Peer Reviewed

User agency and control serve as cornerstones of design in HCI, with numerous studies finding that choice improves user experiences. However, few studies examine how users benefit from the act of choosing, independent from the fulfillment of their chosen option; making this distinction is crucial for refining guidelines on when to provide user control. In our experiment on YouTube, participants randomly experienced either a pre-roll ad, a mid-roll ad, or a choice between the two. Participants then rated their subjective experiences. Mid-roll ads negatively affected experience ratings, but ratings between those choosing a pre-roll ad and those assigned a pre-roll ad were similar. That is, the right ad timing had a much larger impact than choosing an ad timing. The findings suggest that user interfaces should not offer choices solely for the sake of offering choices, and suggest scenarios where automation would be preferable to fine-grained user control.

Errors in AI grading and feedback often have an intractable set of causes and are, by their nature, difficult to completely avoid. Since inaccurate feedback potentially harms learning, there is a need for designs and workflows that mitigate these harms. To better understand the mechanisms by which erroneous AI feedback impacts students’ learning, we conducted surveys and interviews that recorded students’ interactions with a short-answer AI autograder for “Explain in Plain English” code reading problems. Using causal modeling, we inferred the learning impacts of wrong answers marked as right (false positives, FPs) and right answers marked as wrong (false negatives, FNs). We further explored explanations for the learning impacts, including errors influencing participants’ engagement with feedback and assessments of their answers’ correctness, and participants’ prior performance in the class.

Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation, we introduce CNAViz, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAViz allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel single-cell DNA sequencing data from the same samples, we show that by using CNAViz, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling.

Deployment of AI assessment tools in education is widespread, but work on students’ interactions and attitudes towards imperfect autograders is comparatively lacking. This paper presents students’ perceptions surrounding a ∼ 90% accurate automated short-answer grader that determined homework and exam credit in a college-level computer science course. Using surveys and interviews, we investigated students’ knowledge about the autograder and their attitudes.

SUMMARY: Transposon calling cards is a genomic assay for identifying transcription factor binding sites in both bulk and single cell experiments. Here, we describe the qBED format, an open, text-based standard for encoding and analyzing calling card data. In parallel, we introduce the qBED track on the WashU Epigenome Browser, a novel visualization that enables researchers to inspect calling card data in their genomic context. Finally, through examples, we demonstrate that qBED files can be used to visualize non-calling card datasets, such as Combined Annotation-Dependent Depletion scores and GWAS/eQTL hits, and thus may have broad utility to the genomics community. AVAILABILITY AND IMPLEMENTATION: The qBED track is available on the WashU Epigenome Browser (http://epigenomegateway.wustl.edu/browser), beginning with version 46. Source code for the WashU Epigenome Browser with qBED support is available on GitHub (http://github.com/arnavm/eg-react and http://github.com/lidaof/eg-react). A complete definition of the qBED format is available as part of the WashU Epigenome Browser documentation (https://eg.readthedocs.io/en/latest/tracks.html#qbed-track). We have also released a tutorial on how to upload qBED data to the browser (http://dx.doi.org/10.17504/protocols.io.bca8ishw).

Control settings are abundant and have significant effects on user experiences. One example of an impactful but understudied area is feed settings. In this study, we investigated awareness, navigation, and use of feed settings. We began by creating a taxonomy of feed settings on social media and search sites. Via an online survey, we measured awareness of Facebook feed settings. An in-person interview study then investigated how people navigated to and chose to set feed settings on their own feeds. We discovered that many participants did not believe ad personalization feed settings existed. Furthermore, we discovered a misalignment in the expectation and the function of settings, especially of ad personalization settings for many participants. Despite all participants struggling to find at least one setting, participants overall wanted to use settings: 94% altered at least one setting they encountered. From these results, we discuss implications and suggest design guidelines for settings.