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Despite evidence of its effectiveness, Peer Instruction (PI) has not been widely adopted by undergraduate computing instructors. In PI, an instructor displays a hard multiple-choice question that students answer individually, then discuss their answer with peers, then answer again, and finally an instructor leads a discussion of the question. Even though the benefits of PI are well documented, it can be difficult to convince computing instructors to move away from passive lectures. Major reasons why instructors do not adopt PI include a lack of awareness, lack of time, and concerns over their ability to cover content. We hypothesized that we could encourage the adoption of PI by creating Peer+, a free tool in an ebook platform, a searchable question bank, and running summer instructor workshops. We offered a three-day in-person summer workshop to a total of 37 instructors in 2022 and 2023. Instructors completed a pre-survey, immediate post-survey, and a follow-up post survey after the fall semester. We also conducted semi-structured interviews with 17 instructors. On the immediate post-survey most (33/37, 89%) instructors reported that they were very likely or likely to use the tool in the fall. However, on the follow-up survey, less than a quarter (6/26, 23%) actually did. The number one reason for not using the tool was a lack of time (18/26, 69%). Notably, all of the instructors who used Peer+ planned to use it again. This work informs efforts to increase the adoption of evidence-based pedagogical approaches in computing. 

Peer Instruction (PI) is a lecture-based active learning approach that has students solve a difficult multiple-choice question individually, submit their answer, discuss their answer with peers, and then submit their answer again. Despite plentiful evidence to support its effectiveness, PI has not been widely adopted by undergraduate computing instructors due to low awareness of PI, the effort needed to create PI questions, the limited instructional time needed for PI activities during lectures, and potential adverse reactions from students. We hypothesized that we could allay some of these concerns by hosting a three-day summer workshop on Peer Instruction for instructors and building and sharing a free tool and a question bank that supports PI in an open-source ebook platform. We invited eighteen instructors to attend an in-person three-day workshop on PI in the summer of 2022. We collected their feedback by using pre and post surveys and conducting semi-structured interviews. We report on the effect of the three-day summer workshop on instructor attitudes towards and knowledge of PI, the barriers that prevented instructors from adopting the free tool, and feedback from instructors who used the tool. The results show that most workshop attendees reported that they planned to use the tool in the fall semester, but less than half actually did. Responses from both users and non-users yield insights about the support instructors need to adopt new tools. This research informs future professional development workshops, tool development, and how to better support instructors interested in adopting Peer Instruction. 

This working group aims to identify available datasets within the context of computing education research. One particular area of interest is programming education, and the data in question may include students' steps, progress, or submissions in the form of program code. To achieve this goal, the working group will review well-known data resources and repositories (e.g., DataShop, GitHub, NSF Public Access Repository, and IEEE DataPort) and recent papers published within the SIGCSE community. As a result of the review process, the working group will create an overview of available datasets and characterize them while reflecting on current data practices, challenges, and the consequences of limited access to research data. Additionally, the group intends to propose a path for the community to become more open and move toward open data practices. This proposal highlights the importance of sharing research data within the computing education research community to make it stronger and more productive.