Tailoring First-Year Seminars for Computing Majors. Quick Intros

Transcription

1 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 1

2 The rest of the TUES Team Penny Rheingans (PI) Carolyn Seaman Marie desjardins Maryland 2 nd 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 2

3 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: Four Computing Majors FALL 2013 Enrollment Data Source: UMBC Dept. Institutional Research, Analysis & Decision Support,) Total Undergraduate N (%) Women n (%) URM n (%) Department of Computer Science and Electrical Engineering Computer Engineering (15.0%) 62 (19.0%) Computer Science (11.5%) 120 (13.8%) Department of Information Systems Business Technology Administration (30.2%) 57 (24.3%) Information Systems (18.0%) 102 (16.2%) 3

4 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. 4

5 TUES: Transforming the Freshmen Experience of Computing Majors DUE , 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: 5

6 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) 6

7 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 7

8 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 8

9 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 9

10 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 10

11 Questions about the course? Participants & Research Methods 1. Fall 12 and Fall 13 3 sections of course; one section Fall 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. 11

12 Table 1: TUES Participants Total # Eligible # Female # URM Rheingans(F12) desjardins (F13) Seaman (F13) Seaman (F14) F12 Control F13 Control F14 Control TBD TBD Fall 2012 and Fall 13 Totals Total Experimental Total Control Total Participants 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 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%) 12

13 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 nd Sem. GPA st Year Cum GPA % 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) - 13

14 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 th Sem. GPA nd Year Cum GPA % 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? 14

15 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 % vs %) 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 2 nd 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. 15

16 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. 16

17 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