10/28/2014
Tailoring First-Year Seminars
for Computing Majors
Penny Rheingans, Marie desJardins,
Susan Martin, Carolyn Seaman
21st National Conference on Students in Transition
Denver, CO, October 20, 2014
Session CR-77
www.tues.umbc.edu
Quick Intros
•
•
•
•
Name
Institution
Role
1 really quick sentence about why
you are here or what you hope to
learn
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10/28/2014
The rest of the
TUES Team
Penny Rheingans (PI)
[email protected]
Carolyn Seaman
[email protected]
Marie desJardins
[email protected]
Maryland
• 2nd in nation for % of workforce that are
professional and technical workers
• Cybersecurity subsector makes up
70% of IT jobs in state
• Baltimore/Washington/Northern
Virginia home to major players:
National Security Agency, Northrop
Grumman, Lockheed Martin, BAE
Systems, Federal Agencies, NIH
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University of Maryland
Baltimore County (UMBC)
Student Enrollment, Fall 2013:13,908
Graduate: 2,772
Undergraduate: 11,136
Full-time: 9,508, Part-time: 1,628
College of Engineering & Information
Technology: 3,051
Minority Enrollment: 40%
African American 16%, Asian American 17%
Hispanic 5%
AVG SAT: 1216 (2-part), 1800 (3-part)
Faculty
501 Full-time, 268 Part-time
More about accomplishments:
http://www.umbc.edu/excellence.pdf
Four Computing Majors
FALL 2013 Enrollment Data
Source: UMBC Dept. Institutional Research, Analysis & Decision
Support,) http://oir.umbc.edu/databook/)
Total
Undergraduate
N (%)
Women
n (%)
URM
n (%)
Department of Computer Science and Electrical Engineering
Computer
Engineering
Computer
Science
326
49 (15.0%)
62 (19.0%)
867
100 (11.5%)
120 (13.8%)
Department of Information Systems
Business
Technology
Administration
235
71 (30.2%)
57 (24.3%)
Information
Systems
628
113 (18.0%)
102 (16.2%)
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10/28/2014
First Computing Courses
prior to project
• IS101-required BTA; many non-majors;
has “Y” section
• CMSC100-introduction for non-CMSC
majors
• CMSC104-introduction to problem
solving and programming; was half nonmajors and half majors without
programming experience
• CMSC201-required by CMSC and CMPE
majors; requires programming
experience and Calculus 1 co-requisite
Observations leading to
formulating TUES project in
2011:
1. Students frequently struggled to choose
between four majors and initial coursessometimes damaging grades and
motivation.
2. Existing first courses emphasized tools and
programming skills but lacked discussion
about grand challenges and applications.
3. Students begin majors with uneven
academic and professional skills.
4. First-year students reported a lack of
opportunities to interact or collaborate with
other students in their majors.
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10/28/2014
TUES: Transforming the
Freshmen Experience of
Computing Majors
DUE-1140589, 8/15/12-7/31/15
Design, implement, and evaluate a new firstyear course for computing majors at UMBC
Intended Project Goals:
1. Increase retention of COMP101 participants
within computing majors
2. Increase graduation rate of COMP101
participants in computing majors
3. Increase academic performance of
COMP101 participants
COMP101: Computational
Thinking and Design
1. Overview of the discipline
2. Key technical skills
3. Group design and
implementation experience
4. Academic and professional skills
Syllabus and schedule available at:
http://tues.umbc.edu/project-documents/
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10/28/2014
COMP101 Structure
• 4 credit course for freshman
computing majors
• 40 students per section
• Two 75 minute sessions (Tu/Th)
taught by CS and IS faculty w/2
undergraduate teaching fellows
• 50 minute Professional
Development Session (Friday)
taught by student affairs
professional w/ 4 peer mentors
Peer Mentors: Grace Chandler (CS, Sophomore), Logan
Wroblewski (IS, Sophomore), Tahreem Gondal (IS, Senior),
Joshua Massey (CS, Sophomore)
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10/28/2014
Course Learning Goals
1.
2.
3.
4.
5.
6.
7.
Increase understanding of the discipline, in
terms of different majors and careers.
Clarify students' personal interests and
motivations about their choice of major and
career.
Increase confidence, self-efficacy, and
community.
Expose students to, and let them practice,
design and development skills.
Strengthen writing, presentation, and teaming
skills.
Teach skills in problem solving, algorithmic
analysis, and computational thinking.
Help students learn how to study effectively and
how to access campus resources.
Assignments
• 5 Journal Entries
– Strengths; attendance at career fair;
study skills reflection; finals
preparation; career options
reflection
• 3 programming assignments in
Processing
• Resume and cover letter
• Semester Game
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10/28/2014
Overview of the
Discipline
• Big Ideas: computational thinking, algorithmic
problem solving, abstraction, history, theory of
computation
• Data: representation, design and modularization,
data structures, big data, visualization
• Hardware and Systems: beginnings of design,
computer architecture, operating systems,
networks
• People: analysis and requirements, usability, HCI
and accessibility, social and ethical implications
• Applications: graphics and games, intelligence,
security
Key Technical Skills
• Algorithmic design
• Introduces programming using
Processing
• Data analysis skills
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10/28/2014
Group Design Project
• Semester Game:
– Simulation of 15-week semester
– Players make choices about how to allocate
time
– Game calculates outcomes of those choices
– User interaction and presentation
• Phases:
– Design
– First demo
– Demo evaluation
– Poster
– Presentation
Final Project
Presentations
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10/28/2014
Professional Development
• Understanding and using Strengths
• Academic success skills: time
management, test taking, using
academic resources
• Working effectively in teams
• Giving and receiving feedback
• Giving clear and effective presentations
• Understanding degree requirements
and career planning
• Professional networking
Some of the 2014 Teams
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Questions about the
course?
Participants & Research
Methods
1. Fall 12 and Fall 13 – 3 sections of course;
one section Fall 14.
2. Control group volunteers from other
courses (IS101, IS101Y, CMSC104,
CMSC201).
3. Pre and post-course surveys; focus
groups; interviews.
4. Analysis of major switching, GPA, grades
in subsequent computing courses.
5. Themes from focus groups, interviews,
journal entries.
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10/28/2014
Table 1: TUES Participants
Total #
Eligible
# Female
# URM
Rheingans(F12)
30
28
3
2
desJardins (F13)
28
27
3
5
Seaman (F13)
27
25
5
5
Seaman (F14)
43
43
16
10
F12 Control
44
16
4
2
F13 Control
41
27
6
2
33
31
TBD
TBD
F14 Control
Fall 2012 and Fall 13 Totals
Total
Experimental
85
80
11
12
Total Control
85
43
10
4
124
21
16
Total Participants
Table 2: TUES Participants Fall 2012 & Fall 2013
COMP101
N=80
Control
N=43
New
Computing
Majors in
COEIT
N=553
Gender
Female
Male
11 (13.8%)
69
10 (23.3)
33
83 (15%)
470
URMs
Female
Male
0
12 (15%)
1 (2.3%)
3 (7.0%)
12 (2%)
67 (12%)
Majors
BTA
IS
CMPE
CMSC
11 (13.8%)
28 (35.0%)
7 (8.8%)
34 (42.5%)
0
4 (9/3%)
18 (41.9%)
20 (46.5%)
30 (5.4%)
47 (8.5%)
157 (28.4%)
319 (57.7%)
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10/28/2014
Questions about the
participants?
Table 3: First Year Academic Progress and Retention
(Fall 2012 & Fall 2013 data)
COMP101
N=80
Control Group
N=43
All New Freshmen
Cohort of
Computing Majors
(N=553)
1st Sem. GPA
2.80
3.08
-
2nd Sem. GPA
2.52
3.06
-
1st Year Cum GPA
% retained same
major
2.69
3.09
-
83.80%
81.40%
% retained in other
computing major
6.25%
9.30%
90.05%
95.40%
5 of 13
(7 were CMSC)
4 of 8
(5 were CMPE)
% retained in COEIT
major
# switched computing
majors/total # major
switchers
86.60%
91.30%
-
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10/28/2014
Table 4: Second Year Academic Progress and Retention
(Fall 2012 data)
COMP101
N=28
3rd Sem. GPA
Control Group
N=16
2.81
3.24
All New Freshmen
Cohort of
Computing Majors
(N=553)
-
4th Sem. GPA
2.97
3.00
-
2nd Year Cum GPA
% retained same
major
3.08
3.14
-
82.14%
68.75%
% retained in other
computing major
3.57%
18.75%
85.71%
83.75%
% retained in COEIT
major
52.50%
68.60%
Activity
Work with a partner and review
the handout of tables 3 and 4.
• What jumps out at you?
• What conclusions do you draw
from the data?
• What issues exist with the data?
• What is confusing?
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10/28/2014
Summary of Preliminary
Findings about Outcomes
• At the end of the first year, 89.05% of students in
COMP101 and 90.07% of those in the control group
were retained in a computing major.
• After two years a higher percentage of COMP101
•
•
participants from Fall 2012 were in their initial major as
compared to the control group and whole population.
(82.14% vs. 68.75% vs. 52.50%)
COMP101 students actually had a slightly lower first year
cumulative GPA than students in the control group (2.69
vs. 3.09).
Among those leaving computing, COMP101 students
were more likely to do so after the first year. Students in
the control group who switched majors during the first
year were more likely to pursue another computing major
than those in COMP101 (50% versus 30%).
Issues to Keep in Mind
• We are unable to control or account for
underlying differences in the characteristics of
students in COMP101, Control, and Freshman
Cohort. (ex. Math readiness; prior programming
experience). CSEE department allowed
placement into CMSC201 for students without
prior programming experience.
• Too early to determine long term retention and
graduation rates. We will know more when we
have 2nd year of data from F13 participants.
• Small numbers of women and minorities prohibit
analyses of the impact of COMP101 on these
groups.
• Our reporting system does not capture major
changing behavior or retention between
computing majors-limiting this comparison.
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10/28/2014
Next Steps
• Repeat this analysis once we have a
second year of performance data for Fall
2013 participants.
• Analyze qualitative data collected on
surveys, journal entries, focus groups
and interviews.
• Analyze pre- and post survey data.
• Report results related to course
outcomes.
• Continue dissemination and
institutionalization of the course. Apply
knowledge gained to CMSC201.
Things we will explore in
the qualitative data:
• Experiences of those with no/little prior
programming experience.
• The nature and impact of the team based
learning and team design project.
• How students used and made meaning
of the career information and
assignments in the course.
• Academic challenges faced by computing
majors and how students applied
techniques and used resources.
• Impact of peer instruction and impact of
experience on peer mentor development.
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10/28/2014
Lessons Learned
• Need to offer incentives to entice
control group volunteers
• In hindsight, our evaluation and
assessment plans were very
ambitious given the project’s
personnel and budget
• Institutionalization is a
challenging process; slower than
we anticipated
Questions??
Susan Martin
[email protected]
410.455.3109
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