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(Mis)match of Students’ Country of Origin and the Impact of Collaborative Learning in Computer Science

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2020 ASEE Virtual Annual Conference Content Access


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Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

NSF Grantees: Diversity 1

Tagged Topics

Diversity and NSF Grantees Poster Session

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Paper Authors


Nicholas A. Bowman University of Iowa

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Nicholas A. Bowman is a professor of Higher Education and Student Affairs, the director of the Center for Research on Undergraduate Education, and a senior research fellow in the Public Policy Center at the University of Iowa. His research uses a social psychological lens to explore salient issues in higher education, including student success, diversity, undergraduate admissions, college rankings, and research methodology.

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Lindsay Jarratt University of Iowa

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Lindsay Jarratt is a PhD candidate in Educational Policy and Leadership Studies. Her research follows from fifteen years of experience in student support and equity roles in higher education, and is focused on the dynamics of equity and belonging in educational institutions.

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KC Culver University of Southern California

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KC Culver is a postdoctoral scholar at the University of Southern California. Her research focuses on the core academic mission of postsecondary institutions with an emphasis on access, equity, and inclusion; she studies faculty careers, pedagogy and the curriculum, and the experiences and outcomes of students from diverse backgrounds.

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Alberto Segre University of Iowa Orcid 16x16

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Alberto Maria Segre is Professor and Chair of the University of Iowa Computer Science Department, where he is also the Gerard P. Weeg Faculty Scholar in Informatics. Professor Segre holds secondary appointments in the Program in Applied Mathematical and Computational Sciences and the Interdisciplinary Genetics Program. He received a B.A. in Music Theory and a B.S., M.S. and Ph.D. in Electrical Engineering, all from the University of Illinois at Urbana-Champaign. His research interests focus on distributed algorithms for discrete optimization problems, with emphasis on algorithmic problems in the biological and health sciences. Most recently, his work has focused on epidemiological simulation and modeling.

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Pair programming is a collaborative learning technique in which two computer science students work together simultaneously on the same programming assignment. Previous studies have found that this approach is often associated with improved learning, academic achievement, and quality of completed assignments. However, the outcomes from pair programming may depend upon the characteristics of students who work within these pairs, and a fair amount of existing research has explored students’ gender and prior programming experience as potentially influential pair attributes.

The present NSF IUSE study explored another important characteristic that has so far been overlooked: the country of origin for students within these pairs, which is often related to language, culture, and other factors. This study randomly assigned students to pair programming partners within four offerings of an introductory computer science course, and students changed partners through additional random pairings during the semester. Overall, 819 responses from 369 undergraduates were examined. These participants were citizens of the United States (63%), China (30%), and 13 other countries (7% combined). Nearly all international students reported whether English was their first language, and more than 98% said that it was not. Given the high representation of international students within this course, 45% of all pairings involved a country of origin mismatch between the two partners. Among the pairings with students from the same country, 81% involved both partners from the U.S., 18% had both partners from China, and 1% had both partners from Malaysia. Cross-classified analyses were conducted to account for the multilevel structure of the data, since individual responses were simultaneously nested within students (i.e., students engaged in more than one pairing) and pairs (i.e., students’ responses were presumably shaped by their partner).

Within same-country pairings, U.S. students had more favorable outcomes than students from other countries; this pattern was observed for the percentage of the assignment completed, the amount of effort students felt that they exerted, how productive they felt during the lab section, their confidence in the finished product, the extent to which they understood relevant concepts, and their overall interest in computer science. Within different-country pairings, international students often experienced benefits from working with a U.S. partner, whereas U.S. citizens achieved no such gains from being paired with an international student. In fact, U.S. students spent a disproportionate amount of time in the driving role (in which they took the lead in physically writing the code) when they were worked with a partner from another country.

Given the random assignment of students to pairings, these results provide causal evidence about the ways in which pair characteristics affect student outcomes. It might seem intuitively appealing to assign pairs based on students sharing a country of origin or native language, but the present findings suggest that international students benefit from collaboration with U.S. partners, while U.S. students do not realize adverse outcomes from being paired with international students.

Bowman, N. A., & Jarratt, L., & Culver, K., & Segre, A. (2020, June), (Mis)match of Students’ Country of Origin and the Impact of Collaborative Learning in Computer Science Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--33974

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