High-Resolution Course Feedback: Timely Feedback Mechanism for Instructors

We study the problem of minimizing the delay between when an issue comes up in a course and when instructors get feedback about it. The widespread practice of obtaining midterm and end-of-term feedback from students is suboptimal in this regard, especially for large courses: it over-samples at a specific point in the course and can be biased by factors irrelevant to the teaching process. As a solution, we release High Resolution Course Feedback (HRCF), an open-source student feedback mechanism that builds on a surprisingly simple idea: survey each student on random weeks exactly twice per term. Despite the simplicity of its core idea, when deployed to 31 courses totaling a cumulative 6,835 students, HRCF was able to detect meaningful mood changes in courses and significantly improve timely feedback without asking for extra work from students compared to the common practice. An interview with the instructors revealed that HRCF provided constructive and useful feedback about their courses early enough to be acted upon, which would have otherwise been unobtainable through other survey methods. We also explore the possibility of using Large Language Models to flexibly and intuitively organize large volumes of student feedback at scale and discuss how HRCF can be further improved.