It is essential for CS students to graduate with competence about ethics and societal impacts of technology. We designed and taught a new reading discussion course, at Grinnell College, Algorithms, Ethics, and Society, for advanced undergraduate students who have completed CS1 and CS2. Course topics included Identity in Computing, Tech Ethics, Algorithms Informing Policies, Large Language Models, Networks and Social Media, Health Applications, and Robotics. We encountered some challenges with the discussion format, which we addressed by upholding class norms, employing discussion techniques learned from humanities and social science colleagues, and being open to learn from our mistakes.

Motivation. All students studying Computer Science (CS) deserve to feel a sense of belonging. In a post-secondary CS class, undergraduate Teaching Assistants (TAs) have the majority of student contact hours, making student-TA interactions, such as those during office hours, important in shaping student belonging. Therefore, we sought to understand student and TA conceptions of belonging, their narratives about their journeys of belonging in CS, and how TAs influence student sense of belonging through office hour interactions.

Motivated by the need to develop equitable and just computer science education, we implemented a resubmission policy. We ran a large-scale, end-of-term survey of all students across two large introductory CS courses asking about their reasons for resubmission. Though some students were motivated primarily by grades, many responses suggested intrinsic motivation. We interviewed 9 students and found that in our competitive program, resubmissions take the pressure off the need to submit work that earns a perfect grade the first time. However, our findings suggest that resubmissions alone can't create space for equity and belonging.

A digital calculator is one of the most frequently used touch screen applications. However, keypad-based character input in existing calculator applications requires precise, targeted key presses that are time-consuming and error-prone for many screen readers users. We introduce GestureCalc, a digital calculator that uses target-free gestures for arithmetic tasks. It allows eyes-free target-less input of digits and operations through taps and directional swipes with one to three fingers, guided by minimal audio feedback. We conducted a mixed methods longitudinal study with eight screen reader users and found that they entered characters with GestureCalc 40.5% faster on average than with a typical touch screen calculator. Participants made more mistakes but also corrected more errors with GestureCalc, resulting in 52.2% fewer erroneous calculations than the baseline. Over the three sessions in the study, participants were able to learn the GestureCalc gestures and efficiently perform short calculations. From our interviews after the second session, participants recognized the effort in learning a new gesture set, yet reported confidence in their ability to become fluent in practice.

Keypad-based character input in existing digital calculator applications on touch screen devices requires precise, targeted key presses that are time-consuming and error-prone for many screen reader users. We demonstrate GestureCalc, a digital calculator that uses target-free gestures for arithmetic tasks. It allows eyes-free target-less input of digits and operations through taps and directional swipes with one to three fingers, guided by minimal audio feedback. A study of the effectiveness of GestureCalc for screen reader users appears in a full paper by the authors at this conference.

The ability to reference objects in the environment is a key communication skill that robots need for complex, task-oriented human-robot collaborations. In this paper we explore the use of projections, which are a powerful communication channel for robot-to-human information transfer as they allow for situated, instantaneous, and parallelized visual referencing. We focus on the question of what makes a good projection for referencing a target object. To that end, we mathematically formulatelegibility of projections intended to reference an object, and propose alternative arrow-object match functions for optimally computing the placement of an arrow to indicate a target object in a cluttered scene. We implement our approach on a PR2 robot with a head-mounted projector. Through an online (48 participants) and an in-person (12 participants) user study we validate the effectiveness of our approach, identify the types of scenes where projections may fail, and characterize the differences between alternative match functions.

Operating robots across networks with unknown, bandwidth, latency and other conditions presents difficulty when the operation depends on real-time feedback and control. Standard video compression methods do a good job compressing arbitrary video, but do not take domain knowledge into account when more information about the video is known beforehand. We have incorporated robot odometry into the video pipeline, allowing video quality to be selectively reduced at times when odometry suggests that such a reduction will not adversely affect task performance of human operators. We found that selectively reducing video quality significantly reduced bandwidth usage, increasing the robot's responsiveness and controllability, while having no measurable effect on task performance.