We propose a new exercise format for an upper-division software engineering course. The exercises scaffold the gap between the "box and arrows" representation of a complex software architecture and the nuts-and-bolts realization of that architecture in code. A qualitative study with 12 upper-division computer science students explored the effectiveness of these exercises as a teaching tool and opportunities to improve them. All participants desire for these new exercises to be incorporated into their course, self-rating the exercises as educational and confidence-boosting, suggesting these new types of exercises will pave the way for more confident and competent software engineers.

Abstract Context Women have historically been underrepresented in Software Engineering, due in part to the stereotyped assumption that women are less technically competent than men. Pair programming is both widely used in industry and has been shown to increase student interest in Software Engineering, particularly among women; but if those same gender biases are also present in pair programming, its potential for attracting women to the field could be thwarted. Objective We aim to explore the effects of gender bias in pair programming. Specifically, in a remote setting in which students cannot directly observe the gender of their peers, we study whether the perception of the partner, the behavior during programming, or the style of communication of Software Engineering students differ depending on the perceived gender of their remote partner. To our knowledge, this is the first study specifically focusing on the impact of gender stereotypes and bias within pairs in pair programming. Method We have developed an online pair-programming platform () that provides a collaborative editing window and a chat pane, both of which are heavily instrumented. Students in the control group had no information about their partner’s gender, whereas students in the treatment group could see a gendered avatar representing the other participant as a man or as a woman. The gender of the avatar was swapped between programming tasks to analyze 45 variables related to the collaborative coding behavior, chat utterances, and questionnaire responses of 46 pairs in the original study at the University of Seville, and 23 pairs in the external replication at the University of California, Berkeley. Results We did not observe any statistically significant effect of the gender bias treatment, nor any interaction between the perceived partner’s gender and subject’s gender, in any of the 45 response variables measured in the original study. In the external replication, we observed statistically significant effects with moderate to large sizes in four dependent variables within the experimental group, comparing how subjects acted when their partners were represented as a man or a woman. Conclusions The results in the original study do not show any clear effect of the treatment in remote pair programming among current Software Engineering students. In the external replication, it seems that students delete more source code characters when they have a woman partner, and communicate using more informal utterances, reflections and yes/no questions when they have a man partner, although these results must be considered inconclusive because of the small number of subjects in the replication, and because when multiple test corrections are applied, only the result about informal utterances remains significant. In any case, more mixed methods replications are needed in order to confirm or refute the results in the same and other Software Engineering students populations.

Computational notebooks, which seamlessly interleave code with results, have become a popular tool for data scientists due to the iterative nature of exploratory tasks. However, notebooks provide a single execution state for users to manipulate through creating and manipulating variables. When exploring alternatives, data scientists must carefully create many-step manipulations in visually distant cells.

Learning to recognize and apply programming patterns — reusable abstractions of code — is critical to becoming a proficient computer scientist. However, many introductory Computer Science courses do not teach patterns, in part because teaching these concepts requires significant curriculum changes. As an alternative, we explore how a novel user interface for practicing coding — Faded Parsons Problems — can support introductory Computer Science students in learning to apply programming patterns. We ran a classroom-based study with 237 students which found that Faded Parsons Problems, or rearranging and completing partially blank lines of code into a valid program, are an effective exercise interface for teaching programming patterns, significantly surpassing the performance of the more standard approaches of code writing and code tracing exercises. Faded Parsons Problems also improve overall code writing ability at a comparable level to code writing exercises, but are preferred by students.

poster Share on Exploring the Impact of Gender Bias on Pair Programming Authors: Aslihan Akalin UC Berkeley, USA UC Berkeley, USAView Profile , Nathaniel Weinman UC Berkeley, USA UC Berkeley, USAView Profile , Katherine Stasaski UC Berkeley, USA UC Berkeley, USAView Profile , Armando Fox UC Berkeley, USA UC Berkeley, USAView Profile Authors Info & Claims ICER 2021: Proceedings of the 17th ACM Conference on International Computing Education ResearchAugust 2021 Pages 435–437https://doi.org/10.1145/3446871.3469790Online:17 August 2021Publication History 0citation80DownloadsMetricsTotal Citations0Total Downloads80Last 12 Months80Last 6 weeks16 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Introductory programming classes teach students to program using worked examples, code tracing, and code writing exercises. Parsons Problems are an educational innovation in which students unscramble provided lines of code, as a step towards bridging the gap between reading and writing code. Though Parsons Problems have been found effective, there is some evidence that students can use syntactic heuristics to help them solve these problems without fully understanding the solution.. To address this limitation, we introduce Faded Parsons Problems, a variation of Parsons Problems where parts of the provided code are incomplete. We explore a specific instantiation of this idea, Blank-Variable Parsons Problems, in which all variable names are blanked out. Unlike another Parsons Problem variation - adding distractor code lines - Blank-Variable Parsons can be automatically created from a solution without additional effort from an instructor. A 75 minute pilot study with CS1 students indicates that solving standard Parsons Problems does not lead to short-term near-transfer in code writing, suggesting a need for problems with less scaffolding. Additionally, students self-report Blank-Variable Parsons as fitting in difficulty between Parsons Problems and code writing, suggesting Blank-Variable Parsons may be one opportunity to fill this gap.

Parsons Problems, in which students unscramble provided lines of code, are an exciting innovation for teaching introductory Computer Science. Prior work has shown that students can learn more efficiently by solving Parsons Problems compared to writing code and can learn more effectively by solving Parsons Problems compared to worked examples. Though Parsons Problems have been found effective, there is some evidence that students can use syntactic heuristics to help them solve these problems without full understanding, confirmed to be in use by mature CS1 students in our pilot study. To address this limitation, we propose Faded Parsons Problems, a variation of Parsons Problems where parts of the provided code are incomplete. We built a Flask app that supports Python programming exercises - traditional code writing, standard Parsons Problems, and Faded Parsons Problems - as well as survey and simple comprehension questions (multiple choice and short answer questions). The system can log anonymized data from participants, enforce time limits, and randomize treatment selection. Exercises support an autograder, implemented as a separate worker using RQ that safely executes arbitrary code. Students can run pre-configured tests as frequently as they want to receive detailed output from the test cases: function arguments, expected output, actual output, print statement output, raised exceptions, and custom messages configured by the instructor. Exercise descriptions, provided code lines (including blanks), initial code, and test cases may be configured from YAML files. The system has been used successfully with 50 simultaneous users. A demo can be viewed at https://codestyle.herokuapp.com/sigcse-demo