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 1.2 Related Work Several literature reviews [10, 15, 21] have compiled the empirical research on using pair programming in higher education, with [6] being focused on distributed pair programming from a teaching perspective. By means of controlled experiments, remote and co–located pair programming are compared in [1, 22], showing comparable results. In most of the cases, the analyzed variables are related to performance in terms of time, quality, or code tests passed. Students perceptions have been also analyzed in terms of confidence, satisfaction, motivation, or personality [20]. Table 1 summarizes the empirical studies on the influence of gender in pair programming, including findings such as (i) same– gender pairs are more “democratic”; (ii) women working in pairs were more confident than those working solo; and (iii) in mixedgender, pairing women particularly do not benefit [15]. Although such studies reveal that gender seems to be a key factor in pair programming, none of them study gender bias in pair programming. Many factors other than gender may affect the outcomes of remote programming sessions [2, 23]. Previous research on productive pairing looked at factors such as skill levels, autonomy in choosing one’s partner [25], and different personalities [11]. Nevertheless, the work on gender composition of pairs found conflicting results about whether same-gender or mixed-gender pairings are more effective [3, 4, 12, 16]. One possible explanation is that gender correlates with other dimensions that may affect the pairs’ collaboration, but these correlations may vary between different environments. For example, women in a class may, on average, have higher skill level than men because they had to face more societal barriers to enter the class. On the other hand, they may, on average, have lower skill level if women with no background are more actively recruited. 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 1.2 Related Work Several literature reviews [10, 15, 21] have compiled the empirical research on using pair programming in higher education, with [6] being focused on distributed pair programming from a teaching perspective. By means of controlled experiments, remote and co–located pair programming are compared in [1, 22], showing comparable results. In most of the cases, the analyzed variables are related to performance in terms of time, quality, or code tests passed. Students perceptions have been also analyzed in terms of confidence, satisfaction, motivation, or personality [20]. Table 1 summarizes the empirical studies on the influence of gender in pair programming, including findings such as (i) same– gender pairs are more “democratic”; (ii) women working in pairs were more confident than those working solo; and (iii) in mixedgender, pairing women particularly do not benefit [15]. Although such studies reveal that gender seems to be a key factor in pair programming, none of them study gender bias in pair programming. Many factors other than gender may affect the outcomes of remote programming sessions [2, 23]. Previous research on productive pairing looked at factors such as skill levels, autonomy in choosing one’s partner [25], and different personalities [11]. Nevertheless, the work on gender composition of pairs found conflicting results about whether same-gender or mixed-gender pairings are more effective [3, 4, 12, 16]. One possible explanation is that gender correlates with other dimensions that may affect the pairs’ collaboration, but these correlations may vary between different environments. For example, women in a class may, on average, have higher skill level than men because they had to face more societal barriers to enter the class. On the other hand, they may, on average, have lower skill level if women with no background are more actively recruited. 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

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