In Grading for EQUITY: What It Is, Why It Matters, and How It Can Transform Schools and Classrooms, Joe Feldman states 'The ways we grade disproportionately favor students with privilege and disproportionately harm students with less privilege.' (2019, pp. xxii-xxiii) This workshop provides a process to make grading practices more equitable. The process includes: 1) analyzing course goals to yield desired course outcomes; 2) developing assessments of the outcomes; 3) developing learning activities; 4) adopting a different approach to grading assessments; and 5) developing an appropriate overall grading scheme. With some practice and discussion with peers you can implement an equitable grading process that revolutionizes your instructional practice. Grading will: no longer address student behavior subject to bias; incorporate better mathematical practices becoming more accurate, lack bias; and be motivating. Grading For Equity alleviates: the homework grading burden with feedback ignored by students; worries about cheating on homework; and arguing with students about points while it allows instructors to focus on working with students in a professional and less stressful manner. Workshop activity will address all five process elements noted above. Participants need to view a background presentation prior to the workshop and will receive sample outcomes, assessments, and grading suggestions for various types of courses in addition to workshop products.

Overlay networks, where nodes communicate with neighbors over logical links consisting of zero or more physical links, have become an important part of modern networking. From data centers to IoT devices to Internet-based applications, overlay networks are used to organize a diverse set of processes for efficient operations like searching and routing. Many of these overlay networks operate in fragile environments where processes are susceptible to faults which may perturb the logical network topology. Self-stabilizing overlay networks have been proposed as one way to manage these faults, promising to build or restore a particular topology from any initial configuration or after the occurrence of any transient faults. Designing efficient self-stabilizing algorithms for many topologies, however, is not an easy task. For non-trivial topologies that have desirable properties like low diameter and robust routing in the face of node or link failures, self-stabilizing algorithms to date have had at least linear running time or space requirements. In this brief announcement, we sketch an algorithm for building a Chord network that has polylogarithmic time and space complexity.

Perhaps no other task has frustrated us as instructors as much as grading. While we recognize the importance of providing meaningful feedback and accurate assessment, our traditional grading had quickly become a task of administrative minutiae instead of helpful coaching. Like many faculty, we have searched for ways to reduce our grading load without harming student learning. We have tried a variety of approaches, from modifying our existing assignments all the way to rethinking our entire course design. The past few years we have been refining a binary grading system : a set of grading principles based upon the practice of scoring student assessments simply as either satisfactory or unsatisfactory ($10_2$ possible scores, thus the paper title). We believe this approach has significantly improved our students' attitudes towards our courses and holds promise as a way to significantly reduce the time we spend grading. In this paper, we describe our experiences with our binary grading system in several undergraduate computer science courses. We discuss the general practices of our binary grading, how we have implemented it in our courses, and what our experiences have taught us regarding the future refinement of our grading practices. We hope our experiences will spur discussion and assist other computer science educators in improving their assessment process.

Improving one's understanding of teaching and learning and applying it to instructional practice is a continuous process. While a few of us may be natural teachers, most of us need guidance to follow when planning and implementing instruction for the courses we teach. As we have examined our practice, we've discovered that we typically begin planning with instructional activity, but our plans are not done until we have explicated a grading plan (and then revised instructional plans to fit it). Recently, we have begun working with a grading plan motivated by equitable grading, an idea we first encountered in the book Grading for Equity by Joe Feldman. We have found that adopting an equitable grading plan has provided us with a framework for course design that has significantly improved our pedagogy. In this poster, we present the first work on implementing equitable grading in computer science education.

The file system is a critical and ubiquitous part of our operating systems, yet it is often poorly understood. While many undergraduate students studying computer science learn the fundamentals of operating systems, they can often find it difficult to fully comprehend how a file system works. This difficulty may be in part because the true workings of a file system are hidden from the user, and so any coverage of its operation was done, by necessity, in the abstract. To address this, we have created a tool which visualizes the operations of the file system in situ, much like Wireshark provides information on what is happening on a network. Our tool, named Fileshark, provides an easy-to-use interface for visualizing the processes involved in reading, storing, and deleting data. Our tool can aid in the teaching of operating systems, computer forensics, and common concepts like transactions that are also found in other domains (e.g. databases and distributed systems).

In this paper, we study a compositional approach to designing a class of stabilizing distributed systems. We show that the linear pipelined composition of a number of stabilizing modules is inherently stabilizing, and is a useful method of constructing scalable stabilizing solutions for streaming applications that are on the rise in peer-to-peer and sensor networks. We present the correctness proof and complexity analysis of the composition for a linear pipeline. Subsequently, we generalize the pipelined composition to alternative, concurrent, and repetitive versions, investigate the stabilization properties of these versions, and present a set of conditions under which these extended constructions retain their stabilization properties.

We describe a simple framework, called the transitive closure framework (TCF), for the self-stabilizing construction of any overlay network. The TCF is easy to reason about and algorithms derived from it stabilize within O(log n) more rounds than the optimal. As evidence of the power of this framework, we derive from the TCF a simple, self-stabilizing protocol for constructing Skip + graphs in O(log n) rounds.

The token ring is a seminal topic in self-stabilization research. It has been expanded to include multiple tokens, and improved upon using randomization to lower the state space requirements. In this brief announcement, we discuss how the two ideas can be brought together for an optimal solution to the multi-token ring problem.