TY - GEN
T1 - Profiling Conversational Programmers at University
T2 - 20th Annual ACM Conference on International Computing Education Research, ICER 2024
AU - Hur, Jinyoung
AU - Cunningham, Kathryn
N1 - We thank our lab members Arif Demirtas and Yoshee Jain for their support throughout the project. We would also like to express our gratitude to our colleagues, Colleen Lewis, Morgan Fong, Andrea Watkins, Victor Zhao, Max Fowler, and Andrew Chen for their valuable feedback and insights. We extend our appreciation to Lu Ting from the University of Illinois Division of Management Information and Michael Kang for their support in the facilitation of this project. This project is funded by the Department of Computer Science at the University of Illinois Urbana-Champaign.
PY - 2024/8/13
Y1 - 2024/8/13
N2 - Background and Context. Instruction in most introductory computing courses is typically focused on how to program. However, non-majors who take computing courses have a diverse set of desired endpoints. One group of non-majors are the conversational programmers, who do not want to program in their career but enroll in computing courses to improve their ability to communicate about technical topics and their competitiveness in the job market. Research suggests that these learners need an alternate instructional approach, but so far, conversational programmers in higher educational contexts have only been studied in a limited number of small-scale studies. Objectives. To inform curriculum design for conversational programmers at the university level, we (a) examine the prevalence of conversational programmers among non-majors and their characteristics, (b) understand conversational programmers' desired learning goals and classroom activities, and (c) investigate factors associated with these learners' motivation to learn computing. Methods. We designed a survey based on Expectancy-Value Theory and prior work about conversational programmers. We collected responses from randomly sampled non-major students at a large public university, and we analyzed the survey data with descriptive and inferential statistics. Findings. We found that conversational programmers are the largest proportion of non-majors in our sample, both overall and across historically underrepresented groups in CS. We replicated prior findings of low self-efficacy for programming of conversational programmers. We found that conversational programmers' motivation for taking more computing courses is paradoxically driven more by their interest in computing than its utility, despite their general lack of enjoyment in computing. We validate a previously proposed set of conversational programmers' learning goals and show that they value employment-oriented learning goals over those focused on conversations. Implications. Our results suggest that addressing the needs of conversational programmers can contribute to broadening participation in computing. Our study motivates a learner-centered curriculum design that could address conversational programmers' learning needs by enhancing their self-efficacy and interests prior to focusing on conversational goals.
AB - Background and Context. Instruction in most introductory computing courses is typically focused on how to program. However, non-majors who take computing courses have a diverse set of desired endpoints. One group of non-majors are the conversational programmers, who do not want to program in their career but enroll in computing courses to improve their ability to communicate about technical topics and their competitiveness in the job market. Research suggests that these learners need an alternate instructional approach, but so far, conversational programmers in higher educational contexts have only been studied in a limited number of small-scale studies. Objectives. To inform curriculum design for conversational programmers at the university level, we (a) examine the prevalence of conversational programmers among non-majors and their characteristics, (b) understand conversational programmers' desired learning goals and classroom activities, and (c) investigate factors associated with these learners' motivation to learn computing. Methods. We designed a survey based on Expectancy-Value Theory and prior work about conversational programmers. We collected responses from randomly sampled non-major students at a large public university, and we analyzed the survey data with descriptive and inferential statistics. Findings. We found that conversational programmers are the largest proportion of non-majors in our sample, both overall and across historically underrepresented groups in CS. We replicated prior findings of low self-efficacy for programming of conversational programmers. We found that conversational programmers' motivation for taking more computing courses is paradoxically driven more by their interest in computing than its utility, despite their general lack of enjoyment in computing. We validate a previously proposed set of conversational programmers' learning goals and show that they value employment-oriented learning goals over those focused on conversations. Implications. Our results suggest that addressing the needs of conversational programmers can contribute to broadening participation in computing. Our study motivates a learner-centered curriculum design that could address conversational programmers' learning needs by enhancing their self-efficacy and interests prior to focusing on conversational goals.
KW - Conversational Programmers
KW - Learner-centered Design
KW - Learning Goals
KW - Non-majors
UR - http://www.scopus.com/inward/record.url?scp=85202808503&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85202808503&partnerID=8YFLogxK
U2 - 10.1145/3632620.3671123
DO - 10.1145/3632620.3671123
M3 - Conference contribution
AN - SCOPUS:85202808503
T3 - ICER 2024 - ACM Conference on International Computing Education Research
SP - 293
EP - 311
BT - ICER 2024 - ACM Conference on International Computing Education Research
PB - Association for Computing Machinery
Y2 - 13 August 2024 through 15 August 2024
ER -