The field of electronic textiles (e-textiles) combines digital technology with textile objects, and has applications in fields such as wearable computing, theatrical design, and medicine.Prior work has examined deploying this technology in educational settings, to teach such skills as circuit design, computer programming, and iterative design.However, e-textile-based learning materials are still not commonly used, and more validated examples of such interventions would be valuable.The aim of this project is to investigate the state of the art in e-textile technology, especially in educational contexts, and to develop and evaluate an e-textiles intervention which could be deployed in a classroom or extra-curricular setting to teach introductory programming skills.So far, we have conducted a literature review examining applications of e-textiles in learning environments.For example, in one study [9], the researchers provided a safe environment for children with ASD (Autism Spectrum Disorder) to create their own sensory haptic toy.We found that many of the studies targeted middle and high school age children as a way to gauge and increase their knowledge of electricity and sewing techniques, but not many examined undergraduates.Therefore, in future work, we plan to conduct an experiment investigating the effectiveness of e-textiles in undergraduate learning.

Instructors using algorithmic team formation tools must decide which criteria (e.g., skills, demographics, etc.) to use to group students into teams based on their teamwork goals, and have many possible sources from which to draw these configurations (e.g., the literature, other faculty, their students, etc.). However, tools offer considerable flexibility and selecting ineffective configurations can lead to teams that do not collaborate successfully. Due to such tools' relative novelty, there is currently little knowledge of how instructors choose which of these sources to utilize, how they relate different criteria to their goals for the planned teamwork, or how they determine if their configuration or the generated teams are successful. To close this gap, we conducted a survey (N=77) and interview (N=21) study of instructors using CATME Team-Maker and other criteria-based processes to investigate instructors' goals and decisions when using team formation tools. The results showed that instructors prioritized students learning to work with diverse teammates and performed "sanity checks" on their formation approach's output to ensure that the generated teams would support this goal, especially focusing on criteria like gender and race. However, they sometimes struggled to relate their educational goals to specific settings in the tool. In general, they also did not solicit any input from students when configuring the tool, despite acknowledging that this information might be useful. By opening the "black box" of the algorithm to students, more learner-centered approaches to forming teams could therefore be a promising way to provide more support to instructors configuring algorithmic tools while at the same time supporting student agency and learning about teamwork.

The configuration that an instructor enters into an algorithmic team formation tool determines how students are grouped into teams, impacting their learning experiences. One way to decide the configuration is to solicit input from the students. Prior work has investigated the criteria students prefer for team formation, but has not studied how students prioritize the criteria or to what degree students agree with each other. This paper describes a workflow for gathering student preferences for how to weight the criteria entered into a team formation tool, and presents the results of a study in which the workflow was implemented in four semesters of the same project-based design course. In the most recent semester, the workflow was supplemented with an online peer discussion to learn about students' rationale for their selections. Our results show that students want to be grouped with other students who share the same course commitment and compatible schedules the most. Students prioritize demographic attributes next, and then task skills such as programming needed for the project work. We found these outcomes to be consistent in each instance of the course. Instructors can use our results to guide team formation in their own project-based design courses and replicate our workflow to gather student preferences for team formation in any course.

Team formation tools assume instructors should configure the criteria for creating teams, precluding students from participating in a process affecting their learning experience. We propose LIFT, a novel learner-centered workflow where students propose, vote for, and weigh the criteria used as inputs to the team formation algorithm. We conducted an experiment (N=289) comparing LIFT to the usual instructor-led process, and interviewed participants to evaluate their perceptions of LIFT and its outcomes. Learners proposed novel criteria not included in existing algorithmic tools, such as organizational style. They avoided criteria like gender and GPA that instructors frequently select, and preferred those promoting efficient collaboration. LIFT led to team outcomes comparable to those achieved by the instructor-led approach, and teams valued having control of the team formation process. We provide instructors and designers with a workflow and evidence supporting giving learners control of the algorithmic process used for grouping them into teams.

How can instructors group students into teams that interact and learn effectively together? One strand of research advocates for grouping students into teams with "good" compositions such as skill diversity. Another strand argues for deploying team-building activities to foster interpersonal relations like psychological safety. Our work synthesizes these two strands of research. We describe an experiment (N=249) that compares how team composition vs. team-building activities affect student team outcomes. In two university courses, we composed student teams either randomly or using a criteria-based team formation tool. Teams further performed team-building activities that promoted either team or task outcomes. We collected project scores, and used surveys to measure psychological safety, perceived performance, and team satisfaction. Surprisingly, the criteria-based teams did not statistically differ from the random teams on any of the measures taken, despite having compositions that better satisfied the criteria defined by the instructor. Our findings argue that, for instructors deploying a team formation tool, creating an expectation among team members that their team can perform well is as important as tuning the criteria in the tool. We also found that student teams reported high levels of psychological safety, but these levels appeared to develop organically and were not affected by the activities or compositional strategies tested. We distill these and other findings into implications for the design and deployment of team formation tools for learning environments.

High-performance computing systems are shifting away from traditional interconnect topologies to exploit new technologies and to reduce interconnect power consumption. The Dragonfly topology is one promising candidate for new systems, with several variations already in production. It is hierarchical, with local links forming groups and global links joining the groups. At each level, the interconnect is a clique, with a link between each pair of switches in a group and a link between each pair of groups. This paper shows that the intergroup links can be made in meaningfully different ways. We evaluate three previously-proposed approaches for link organization (called global link arrangements) in two ways. First, we use bisection bandwidth, an important and commonly-used measure of the potential for communication bottlenecks. We show that the global link arrangements often give bisection bandwidths differing by 10s of percent, with the specific separation varying based on the relative bandwidths of local and global links. For the link bandwidths used in a current Dragonfly implementation, it is 33%. Second, we show that the choice of global link arrangement can greatly impact the regularity of task mappings for nearest neighbor stencil communication patterns, an important pattern in scientific applications.