Motivated by the mandate to design and deploy a practical, real-world educational tool for grading, we extensively explore linguistic patterns for Short Answer Scoring (SAS) as well as authorship feedback. We approach the SAS task via a multipronged approach that employs linguistic context features for capturing domain-specific knowledge while emphasizing on domain agnostic grading and detailed feedback via an ensemble of explainable statistical models. Our methodology quantitatively supersedes multiple automatic short answer scoring systems.

Monitoring performance and availability are critical to operating successful content distribution networks. Internet measurements provide the data needed for traffic engineering, alerting, and network diagnostics. While there are significant benefits to performing end-user active measurements, these capabilities are limited to a small number of content providers with application control. In this work, we present a solution to the long-standing problem of issuing active measurements from clients without requiring application control, e.g., injecting JavaScript to the content served. Our approach uses server-side programmable features of the Network Error Logging specification that allow a CDN to induce a browser connection to an HTTPS server of the CDN's choosing without application control.