Title: EmbedInsight: Automated Grading of Embedded Systems Assignments,
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.