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Among ongoing eforts to broaden participation in K–12 computer science (CS) education, the Advanced Placement (AP) Computer Science Principles (CSP) course receives a lot of attention. While prior research has shown increased participation among some his- torically underrepresented groups, little is known about how the course serves students with disabilities. This study examines participation patterns of students with dis- abilities in CSP courses across 230 public schools in Pennsylvania during the 2022–2023 academic year. Drawing on statewide enroll- ment data from 306 CSP courses, we conducted a series of statistical analyses to investigate relationships between student participation and school-level capacity factors identifed by the Capacity, Ac- cess, Participation, and Experience (CAPE) framework, including teacher experience, school funding, and locale. Findings show that many factors have a small, but statistically signifcant infuence. However, CSP courses labeled as AP were associated with signif- cantly lower participation rates among students with disabilities, compared to CSP courses without the designation. These fndings suggest that course labeling and underlying assumptions about aca- demic rigor may unintentionally limit opportunities for students with disabilities. 

Parsons problems have become a mainstay of computer science education. They are heavily used among students, especially in K-12 and provide a small puzzle-like experience for students to practice their skills. Today, while prior work has explored com- plex issues with accessibility and block languages in general, the 2024 changes to accessibility regulations by the U.S. Department of Justice includes new rules around mobile platforms. These rules are ill-defned and in need of evaluation. In this work, we make several contributions. First, we conducted an evaluation of existing blocks with respect to their regulatory compliance and discuss a new blocks technology that we developed that meets these new mobile guidelines. Second, we conducted three empirical studies using Parsons problems to evaluate the usability of the technology with teachers of the visually impaired (n = 32), high-school students with diverse disabilities (n = 28), and high-school students with blindness or low vision (n = 13). 

Overwash is the cross‐shore transport of water and sediment from a waterbody over the crest of a sand or gravel barrier beach, and washover is the resulting sedimentary deposit. Washover volume, and alongshore patterns of washover distribution, are fundamental components of sediment budgets for low‐lying coastal barrier systems. Accurate sediment budgets are essential to forecasting barrier system sustainability under future climate‐driven forcing. However, comprehensive surveys of three‐dimensional washover morphology are challenging to deliver. Here, we use the results of a physical experiment, analysis of lidar data, and examples of washover characteristics reported in the literature to develop scaling relationships for washover morphometry that demonstrate volume can be reasonably inferred from planform measurements, for washover in natural (non‐built) and built barrier settings. Gaining three‐dimensional insight into washover deposits from two‐dimensional information unlocks the ability to analyze past aerial imagery and estimate contributions from washover flux to sediment budgets for past storms. 

Abstract Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data‐driven models are only as good as the data used for training, and this points to the importance of high‐quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time‐consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.