Navigating the hiring process and workplace in computing can require a combination of professional and technical competencies. What is less clear are the approaches educators may take to cultivate students' development, prepare for technical interviews, and how the educational context might influence students' needs and learning experiences. In this paper, we focused on the perspectives of computing faculty in the context of supporting learners at Hispanic-Serving Institutions (HSIs) in the southeastern region of the United States. Data collection included semi-structured interviews with n = 20 participants, and we applied reflexive thematic analysis to examine the resultant transcripts. The findings suggested that critical thinking and decomposing problems could become even more relevant in light of the evolving landscape of generative artificial intelligence and its ability to create code. Faculty highlighted the value of taking more social approaches to computing instruction, such as enlisting pair programming and encouraging students to ideate on solutions with groups. Additionally, written and spoken language, as well as how they may pertain to the expression of technical concepts, were seen as important for students' long-term success in the field. For students whose first language was Spanish (or a language other than English), instructors suggested incorporating oral presentations and providing constructive feedback on how students expressed coding solutions. The outcomes from this work can serve to offer insight not only for faculty at HSIs but also for educators seeking to consider new ways to support students.

This innovative practice WIP paper describes an initiative that seeks to promote faculty awareness and interventions towards student-based technical interview preparations. In the field of Computing, there is a trend for having a high demand of jobs in industry and a low supply of candidates to fill them. Literature indicates notable challenges that could pose as influential factors for this job/candidate ratio discrepancy in the field: 1) student retention challenges in computing areas at the collegiate level; 2) fostering stronger K-12 student interests and engagement towards computing fields as a career choice; and 3) representation barriers that exist within marginalized groups in computing. Yet, computing majors' inability to showcase their developed computational skills as prospective candidates to tech employers and hiring managers can persist as another barrier. Candidates are typically expected to demonstrate their computational skills and abilities practically. A candidate's inability to demonstrate such skills and abilities effectively could lead to missed job opportunities and even lucrative career pursuits in tech. One notable approach used by tech industry companies to examine the skillset compositions of their candidates is through technical interviews. This interview-based examination allows a hiring manager(s) to observe a candidate's ability to solve problems computationally in a think-aloud format, which also enables these professionals to gauge the candidate's verbal and communication skills during this interaction. When cross referencing the practice of technical interview examination in industry with the styles of learning and application fostered in computing classrooms in the academy, there exists a potential contrast between this expectation imposed by tech industry companies and professional preparation protocols fostered at academic institutions. There are computing-focused departments/schools and institutions at large that have identified the need to educate and prepare their computing majors for technical interviews. Yet, there still exists a greater disparity for institutional awareness and effective preparation when observing academia altogether. Such a disparity presents an opportunity to promote the importance and need for effective technical interview preparation strategies and enhanced awareness in the academy on a larger scale. In partnership with the STARS Computing Corps and KARAT, the authors of this manuscript created a faculty-targeted workshop in such efforts to increase academic awareness to the technical interview process. Faculty from a variety of institutions across the U.S. are participating in this workshop with the sole purpose of: 1) gaining first-hand exposure to the technical interview process, and 2) developing initiatives that can be implemented back at their home institutions to better prepare their students for the technical interview process. Pre and post surveys have been administered to the participating faculty to gather feedback about their experiences while matriculating through this workshop. Notable outcomes indicate that the participating faculty value this workshop and its intended impact to promote effective technical interview preparation practices. Likewise, these faculty are developing and adopting applicable initiatives to be employed back at their home institutions to better prepare their students for technical interviews. Overall, these faculty feel more equipped with preparing their students to successfully conduct technical interviews.

The purpose of our research is to direct more attention to two relationship privacy boundary strategies: connection avoidance (i.e., rejecting friend/follower requests) and connection termination (i.e., removing existing friends/followers). A survey study was conducted with 222 college students that examined how participants regulated these boundaries with others versus how they perceived others who regulated these boundaries with them ("self" vs. "other") on Facebook, Twitter, and Instagram. Participants reported using relationship avoidance and termination strategies more than they perceived others using these strategies against them. Overall, there were minimal impacts reported in terms of relationship changes due to others avoiding and terminating relationships. Site affordances partially explain these results, as none of the sites currently notify users when a friend request is denied or an existing friendship is severed.

Homeland safety and security will increasingly depend upon autonomous unmanned vehicles as a method of assessing and maintaining situational awareness. As autonomous team algorithms evolve toward requiring less human intervention, it may be that having an “operator-in-the-loop” becomes the ultimate goal in utilizing autonomous teams for surveillance. However studies have shown that trust plays a factor in how effectively an operator can work with autonomous teammates. In this work, we study mechanisms that look at autonomy as a system and not as the sum of individual actions. First, we conjecture that if the operator understands how the team autonomy is designed that the user would better trust that the system will contribute to the overall goal. Second, we focus on algorithm input criteria as being linked to operator perception and trust. We focus on adding a time-varying spatial projection of areas in the ROI that have been unseen for more than a set duration (STEC). Studies utilize a custom test bed that allows users to interact with a surveillance team to find a target in the region of interest. Results show that while algorithm training had an adverse effect, projecting salient team/surveillance state had a statistically significant impact on trust and did not negatively affect workload or performance. This result may point at a mechanism for improving trust through visualizing states as used in the autonomous algorithm.

Improving the novice's experience with programming may be key to retaining more students interested in Computer Science. Programming environments is an area that lacks comprehensive study. Some entry-level sequences prefer visual environments while others tout the benefits of command line environments. In either case, there is little more than anecdotal evidence to support either choice. We present a novel categorization of programming tools based on assistive features that predict the usefulness of programming environments for novices. This categorization is supported by a controlled study that measured the differences in engagement, efficiency, comprehension, and usability. We found that the extremes of assistive features are problematic regardless of prior programming experience.