Applied Computing for Behavioral and Social Sciences (ACBSS) Minor - - PowerPoint PPT Presentation

Applied Computing for Behavioral and Social Sciences (ACBSS) Minor

Farshid Marbouti, Valerie Carr, Belle Wei, Morris Jones, and Amy Strage San Jose State University

Road map

Student recruitment and profile Courses and pedagogical approaches Program assessment Conclusions

Road map

Student recruitment and profile Courses and pedagogical approaches Program assessment Conclusions

Why social science students?

 Tech economy generates vast amounts of

data regarding human behavior

 Growing need for employees with social

science knowledge + technical skills

Why social science students?

 Increase job prospects  In the US, social science students account for more than 1

• ut of 7 bachelor’s degrees (Synder & Dillow, 2012)

 …. yet they have among the highest unemployment rates of

all college grads (Burning Glass Technologies, 2013)

 Adding technical skills can double the jobs available to them

and raise salaries (Burning Glass Technologies, 2013)

Why social science students?

 Increase diversity in engineering courses  Women, African-Americans, and Latinx students

are underrepresented in engineering

 …. and overrepresented in social science majors

Student recruitment

 Challenges:  Students uncertain how programming relates to their interests  Assume that tech jobs require extensive programming experience  Worried about having low grades on transcript  When recruiting…  Included info about relevant career opportunities (e.g., user experience

research, econometrics, data analysis, etc.)

 Reassured that class is for social science students only

Student profile

Demographic Category College of Engineering ENGR120 (F16, F17, S18) # Students 5,060 113 Gender Female 18% 55.8% Male 82% 44.2% Ethnicity URM 23% 27.4% non-URM 77% 72.6% Majors Psychology N/A 58.4% Economics N/A 19.5% Other N/A 22.1%

Road map

Student recruitment and profile Courses and pedagogical approaches Program assessment Conclusions

Interdisciplinary team

 Courses created by 12 faculty members across several colleges and departments:  AVP faculty development  Engineering  Psychology  Economics  Political Science

ENGR 120: Programming concepts

 Intro to Python + applications to the social sciences  Basics: Data types, conditionals, iteration, functions, etc.  Application: Web scraping, data analysis and visualization  Hybrid lecture + lab format  Lectures include “Spotlight on ___” and growth mindset

concepts

 Build upon and extend Python knowledge  Data structures: Classes, trees, graphs, etc.  More sophisticated algorithms for data analysis  Similar format and pedagogy

ENGR 121: Data structures & algorithms

 Intro to R  Similarities and differences from Python  Focus on data analysis and visualization  Similar format and pedagogy

ENGR 122: Data technology

 Culminating experience  Student-selected project advised by domain-relevant

faculty member

 Project proposal, regular code reviews and project demos,

final paper, presentation

ENGR 195E: Interdisciplinary computing project

Road map

Student recruitment and profile Courses and pedagogical approaches Program assessment Conclusions

Assessment approaches

 Question: Are we developing pedagogical practices

best suited to social science students?

 Assessments:  Pre- and post-course surveys  Focus groups with external evaluator

Post-survey: ENGR 120

 Students read a series of statements relating to the

course, and rated their level of agreement with each

 1 = strongly disagree, 5 = strongly agree  Data for three semesters:  Fall 2016 (39%), Fall 2017 (95%), Spring 2018 (93%)

 Overall, enthusiasm was

high

 Significant changes over

time:

 Hard  Positive environment  Community

Interesting Hard Different New Career Glad Connections Positive Envt Community F16 F17 S18 F16 F17 S18 F16 F17 S18 Rating

3.0 4.0 5.0 3.5 4.5 3.0 4.0 5.0 3.5 4.5 3.0 4.0 5.0 3.5 4.5

Rating Rating

*** * ***

* *** p < 0.05 p < 0.001

Road map

Student recruitment and profile Courses and pedagogical approaches Program assessment Conclusions

Conclusions

 Successfully recruited students whose backgrounds are typically underrepresented in engineering/CS  Students found course material challenging but accessible;

• pened up new career aspirations

 Future: Incorporate more industry connections (e.g., internships)

Acknowledgments

 ACBSS students  NSF DUE 1626600  Interdisciplinary team:  Amy Strage  David Schuster  Evan Palmer  Cheryl Chancellor-Freeland  Rui Liu  Matthew Holian  Melinda Jackson  Belle Wei  Farshid Marbouti  Morris Jones  Chao Li Tarng

Questions?

valerie.carr@sjsu.edu sjsu.edu/acbss

Introduction to Computing Using Python: An Application Development Focus (2nd edition)

Focus groups: ENGR 121 & 122

 In general, students saw courses as providing unique opportunity to gain

valuable knowledge that made them more marketable

 I'm interested in data analytics and I feel like being in Silicon Valley you need to have some

sort of programming experience.

 My cousin works as a financial analyst, and they require you to have Python and R. She

never took these classes and she struggles.

 I looked at opportunities in the CS department to see if I could take a few courses…they

exclude anybody who’s not in their program. This is the only opportunity to do this path with Python.

Quiz wrapper