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www.tues.umbc.edu

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

Quick Intros

• Name

• Institution

• Role

• 1 really quick sentence about why

you are here or what you hope to

learn

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The rest of the

TUES Team

Penny Rheingans (PI)rheingans@cs.umbc.edu

Marie desJardinsmariedj@cs.umbc.edu

Carolyn Seamancseaman@umbc.edu

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 2013:13,908Graduate: 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 MajorsFALL 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 326 49 (15.0%) 62 (19.0%)

Computer

Science 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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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 non-

majors 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 courses-

sometimes 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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TUES: Transforming the

Freshmen Experience of

Computing Majors

DUE-1140589, 8/15/12-7/31/15

Design, implement, and evaluate a new first-

year 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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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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Course Learning Goals

1. Increase understanding of the discipline, in

terms of different majors and careers.

2. Clarify students' personal interests and

motivations about their choice of major and

career.

3. Increase confidence, self-efficacy, and

community.

4. Expose students to, and let them practice,

design and development skills.

5. Strengthen writing, presentation, and teaming

skills.

6. Teach skills in problem solving, algorithmic

analysis, and computational thinking.

7. 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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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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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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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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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

F14 Control 33 31 TBD TBD

Fall 2012 and Fall 13 Totals

Total

Experimental 85 80 11 12

Total Control 85 43 10 4

Total Participants 124 21 16

Table 2: TUES Participants Fall 2012 & Fall 2013

COMP101 Control

New

Computing

Majors in

COEIT

N=80 N=43 N=553

Gender Female 11 (13.8%) 10 (23.3) 83 (15%)

Male 69 33 470

URMs Female 0 1 (2.3%) 12 (2%)

Male 12 (15%) 3 (7.0%) 67 (12%)

Majors BTA 11 (13.8%) 0 30 (5.4%)

IS 28 (35.0%) 4 (9/3%) 47 (8.5%)

CMPE 7 (8.8%) 18 (41.9%) 157 (28.4%)

CMSC 34 (42.5%) 20 (46.5%) 319 (57.7%)

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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 2.69 3.09 -

% retained same

major 83.80% 81.40% 86.60%

% retained in other

computing major 6.25% 9.30% -

% retained in COEIT

major 90.05% 95.40% 91.30%

# switched computing

majors/total # major

switchers

5 of 13

(7 were CMSC)

4 of 8

(5 were CMPE)

-

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Table 4: Second Year Academic Progress and Retention

(Fall 2012 data)

COMP101

N=28

Control Group

N=16

All New Freshmen

Cohort of

Computing Majors

(N=553)

3rd Sem. GPA 2.81 3.24 -

4th Sem. GPA 2.97 3.00 -

2nd Year Cum GPA 3.08 3.14 -

% retained same

major 82.14% 68.75% 52.50%

% retained in other

computing major 3.57% 18.75% -

% retained in COEIT

major 85.71% 83.75% 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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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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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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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

susan@umbc.edu

410.455.3109