Summary of Video Project Development in the Midst of an Impromptu Online Semester
Students in EECS 373 adapted to the sudden shift to remote learning in Spring 2020 by receiving mailed component kits and building autonomous robots at home. Each student built a robot that seeks the brightest light while avoiding obstacles, learning to read datasheets and integrate sensors, actuators, and controllers. Presentations and demos were recorded for grading; instructors noted success despite time constraints and suggested earlier planning could expand future kit options.
Parts used in the EECS 373 Robot Project:
- Light sensor
- Ultrasonic distance sensor
- Analog-to-digital converter
- Robot chassis
- H-bridge motor controller
- Servo motor for sensor rotation
- Nucleo development board
Receiving robot parts in the mail was an unexpected conclusion to the Winter 2020 semester for students. However, when faced with a unique situation, innovative solutions were required, and the instructors of EECS 373: Introduction to Embedded System Design rose to the challenge.
With the sudden transition to online learning in March due to the impact of the coronavirus, instructors had to rapidly reimagine their semester plans. Converting lectures, labs, and exams to an online format was an urgent task, and this was especially demanding for courses that heavily relied on physical components and hands-on projects.
EECS 373 was among those courses. It educates students about the fundamentals of designing embedded systems, which are specialized computing devices distinct from typical computers. These systems encompass the smart devices found in many households, sensing systems, and the computers integrated into automobiles and other intricate machinery. In EECS 373, students acquire skills in working with the sensors, actuators, wireless communication, and computing elements that contribute to the resilience of these systems.
The course typically concludes with a final design project, usually executed in small groups and presented during a public demonstration at the semester’s end. In an effort to ensure students don’t miss out on their hands-on experience, the course instructors swiftly devised an alternative plan, which involved students constructing robots from home.
Prof. Ron Dreslinski, the course’s primary instructor, explained the traditional process: students would form groups, collaboratively devise solutions for embedded systems challenges, work on problem statements, select components, construct systems, and troubleshoot issues.
Transitioning to remote learning presented difficulties in sharing physical projects. Therefore, during the spring break, the course instructors began to prepare for the possibility of finishing the semester remotely by researching component kits that could be sent to the students.
Each student received a kit containing components and was tasked with transforming them into an autonomous robot capable of detecting and moving toward the brightest source of light in a room while avoiding obstacles. As they progressed, they had to familiarize themselves with the components by studying accompanying datasheets. The kit included a light sensor, ultrasonic distance sensor, analog-to-digital converter, robot chassis, h-bridge motor controller, servo motor for sensor rotation, and a Nucleo development board.
Dreslinski mentioned, “The students still had to read and interpret the datasheets and figure out how to integrate the pieces. We left the problem description vague, so that students had to define the corner cases and solution.”
Although the project’s scale was smaller compared to traditional group projects, it was challenging enough for an individual student to tackle. Students recorded presentations and demonstrations of their final designs to submit their work.
Dreslinski expressed satisfaction with the results given the time constraints but believes that future planning can alleviate issues related to waiting for parts and assembling kits. He said, “With more lead time, if we needed to do this again in the fall, we would be able to select a wider range of sensors to provide, as well as make the problem even more open-ended.”
The project was well-received by students, as evidenced in course feedback, with many appreciating the opportunity to apply their semester-long learning despite the unexpected shift in plans.
The leadership of EECS 373 included Dreslinski, with Dr. Matt Smith serving as the lab instructor, and undergraduate students Tejas Harith, Mayukh Nath, Ritika Sibal, Alexander Skillin, and David Waier assisting in instructional roles.
Source: Video Project Development in the Midst of an Impromptu Online Semester
- What was the final project students built for EECS 373?
Each student built an autonomous robot that detects and moves toward the brightest light while avoiding obstacles. - Why did students receive robot kits in the mail?
Kits were mailed so students could complete hands-on projects remotely after the sudden shift to online learning. - What components were included in each kit?
Kits included a light sensor, ultrasonic distance sensor, analog-to-digital converter, robot chassis, h-bridge motor controller, servo motor for sensor rotation, and a Nucleo development board. - How did students learn to use the components?
Students studied accompanying datasheets and integrated the pieces themselves. - Were students required to work in groups for the mailed-kit project?
The mailed-kit project was smaller in scale and designed so an individual student could tackle it. - How were final projects evaluated?
Students recorded presentations and demonstrations of their final designs to submit for grading. - Who led the EECS 373 course during this semester?
Prof. Ron Dreslinski was the primary instructor, with Dr. Matt Smith as lab instructor and several undergraduate instructional assistants. - Did instructors find the mailed-kit approach successful?
Yes; instructors were satisfied with results given time constraints and noted student appreciation in course feedback. - What improvement did instructors suggest for future iterations?
With more lead time, they would select a wider range of sensors and make the problem more open-ended.
