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Redox-active colloids (RACs) represent a novel class of energy carriers that exchange electrical energy upon contact. Understanding contact-mediated electron transfer dynamics in RACs offers insights into physical contact events in colloidal suspensions and enables quantification of electrical energy transport in nonconjugated polymers. Redox-based electron transport was directly observed in monolayers of micron-sized RACs containing ethyl-viologen side groups via fluorescence microscopy through an unexpected nonlinear electrofluorochromism that is quantitatively coupled to the redox state of the colloid. Via imaging studies, using this electrofluorochromism, the apparent charge transfer diffusion coefficientD_CTof the RAC was easily determined. The visualization of energy transport within suspensions of redox-active colloids was also demonstrated. Our work elucidates fundamental mechanisms of energy transport in colloidal systems, informs the development of next-generation redox flow batteries, and may inspire new designs of smart active soft matter including conductive polymers for applications ranging from electrochemical sensors and organic electronics to colloidal robotics. 

Peer evaluations are a well-established tool for evaluating individual and team performance in collaborative contexts, but are susceptible to social and cognitive biases. Current peer evaluation tools have also yet to address the unique opportunities that online collaborative technologies provide for addressing these biases. In this work, we explore the potential of one such opportunity for peer evaluations: data traces automatically generated by collaborative tools, which we refer to as "activity traces". We conduct a between-subjects experiment with 101 students and MTurk workers, investigating the effects of reviewing activity traces on peer evaluations of team members in an online collaborative task. Our findings show that the usage of activity traces led participants to make more and greater revisions to their evaluations compared to a control condition. These revisions also increased the consistency and participants' perceived accuracy of the evaluations that they received. Our findings demonstrate the value of activity traces as an approach for performing more reliable and objective peer evaluations of teamwork. Based on our findings as well as qualitative analysis of free-form responses in our study, we also identify and discuss key considerations and design recommendations for incorporating activity traces into real-world peer evaluation systems. 

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. 

Peer evaluations are critical for assessing teams, but are susceptible to bias and other factors that undermine their reliability. At the same time, collaborative tools that teams commonly use to perform their work are increasingly capable of logging activity that can signal useful information about individual contributions and teamwork. To investigate current and potential uses for activity traces in peer evaluation tools, we interviewed (N=11) and surveyed (N=242) students and interviewed (N=10) instructors at a single university. We found that nearly all of the students surveyed considered specific contributions to the team outcomes when evaluating their teammates, but also reported relying on memory and subjective experiences to make the assessment. Instructors desired objective sources of data to address challenges with administering and interpreting peer evaluations, and have already begun incorporating activity traces from collaborative tools into their evaluations of teams. However, both students and instructors expressed concern about using activity traces due to the diverse ecosystem of tools and platforms used by teams and the limited view into the context of the contributions. Based on our findings, we contribute recommendations and a speculative design for a data-centric peer evaluation tool. 

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.