The widespread movement to integrate computational thinking (CT) and computer science (CS) into K-12 education has led to rapid expansion. However, this growth is challenged by the reality that many in-service teachers asked to teach CS/CT are novices with little formal preparation. Professional development is critical to bridging this gap, yet many programs struggle to connect with teachers' diverse experiences and move beyond theory to classroom practice. This paper presents an experience report from a multi-year one-week summer course serving over 100 middle and high school teachers, the majority of whom were novices in CT. Our work contributes a novel pedagogical model for CT professional development grounded in the principles of Culturally Relevant Pedagogy (CRP). It is structured around two key components within each session: a ''Teachers as Learners'' mini-lecture that connects CT concepts to teachers' own lived experiences, followed immediately by a ''Teachers as Teachers'' hands-on exercise where they adapt K-12 CT activities for their own students. We call it the ''Dual CRP Model''. Pre- and post-course survey data consistently show that teachers enter with low confidence and leave feeling highly prepared to teach CT. Qualitative findings and an analysis of course artifacts reveal that the model's success stems from its use of culturally relevant analogies to make concepts accessible and its bridge from theory to classroom practice, which are key factors in building both conceptual understanding and pedagogical confidence.

Given the potential for technology to inflict harm and injustice on society, it is imperative that we cultivate a sense of social responsibility among our students as they progress through the Computer Science (CS) curriculum. Our students need to be able to examine the social complexities in which technology development and use are situated. Also, aligning students' personal goals and their ability to achieve them in their field of study is important for promoting motivation and a sense of belonging. Promoting communal goals while learning computing can help broaden participation, particularly among groups who have been historically marginalized in computing. Keeping these considerations in mind, we piloted an introductory Java programming course in which activities engaging students in ethical and socially responsible considerations were integrated across modules. Rather than adding social on top of the technical content, our curricular approach seeks to weave them together. The data from the class suggests that the students found the inclusion of the social context in the technical assignments to be more motivating and expressed greater agency in realizing social change. We share our approach to designing this new introductory socially responsible computing course and the students' reflections. We also highlight seven considerations for educators seeking to incorporate socially responsible computing.

The relentless pace of video production exacerbates the digital accessibility gap that individuals who are blind or low vision (BLV) face on a daily basis, resulting in disproportionate exclusion from community opportunities and risk management. Whereas previous automated audio description (AD) systems provide single-tool approaches for delivering minimum viable description (MVD) or delivering on-demand visual question answering (VQA), we present a tandem AI-based AD tool that combines MVD and on-demand VQA. A user study with 26 BLV individuals explored how the tandem system may be used under the conditions of delivering MVD and/or on-demand VQA with AI-only or human-in-the-loop support. When each tool was used in isolation, AI-only conditions scored significantly lower in both user enjoyment and comprehension. When used in tandem, AI-only conditions matched outcomes delivered with human-in-the-loop, which suggests that AI-only AD tools may be most effective when both types of tools are used in tandem. A multimodal analysis of interactions with the tandem system revealed areas for system improvement in terms of the timing of AD delivery and accurate content delivery. We discuss how the use of both types of tools in a tandem system can mitigate some of the digital frictions that have plagued efforts in machine learning and automated tools for accessibility.

We present a new high-quality validated image caption rating (VICR) dataset. How well a caption fits an image can be difficult to assess due to the subjective nature of caption quality. How do we evaluate whether a caption is good? We generated a new dataset to help answer this question by using our new image caption rating system, which consists of a novel robust rating scale and gamified approach to gathering human ratings. We show that our approach is consistent and teachable. 113 participants were involved in generating the dataset, which is composed of 68,217 ratings among 15,646 image-caption pairs. Our new dataset has greater inter-rater agreement than the state of the art, and custom machine learning rating predictors that were trained on our dataset outperform previous metrics. We improve over Flickr8k-Expert in Kendall's $W$ by 12\% and in Fleiss' $\kappa$ by 19\%, and thus provide a new benchmark dataset for image caption rating.

Science students increasingly need programming and data science skills to be competitive in the modern workforce. However, at our university (San Francisco State University), until recently, almost no biology, biochemistry, and chemistry students (from here bio/chem students) completed a minor in computer science. To change this, a new minor in computing applications, which is informally known as the Promoting Inclusivity in Computing (PINC) minor, was established in 2016. Here, we present the lessons we learned from our experience in a set of 10 rules. The first 3 rules focus on setting up the program so that it interests students in biology, chemistry, and biochemistry. Rules 4 through 8 focus on how the classes of the program are taught to make them interesting for our students and to provide the students with the support they need. The last 2 rules are about what happens "behind the scenes" of running a program with many people from several departments involved.

This paper presents Koob, an eBook Reader app that coalesces three key ideas to enhance students’ language learning, specifically for ambiguous words. The first idea is to improve the effectiveness of word lookup functionality through contextualization – by incorporating word sense disambiguation (WSD) techniques to show the contextually relevant definition at the top. The second idea is to augment WSD results with crowd-sourcing solutions. The last idea seeks to reinforce students’ learning by augmenting textual information with a visual aid, pictures related to the word, as part of the word lookup functionality. An empirical evaluation demonstrates that existing WSD techniques can successfully employed to dynamically reorder definitions such that the most relevant definition is at the top of the list for more than 80% of the instances.

This qualitative analysis explores the development of Computer Science (CS) identity of university students majoring in CS and students participating in a novel CS minor geared toward students who have traditionally been underrepresented in the field. We examine student perceptions of their CS identity at two critical junctures: pre-CS exposure (initial interest in CS), and during early CS exposure (performance and competence). Findings demonstrate the different paths to CS identity that the groups take, and highlight the importance of the CS educational environment in efforts to diversify the field.

Video accessibility is crucial for blind and visually impaired individuals for education, employment, and entertainment purposes. However, professional video descriptions are costly and time-consuming. Volunteer-created video descriptions could be a promising alternative, however, they can vary in quality and can be intimidating for novice describers. We developed a Human-in-the-Loop Machine Learning (HILML) approach to video description by automating video text generation and scene segmentation and allowing humans to edit the output. The HILML approach facilitates human-machine collaboration to produce high quality video descriptions while keeping a low barrier to entry for volunteer describers. Our HILML system was significantly faster and easier to use for first-time video describers compared to a human-only control condition with no machine learning assistance. The quality of the video descriptions and understanding of the topic created by the HILML system compared to the human-only condition were rated as being significantly higher by blind and visually impaired users.

Underrepresentation of women and Black and Latinx individuals in computer science (CS) is a well-documented issue facing university training programs and the field in general. In an effort to expand on current knowledge and help bridge the equality gap in CS, a pilot interdisciplinary Computing Applications minor program was started in 2016 at San Francisco State University (SFSU). The "Promoting INclusivity in Computing" (PINC) program was designed to improve diversity in computing and increase computing literacy in data-intensive fields, specifically biology and chemistry. Students participating in the PINC program were compared to lower division CS majors at SFSU on measures assessing their attitudes toward computer science, goal-setting tendencies, experience of stereotype threat, general self-efficacy, and computer science self-efficacy. Analysis showed that students in the PINC program reported lower levels of computer science self-efficacy, but there were no significant differences between groups on self-report measures of goal-setting tendencies, experience of stereotype threat, attitudes toward computer science, and general self-efficacy. These findings highlight the success of the minor program in creating an educational environment that supports the achievement of underrepresented (UR) students, as well as the similar psychoeducational traits of the two groups of students. Findings may be of particular interest to postsecondary CS teachers, researchers interested in social justice and representation issues as they pertain to the field of computing, and university and departmental administrators who wish to increase and promote diversity in their CS programs.

In this paper we present a pilot program at San Francisco State University, Promoting INclusivity in Computing (PINC), that is designed to achieve two goals simultaneously: (i) improving diversity in computing, and (ii) increasing computing literacy in data-intensive fields. To achieve these goals, the PINC program enrolls undergraduate students from non Computer Science (non-CS) fields, such as, Biology, that have become increasingly data-driven, and that traditionally attract diverse student population. PINC incorporates several well-established pedagogical practices, such as, cohort-based program structure, near-peer mentoring, and project-driven learning, to attract, retain, and successfully graduate a highly diverse and interdisciplinary student body. On successful completion of the program, students are awarded a minor in Computing Applications. Since its inception 18 months ago, 60 students have participated in this program. Of these 73% are women, and 51% are underrepresented minorities (URM). 74% of the participating students had nominal or no exposure to computer programming before PINC. Findings from student surveys show that majority of the PINC students now feel less intimidated about computer programming, and vividly see its utility and necessity. For several students, participation in the PINC program has already opened up career pathways (industry and academic summer internships) that were not available to them before.

We present an instructional design of computer science project-based course to transform students’ experiences of acquiring software development skills. In a collaborative classroom emulating a typical industry work setting, students will collectively create and build a Multiplayer Online Game using a variety of complex software components. A course was taught to design and develop a working Multiplayer Online Game within one semester: building a ready-to-usable game in one semester with whole classmates presents significant challenges to cope with and stimulate students to realize the important aspects of teamwork and software engineering principles. Students present their progress, discuss future milestones and trouble shoots, update documents for clearer communication and utilize source control tool throughout the semester. Unlike usual class setting, all students worked collaboratively together like one company to achieve the goal. In the class, students started from concept design and developed specific components of working Multiplayer Online Game, while broadly learning game design, 3D graphics, Game Engine, Server-client architecture, Game Protocol, network programming, database, Software Engineering principles, and large application development as a team project. The course was successfully transferred to CSULA in Fall Quarter, 2013.

Understanding ecological complexity has stymied scientists for decades. Recent elucidation of the famously coined "devious strategies for stability in enduring natural systems" has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically modeled and understood. Here, we describe the first extension of this field to include coupled human-natural systems. This extension elucidates new strategies for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and can pursue multiple goals such as maximizing economic profit, employment and carbon sequestration by ecosystems. Our more realistic modeling of ecosystems helps explain why simpler "maximum sustainable yield" bioeconomic models underpinning much natural resource extraction policy leads to less profit, biomass, and biodiversity than predicted by those simple models. Current research directions of this integrated natural and social science include applying artificial intelligence, cloud computing, and multiplayer online games.

We present a novel approach for latency-tolerant delivery of visualization and rendering results where client-side frame rate display performance is independent of source dataset size, image size, visualization technique or rendering complexity. Our approach delivers pre-rendered, multiresolution images to a remote user as they navigate through different viewpoints, visualization or rendering parameters. We employ demand-driven tiled, multiresolution image streaming and prefetching to efficiently utilize available bandwidth while providing the maximum resolution user can perceive from a given viewpoint. Since image data is the only input to our system, our approach is generally applicable to all visualization and graphics rendering applications capable of generating image files in an ordered fashion. In our implementation, a normal web server provides on-demand images to a remote custom client application, which uses client-pull to obtain and cache only those images required to fulfill the interaction needs. The main contributions of this work are: (1) an architecture for latency-tolerant, remote delivery of precomputed imagery suitable for use with any visualization or rendering application capable of producing images in an ordered fashion; (2) a performance study showing the impact of diverse network environments and different tunable system parameters on end-to-end system performance in terms of deliverable frames per second.

Recent advances in Internet and computer graphics stimulate intensive use and development of 3D graphics on the World Wide Web. To increase efficiency of systems using 3D graphics on the web, the presented method utilizes previously rendered and transmitted images to accelerate the rendering and compression of new synthetic scene images. The algorithm employs ray casting and epipolar constraints to exploit spatial and temporal coherence between the current and previously rendered images. The reprojection of color and visibility data accelerates the computation of new images. The rendering method intrinsically computes a residual image, based on a user specified error tolerance that balances image quality against computation time and bandwidth. Encoding and decoding uses the same algorithm, so the transmitted residual image consists only of significant data without addresses or offsets. We measure rendering speed‐ups of four to seven without visible degradation. Compression ratios per frame are a factor of two to ten better than MPEG2 in our test cases. There is no transmission of 3D scene data to delay the first image. The efficiency of the server and client generally increases with scene complexity or data size since the rendering time is predominantly a function of image size. This approach is attractive for remote rendering applications such as web‐based scientific visualization where a client system may be a relatively low‐performance machine and limited network bandwidth makes transmission of large 3D data impractical.