Table of Links Abstract and 1 Introduction 1.1 The twincode platform 1.2 Related Work 2 Research Questions 3 Variables 3.1 Independent Variables 3.2 Dependent Variables 3.3 Confounding Variables 4 Participants 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis Acknowledgments and References 5.4 Data Analysis During the manual tagging of the dialog messages, all pairs in which the gender of any of the peers is disclosed in any way are excluded from the data analysis. Then, before analyzing response variables, the internal consistency of the questionnaire data is checked using Cronbach’s alpha and Kaiser criterion. After that, for every dependent variable v, we compute the distance between the two in–pair tasks as the absolute value of the difference, i.e. | v(t2) – v(t1) |. Ideally, this distance should be lower for the students in the control group (no information about partners’ genders) than for those in the experimental group (with two different perceived partners’ genders at t1 and t2). Therefore, for every variable, we perform a t–test to detect distance differences between the groups. Then, using the data from the experimental group only, we perform a t–test to detect differences in the scores of every dependent variable between perceived partner’s gender, i.e. to detect differences in the scores when partners are perceived as men vs. as women. Finally, to detect a potential interaction between the perceived partner’s gender and the subject’s gender, we perform a mixed–model ANOVA with the perceived gender as a within– subjects variable and subject’s gender as a between–subjects variable. As complementary analyses, we also study (i) the correlation between the induced and the perceived gender for the subjects in the experimental group, and the distribution of the perceived gender (if any) in the control group; and (ii) the potential cultural impact of the different locations at which the experiment is carried out. All the data analysis will be performed using R scripts, that will be available in a public repository together with the datasets in the corresponding laboratory package. Authors:
(1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es);
(2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es);
(3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es);
(4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu);
(5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu);
(6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 The twincode platform 1.1 The twincode platform 1.2 Related Work 1.2 Related Work 2 Research Questions 2 Research Questions 3 Variables 3 Variables 3.1 Independent Variables 3.1 Independent Variables 3.2 Dependent Variables 3.2 Dependent Variables 3.3 Confounding Variables 3.3 Confounding Variables 4 Participants 4 Participants 5 Execution Plan and 5.1 Recruitment 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis 5.4 Data Analysis Acknowledgments and References Acknowledgments and References 5.4 Data Analysis During the manual tagging of the dialog messages, all pairs in which the gender of any of the peers is disclosed in any way are excluded from the data analysis. Then, before analyzing response variables, the internal consistency of the questionnaire data is checked using Cronbach’s alpha and Kaiser criterion. After that, for every dependent variable v, we compute the distance between the two in–pair tasks as the absolute value of the difference, i.e. | v(t2) – v(t1) |. Ideally, this distance should be lower for the students in the control group (no information about partners’ genders) than for those in the experimental group (with two different perceived partners’ genders at t1 and t2). Therefore, for every variable, we perform a t–test to detect distance differences between the groups. Then, using the data from the experimental group only, we perform a t–test to detect differences in the scores of every dependent variable between perceived partner’s gender, i.e. to detect differences in the scores when partners are perceived as men vs. as women. Finally, to detect a potential interaction between the perceived partner’s gender and the subject’s gender, we perform a mixed–model ANOVA with the perceived gender as a within– subjects variable and subject’s gender as a between–subjects variable. As complementary analyses, we also study (i) the correlation between the induced and the perceived gender for the subjects in the experimental group, and the distribution of the perceived gender (if any) in the control group; and (ii) the potential cultural impact of the different locations at which the experiment is carried out. All the data analysis will be performed using R scripts, that will be available in a public repository together with the datasets in the corresponding laboratory package. Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). Authors: Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

How Twincode Uncovers Gender Bias in Remote Pair Programming

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

11 Proven Solutions to Common GitHub Copilot Problems

5 Innovations In Tech That Are Transforming Higher Education

Crucial Features for Customized Virtual Classroom Software

Four Tips For The Newly Minted Remote Student

How Big Data is Shaping Adaptive Learning

How Long Does it Take to Develop an Educational App?

11 Proven Solutions to Common GitHub Copilot Problems

5 Innovations In Tech That Are Transforming Higher Education

Crucial Features for Customized Virtual Classroom Software

Four Tips For The Newly Minted Remote Student

How Big Data is Shaping Adaptive Learning

How Long Does it Take to Develop an Educational App?

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps