Automated Grading System Earns Inaugural Outstanding Teaching Innovation Award

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A scalable, automated grading system designed to provide students with intelligent feedback on their work while freeing up instructor time for in-person student support was recognized with the inaugural Award for Outstanding Teaching Innovation from the University of California San Diego Jacobs School of Engineering.

Developed by Niema Moshiri, an associate teaching professor in the Department of Computer Science and Engineering, the scalable adaptive automated grading system framework provides several key benefits:

• Students receive descriptive feedback if they submit an incorrect answer, so they are able to diagnose the error and learn from the mistake
• Faculty and instructional staff spend less time grading assignments, and more time interacting with students and designing more effective, creative coursework
• Students receive immediate feedback, instead of waiting to see what course material they need to review

“AI systems and algorithms will never be able to create empathy or connection with students,” Moshiri said. “So I use technology to automate what I can automate, so that I can maximize the human empathy aspect to work with students, diagnose issues, and talk with them about the class.”

Faculty in structural engineering, bioengineering, electrical engineering and even biology have met with Moshiri to discuss potentially implementing similar grading systems for their classes. While some components of Moshiri’s system are modular and could be adapted by any faculty, the key element – the structure of the questions and feedback provided for incorrect answers – is tailored by Moshiri for each course and does not rely on any AI models. Moshiri views the grading system as a living document that he updates each quarter as he and his instructional staff continue to learn about pitfalls that students face when learning the course material.

Moshiri designs the assessment questions to build off one another, so that he is able to predict the misconceptions that students will have for each question, and provides specific feedback for the top few incorrect answers he suspects students may submit. In introductory courses, he is very detailed about the output and guidance provided to students. In upper division courses, he starts out providing a good deal of feedback, but reigns it in as the course progresses.

Automating all grading through the tools he created frees up his instructional staff to devote the bulk of their time to in-person student support.

“Every day of the week we have two or three people at any given time from roughly 9am to 7pm ready to talk to students and help them truly learn this material,” said Moshiri.

The grading system also allows faculty to randomize the data set generation, so that they don’t need to spend time recreating the same problem with different numbers or figures from quarter to quarter, or even from a homework assignment to an exam. This reduces the faculty workload, and ensures that the problems students see on exams correspond to problems they’ve seen in class, removing concerns about exams testing on unfamiliar material.

Jacobs School of Engineering Teaching and Mentorship Awards

The new Jacobs School of Engineering Teaching Innovation Award was created to recognize a specific teaching innovation with impact beyond a single classroom.

The award can go to either an individual faculty member or a faculty team, and can include such innovations as the creation of new pedagogical techniques, developing innovative undergraduate curriculum, progressive assessment strategies, educational enhancement tools, inclusive classroom practices, development of educational technologies, or impactful educational research.

This new award complements the Jacobs School’s suite of annual teaching and mentorship awards, which include a best undergraduate teaching award in each academic department, an outstanding graduate student mentorship award in each department, and a Jacobs School-wide outstanding faculty mentor award.

“Educating the innovation workforce for the economy of the future is at the core of what we do at the Jacobs School of Engineering,” said Albert P. Pisano, Dean of the Jacobs School of Engineering and Special Adviser to the Chancellor for Campus Strategic Initiatives. “We are able to deliver on this mission because of our talented and creative educators of every stripe. Congratulations to all our faculty who have received teaching and mentorship awards this year.”

Best Undergraduate Teacher Awards

Each year, the Jacobs School honors a faculty member from each academic department as a Best Undergraduate Teacher of the year.

The 2024 - 2025 Best Undergraduate Teacher Awards to go:

Alyssa Taylor, Bioengineering

Justin Opatkiewicz, Chemical and Nano Engineering

Sicun Gao, Computer Science and Engineering

Rajeev Sahay, Electrical and Computer Engineering

Sylvia Herbert, Mechanical and Aerospace Engineering

Lelli Van Den Einde, Structural Engineering

Best Graduate Mentor Awards

The Jacobs School honors six faculty awarded Best Graduate Mentor

2024-2025 Outstanding Graduate Mentor Award:

Vira Kravets, Bioengineering

Liangfang Zhang, Chemical and Nano Engineering

Imani Munyaka, Computer Science and Engineering

Gabriel Rebeiz, Electrical and Computer Engineering>

Hyonny Kim, Structural Engineering

Sergei Krasheninnikov, Mechanical and Aerospace Engineering

Outstanding Faculty Mentor Award

Each year, the Jacobs School names one Outstanding Faculty Mentor from the Jacobs School of Engineering.

2024-2025 Outstanding Faculty Mentor:

Shaya Fainman, Electrical and Computer Engineering