NESL Technical Report #: 2017-2-1
Abstract: Grading in embedded systems courses typically requires a face-to-face appointment between the student and the instructor because of experimental setups that are only available in laboratory facilities. Such a manual grading process is an impediment to both students and instructors. Students have to wait for several days to get feedback, and instructors may spend valuable time evaluating trivial aspects of the assignment. As seen with software courses, an automated grading system can significantly improve the insights available to the instructor and encourage students to learn quickly with iterative testing. We have designed and implemented EmbedInsight, an automated grading system for embedded system courses that accommodates a wide variety of experimental setups and is scalable to MOOC-style courses. EmbedInsight employs a modular web services design that separates the user interface and the experimental setup that evaluates student assignments. We deployed and evaluated EmbedInsight for our university embedded systems course. We show that our system scales well to a large number of submissions, and students are satisfied with their overall experience.
Public Document?: Yes
NESL Document?: Yes
Document category: Report
Primary Research Area: #<ResearchArea:0x007efc40c0ddc8>