Automated grading tools enable instructors to quickly assess student programming submissions in a scalable fashion. Not only do they save staff time, they enable a virtuous cycle in which students can submit work, get immediate feedback, and make improvements before the final deadline, rather than finding out about shortcomings when it is too late to get credit for fixing them. This supports mastery-based and specifications grading. Jacquard (https://github.com/espertus/jacquard) is a powerful open-source Java autograder for Gradescope that has been used by over a thousand students at two universities. Jacquard is closely integrated with other code evaluation tools, including Checkstyle (code style adherence), PMD (static analysis), JUnit 5 (unit testing), and JaCoCo (code coverage and cyclomatic complexity). To encourage students to write good tests, Jacquard supports cross-testing, where student tests are graded on their ability to find bugs in hidden instructor code while not falsely reporting bugs in correct code. Jacquard also provides a powerful API for custom tests, such as that code uses language features in the manner required by the instructor. Workshop attendees will learn how to use basic and advanced Jacquard features so they can apply Jacquard to their own assignments. Attendees will also have the opportunity to discuss how best to apply autograders in different contexts, such as in-class activities, homework assignments, and exams. A laptop with IntelliJ and Java 17 is required.

Conference presentations usually focus on successful innovations: new ideas that yield significant improvements to current practice. Yet we often learn more from failure than from success. In this panel, we present five case studies of "good ideas" for improving CS education (most related to the COVID-19 pandemic) that didn't go as planned. Each contributor will describe their "good idea", the situation that resulted, and wider lessons for the CS community.

1. SUMMARY Google's App Inventor for Android (AIA) is a visual programming environment for creating mobile phone applications that is designed to be accessible and appealing to college nonmajors taking introductory courses in computer science. Specifically, AIA provides a development environment similar to StarLogo TNG [7], Scratch [5], and Alice [2] but enabling users to create mobile applications incorporating social networking, location awareness, and Web-based services for Google's Android platform [1].

Online information services have grown too large for users to navigate without the help of automated tools such as collaborative filtering, which makes recommendations to users based on their collective past behavior. While many similarity measures have been proposed and individually evaluated, they have not been evaluated relative to each other in a large real-world environment. We present an extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network. We determine the usefulness of the different recommendations by actually measuring users' propensity to visit and join recommended communities. We also examine how the ordering of recommendations influenced user selection, as well as interesting social issues that arise in recommending communities within a real social network.

Article Free Access Share on Just-in-time databases and the World-Wide Web Authors: Ellen Spertus Mills College, 5000 MacArthur Blvd, Oakland, CA Mills College, 5000 MacArthur Blvd, Oakland, CAView Profile , Lynn Andrea Stein MIT AI Lab, 545 Technology Square, Cambridge, MA MIT AI Lab, 545 Technology Square, Cambridge, MAView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 30–37https://doi.org/10.1145/288627.288638Online:01 November 1998Publication History 3citation310DownloadsMetricsTotal Citations3Total Downloads310Last 12 Months13Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

A major challenge in fine-grained computing is achieving locality without excessive scheduling overhead. We built two J-Machine implementations of a fine-grained programming model, the Berkeley Threaded Abstract Machine. One implementation takes an Active Messages approach, maintaining a scheduling hierarchy in software in order to improve data cache performance. Another approach relies on the J-Machine's message queues and fast task switch, lowering the control costs at the expense of data locality. Our analysis measures the costs and benefits of each approach, for a variety of programs and cache configurations. The Active Messages implementation is strongest when miss penalties are high and for the finest-grained programs. The hardware-buffered implementation is strongest in direct-mapped caches, where it achieves substantially better instruction cache performance.

This paper uses an abstract machine approach to compare the mechanisms of two parallel machines: the J-Machine and the CM-5. High-level parallel programs are translated by a single optimizing compiler to a fine-grained abstract parallel machine, TAM. A final compilation step is unique to each machine and optimizes for specifics of the architecture. By determining the cost of the primitives and weighting them by their dynamic frequency in parallel programs, we quantify the effectiveness of the following mechanisms individually and in combination. Efficient processor/network coupling proves valuable. Message dispatch is found to be less valuable without atomic operations that allow the scheduling levels to cooperate. Multiple hardware contexts are of small value when the contexts cooperate and the compiler can partition the register set. Tagged memory provides little gain. Finally, the performance of the overall system is strongly influenced by the performance of the memory system and the frequency of control operations.

No abstract available.