/15 Student Focus Group Characteristics
N
% of total sample
Gender:
Male
Female
108
137
44%
56%
Racial/ethnic Identitya:
White
Asian or Pacific Islander
Latin@ or Hispanic
Black or African American
American Indian or Alaska Native
Multi-racial
Not reported
166
44
13
10
1
10
19
67%
18%
5%
4%
<1%
4%
8%
92
71
32
24
14
10
8
6
6
1
37%
29%
13%
10%
6%
4%
3%
3%
2%
1%
Majorb:
Biology
Engineering
Computer Science
Other Science
Other Non-Science
Physics
Mathematics
Chemistry
Social Science
Undeclared
a
Students who reported multiple racial/ethnic groups are counted as members of all the groups indicated as well as multi-racial.
Students who reported multiple majors are counted as a student in all of the majors indicated.!
b
What are the most important things instructors want
students to learn from these courses?
Learning objectives
11 6/4/15 Thematic Coding Analysis of Instructor Interviews
Most Important things students should learn
% of
Instructors*
Content
63.2
Conceptual understanding and application
47.4
Perseverance in solving problems
24.6
Doing science
17.5
Connections to daily experience
15.8
Interpretation
5.3
*NOTE: Percentages reflect those instructors for whom we have corresponding student
focus group data (n=57)
How do instructors think students come to learn
these most important things?
Instructors’ (folk) theories about how students learn
and their own role in those processes.
12 6/4/15 Things Instructors Do v. Things Students Do
Things Instructors Do…
% of
Instructors
Things Students Do…
% of
Instructors
Provide problem scenarios
38.6
Practice
43.9
Motivate relevance
33.3
Develop perseverance
35.1
Demonstrate & model
28.1
Provide examples
24.6
Conceptual understanding & application
33.3
Scaffolding material
22.8
Become resourceful at solving problems
19.3
17.5
Variability in style
21.1
Collaboration
Establish rapport & accessibility
14.0
Make connections
14.0
Work from theory to application
14.0
Explanation & discussion
10.5
Provide clear explanations
10.5
Intellectual risk-taking
8.8
Repetition
10.5
Socratic dialogue
10.5
Take on an apprenticeship model
3.5
Provide analogies
5.3
*NOTE: Percentages reflect those instructors for whom we have corresponding student
focus group data (n=57)
What are the types and frequencies of pedagogical
practices observed in Gateway Courses?
13 6/4/15 Teaching Dimensions Observation Protocol (TDOP)
Computer
Biology Chemistry Science
`
Teaching Methods
%
Eng.
Math
Physics
%
%
%
%
%
Lecture
15.7
11.9
12.2
15.4
3.9
12.4
Lecture: pre-made visuals
41.5
31.0
28.2
27.4
6.0
33.3
Lecture: hand-made visuals
10.3
41.7
53.6
58.6
65.8
38.7
Lecture: demonstration
1.7
3.0
22.1
3.7
0.1
4.2
Lecture: interactive
6.0
0.6
20.0
10.8
0.0
7.0
Small group work
26.5
12.5
3.4
15.4
2.9
21.0
Desk work
3.9
8.1
3.4
0.0
4.2
11.5
Multimedia
3.0
0.0
0.2
1.0
0.0
0.4
Student presentation
3.2
1.0
0.0
1.0
0.0
2.0
NOTE: The percentages represent the proportion of observed 2-minute intervals in
which each practice was observed.
TDOP: Pedagogical Moves
Computer
Biology Chemistry Science
Pedagogical Moves
Eng.
Math
Physics
%
%
%
%
%
%
Movement
29.9
15.6
1.6
1.6
9.1
14.2
Humor
3.7
8.0
18.2
10.6
10.3
9.4
Illustration
13.8
8.1
42.6
18.1
11.4
12.9
Organization
4.7
4.6
1.8
5.8
1.4
2.3
Emphasis
6.7
8.3
8.1
5.8
2.9
3.3
Assessment
12.5
7.5
5.9
0.0
1.2
16.8
Administrative task
4.7
4.9
9.2
7.4
5.3
4.1
14 6/4/15 TDOP: Instructor/Student Interactions
Computer
Biology Chemistry Science
Eng.
Math
Physics
%
%
%
%
4.7
16.2
13.8
7.7
3.8
25.4
38.1
51.1
39.8
33.0
42.2
Comprehension question
10.3
13.1
14.9
14.3
12.0
8.5
Student question
9.5
24.0
28.8
20.5
20.2
23.3
Student response
26.7
33.0
60.4
38.8
30.7
40.9
Peer interaction
27.3
12.6
14.0
16.5
3.0
23.0
Instructor/Student
Interactions
%
%
Rhetorical question
5.0
Display question
TDOP: Cognitive Engagements
Computer
Biology Chemistry Science
Cognitive Engagements
Eng.
Math
Physics
%
%
%
%
%
%
Retain/recall information
38.9
28.3
43.2
37.6
26.1
33.2
Problem solving
15.3
32.9
39.4
22.6
26.6
43.5
Creating
2.6
3.0
0.7
13.3
1.5
2.9
Connections
17.2
18.3
47.1
22.2
15.1
19.0
15 6/4/15 TDOP: Instructional Technology
Computer
Biology Chemistry Science
Eng.
Math
%
%
%
%
%
%
Pointer
15.5
8.1
6.3
2.7
0.0
9.7
Chalk/white board
8.6
47.7
43.2
49.6
62.5
44.4
Overhead projector
0.0
4.3
5.9
0.0
0.0
0.1
PowerPoint/slides
47.3
23.5
35.6
20.9
0.0
41.6
Clickers
3.0
4.3
3.8
0.0
0.0
12.3
Demonstration equipment
0.0
3.8
18.2
3.0
0.0
4.1
Digital tablet
2.4
4.7
14.9
15.2
6.7
0.0
Simulation
3.7
0.4
1.1
1.1
0.0
0.3
Instructional Technology
Physics
Do these practices tend to cluster into distinct types?
16 6/4/15 Cluster and Principal Component Analysis Suggest that
Courses Tend to Fall into One of Four General Scripts
1.  Chalk Talks (n=34 / 48%):
– 
– 
Courses that are facilitated by instructors who spend the vast
majority of instructional time lecturing at the chalkboard and
frequently posing questions to the class.
Students experience very little peer interaction, demonstration of
knowledge, or the use of technology.
2.  Slide Shows (n=21 / 30%)
– 
– 
– 
Courses that are facilitated by instructors who spend the vast
majority of time lecturing through the use of pre-made PowerPoint
slides.
A significant amount of class time is also spent lecturing at the
chalkboard or lecturing without any visuals or demonstrations.
Students in slide show courses spend more than twice as much
time interacting with their peers as do those in chalk talks, and are
more frequently engaged with real-time assessments (e.g., clickers).
3. Inter-activities (n=9 / 13%)
– 
– 
Students spend the vast majority of their time interacting with
their peers in small groups while the instructor moves throughout
the room discussing the material and answering questions.
In nearly half of the observed 2-minute intervals students are
engaged in some form of problem-solving activity, and also spend
a significant amount of time creating and brainstorming new ideas.
4. Connectors (n=6 / 9%)
– 
– 
– 
– 
Constituted by a high degree of variability in practice—most of
which centers on illustrating, demonstrating, and connecting
knowledge.
In the process, instructors in these courses frequently utilize humor
while posing display questions and student comprehension
questions.
Students experience frequent demonstration and illustration of
course content and a variety of cognitive engagements, such as
problem solving, creating, and connecting.
Connector courses also make the greatest use of technology,
especially digital tablets, demonstration equipment, clickers, and
overhead projectors.
17 6/4/15 How do students conceptualize their
experiences of gateway instructional
practices and curriculum, and with what
consequences?
This multi-faceted analysis will draw on sources from three
sources: Gateway study student focus groups, Persistence
study switcher and non-switcher interviews and focus groups,
and results from the SALG survey administered to students in
the sample of gateway courses,
18