Grading student SQL queries manually is a tedious and error-prone process. The XData system, developed at IIT Bombay, can be used to test if a student query is correct or not. However, in case a student query is found to be incorrect, there is currently no way to automatically assign partial marks. Manually awarding partial marks is not scalable for classes with a large number of students, especially MOOCs, and is also prone to human errors. In this paper, we discuss techniques to award partial marks to student SQL queries, in case they are incorrect, based on a weighted equivalence edit distance metric. Our goal is to find a minimal sequence of edits on the student query such that it can be transformed to a query that is equivalent to a correct query. Our system can also be used in a learning mode where query edits can be suggested as feedback to students to guide them towards a correct query. Our automated partial marking system has been successfully used in courses at IIT Bombay and IIT Dharwad.

The XData system, currently being developed at IIT Bombay, provides an automated and interactive platform for grading student SQL queries, as well as for learning SQL. Prior work on the XData system focused on generating query specific test cases to catch common errors in queries. These test cases are used to check whether the student queries are correct or not. For grading student assignments, it is usually not sufficient to just check if a query is correct: if the query is incorrect, partial marks may need to be given, depending on how close the query is to being correct. In this paper, we extend the XData system by adding features that enable awarding of partial marks to incorrect student queries. Our system is able to go beyond numerous syntactic features when comparing a student query with a correct query. These features of our grading system allow the grading of SQL queries to be fully automated, and scalable to even large class sizes such as those of MOOCs.

Visual sensing for robotics has been around for decades, but our understanding of a timing model remains crude. By timing model, we refer to the delays (processing lag and motion lag) between "reality" (when a part is sensed), through data processing (the processing of image data to determine part position and orientation), through control (the computation and initiation of robot motion), through "arrival" (when the robot reaches "reality"). In this work we introduce a timing model where sensing and control operate asynchronously. We apply this model to a robotic workcell consisting of a Staubli RX-130 industrial robot manipulator, a network of six cameras for sensing, and an off-the-shelf Adept MV-19 controller. We demonstrate some experiments to show how the model is applied.