This study investigates the error resolution strate-gies employed by novice non-native English-speaking students in programming tasks. A controlled experiment was conducted with think-aloud sessions to solve code fragments containing syntax and logic errors, allowing participants to use any resources and tools but AI. Observations and notes were taken on each student's actions and answers to questions from two accompanying researchers. Four distinct strategies emerged: Trial-and-Error, Debugging, Online Searching, and Online Translation. Our findings suggest that students rely mainly on trial-and-error while missing opportunities to apply other strategies or poorly executing them.

The introduction of block-based coding provided teachers a new method to teach computer science to beginners. Since then, numerous programmable learning technologies (PLTs) utilizing this coding modality have surfaced, each bringing their unique properties to the CS education playing field. But while research comparing blocks to text-based coding have identified benefits and drawbacks of each, the same has not been done solely among block-based coding PLTs, specifically regarding their affordances to teachers' learning objectives and instructional needs. In this poster presentation, we present an experiment seeking to understand which properties of block-based coding environments teachers find most attractive to their pedagogical needs. Teachers enrolled in an online graduate-level CS pedagogy course reviewed 6 block-based coding PLTs, reflecting on the extent to which they would use the tools in their classes. We explain their assignment, guidelines given to scaffold their reviews, and our plan for future analysis.

Artistic computing learning environments have been of central importance in the exploration of how to support equity and inclusion in computing. Explorations within e-textiles, music, and interactive media, for example, have created diverse opportunities for learning how to program while creating culturally relevant artifacts. However, there is a gap in our understanding of the design processes of learners in these constructionist environments, including how the computational artifacts and their components impact the learning processes and the ways they build meaning and agency with computing. We advocate for research to attend more closely to the materiality of the computational materials to understand how they impact the social and cultural dimensions in which students are learning. In this paper, we present an analysis of 6 high school learners’ experiences within a co-designed arts and computing curriculum. Our analyses highlight how the materiality of the components impacted the ways in which learners developed personal and epistemological connections to computing based on how it enabled them to connect to their interests, represent their ideas, engage with their community, and overcome or navigate around challenges to get to their final designs. We demonstrate how centralizing the materiality in the design of computational construction kits can inform how we support agency and engagement with computing.

This panel brings together personnel from the Georgia Institute of Technology and the National Center for Women & Information Technology to discuss challenges, necessary logistics, and practical advice for creating a high school-to-college pipeline for recruiting women and gender nonconforming students into computing. Framed by our experiences developing BridgeUP STEM---an ongoing, two-year program comprising coursework, tiered mentoring, research internships, and community events---panelists will discuss important lessons for designing bridge programs, including what worked well and what did not for project planning, recruiting participants, structuring activities, and negotiating flexible formats to accommodate students' needs and unexpected challenges. Panelists represent key perspectives including project leaders, a logistics coordinator, a coding instructor, a Faculty Mentor, an undergraduate student participant, and a program evaluator. This panel will provide attendees with guidance for replicating similar programs at their own institutions to better foster recruitment and retention pipelines for attracting a greater diversity of students in computing.

As robots become more prevalent, the importance of the field of human-robot interaction (HRI) grows accordingly. As such, we should endeavor to employ the best statistical practices in HRI research. Likert scales are commonly used metrics in HRI to measure perceptions and attitudes. Due to misinformation or honest mistakes, many HRI researchers do not adopt best practices when analyzing Likert data. We conduct a review of psychometric literature to determine the current standard for Likert scale design and analysis. Next, we conduct a survey of five years of the International Conference on Human-Robot Interaction (HRIc) (2016 through 2020) and report on incorrect statistical practices and design of Likert scales [ 1 , 2 , 3 , 5 , 7 ]. During these years, only 4 of the 144 papers applied proper statistical testing to correctly designed Likert scales. We additionally conduct a survey of best practices across several venues and provide a comparative analysis to determine how Likert practices differ across the field of Human-robot Interaction. We find that a venue’s impact score negatively correlates with number of Likert-related errors and acceptance rate, and total number of papers accepted per venue positively correlates with the number of errors. We also find statistically significant differences between venues for the frequency of misnomer and design errors. Our analysis suggests there are areas for meaningful improvement in the design and testing of Likert scales. Based on our findings, we provide guidelines and a tutorial for researchers for developing and analyzing Likert scales and associated data. We also detail a list of recommendations to improve the accuracy of conclusions drawn from Likert data.

Learning from Demonstration (LfD) algorithms seek to enable end-users to teach robots new skills through human demonstration of a task. Previous studies have analyzed how robot failure affects human trust, but not in the context of the human teaching the robot. In this paper, we investigate how human teachers react to robot failure in an LfD setting. We conduct a study in which participants teach a robot how to complete three tasks, using one of three instruction methods, while the robot is pre-programmed to either succeed or fail at the task. We find that when the robot fails, people trust the robot less (p < .001$) and themselves less (p=.004) and they believe that others will trust them less (p < .001$). Human teachers also have a lower impression of the robot and themselves (p < .001) and found the task more difficult when the robot fails (p < .001$). Motion capture was found to be a less difficult instruction method than teleoperation (p=.016), while kinesthetic teaching gave the teachers the lowest impression of themselves compared to teleoperation (p=.017) and motion capture (p < .001). Importantly, a mediation analysis showed that people's trust in themselves is heavily mediated by what they think that others -- including the robot -- think of them (p < .001). These results provide valuable insights to improving the human-robot relationship for LfD.

As automation becomes more prevalent, the fear of job loss due to automation increases [22]. Workers may not be amenable to working with a robotic co-worker due to a negative perception of the technology. The attitudes of workers towards automation are influenced by a variety of complex and multi-faceted factors such as intention to use, perceived usefulness and other external variables [15]. In an analog manufacturing environment, we explore how these various factors influence an individual's willingness to work with a robot over a human co-worker in a collaborative Lego building task. We specifically explore how this willingness is affected by: 1) the level of social rapport established between the individual and his or her human co-worker, 2) the anthropomorphic qualities of the robot, and 3) factors including trust, fluency and personality traits. Our results show that a participant's willingness to work with automation decreased due to lower perceived team fluency (p=0.045), rapport established between a participant and their co-worker (p=0.003), the gender of the participant being male (p=0.041), and a higher inherent trust in people (p=0.018).

Signal strength based positioning technology offers both cost advantages and technical challenges compared with other positioning methods. This study analyzes measurements taken in a commercial retail environment to develop a set of practical expectations and guidelines in the use of signal strength based positioning systems. A commercially available positioning engine for wireless LANs was used as a readily reproducible basis for the experiments.

Collisions, collision avoidance, hidden terminals, and automatic rate selection in response to signal quality all reduce the effective bandwidth of the 802.11 wireless medium when many clients are active in proximity. To determine the resulting degraded bandwidth, experiments were conducted with 96 wireless clients operating simultaneously in a variety of signal quality conditions, each having traffic typical of a browser-interfaced Web service. The wireless medium was modeled as an M/M/1 queue with capacity a parameter determined by a nonlinear regression fit to the measurements. The analysis provides practical design guidelines for wireless bandwidth utilization and access point placement in settings having a large number of moving clients.

Two-dimensional (2-D) effects are becoming increasingly important in the diffusion of impurities in submicrometer silicon devices. The authors describe a 2-D process simulator, PREDICT2, that handles implant damage effects, annealing, and lateral diffusion. PREDICT2 simulates the diffusion of impurities in silicon by using phenomenological diffusion coefficients. The phenomenological models are verified by comparing simulated and experimental results. This approach is compared with that of point-defect-based simulators. The experimental technique for measuring impurity profiles in two dimensions is outlined. The method for generating diffusion models is illustrated by examining 2-D phosphorous diffusion. Numerical simulations and experimental measurements are compared. The numerical methods used in the simulator are described, and directions for future work are suggested.>