Conventional methods of obtaining student feedback on course experience face a fundamental tradeoff between feedback frequency and quality: as feedback requests become more frequent, participation often declines, and responses become less thoughtful over time. To obtain both timely and thoughtful feedback from students, Kim and Piech (Learning at Scale, 2023) recently proposed a simple, lightweight course feedback mechanism: surveying each student a small number of times per term during randomly selected weeks. Named High-Resolution Course Feedback (HRCF), this method has been shown to elicit feedback that instructors find helpful without imposing excessive burden on students. An important question, however, remains unanswered: is the use of this simple method associated with measurable improvements in students' actual course experiences? We study HRCF use across 103 course offerings, totaling 24,216 student enrollments, over four years from Fall 2021 through Fall 2025, spanning 42 unique computer science courses at an R1 institution. Through a regression analysis of four end-of-term student evaluation items for these courses, we find that first-time use of HRCF is not associated with a measurable change in average student ratings. However, among small- and medium-enrollment (<250 students) course offerings, continued HRCF use is associated with average rating increases of 0.045 to 0.048 points per additional term of use for learning-related items. We observe no statistically significant associations for large-enrollment (250 or more students) course offerings, nor for items measuring instructional quality and course organization. Together, these findings suggest that sustained HRCF use may support improvements in students' learning experiences, but that further design enhancements may be needed to produce measurable improvements in instructional quality and course organization.

翻译：传统获取学生课程体验反馈的方法在反馈频率与质量之间存在根本性权衡：随着反馈请求频率增加，参与度往往下降，且回复随时间推移而变得缺乏深度。为同时获取学生及时且有深度的反馈，Kim和Piech（《规模化学习》，2023）近期提出了一种简单、轻量级的课程反馈机制：在学期内随机选取的几周对每位学生进行少量调查。该方法被命名为高分辨率课程反馈（HRCF），已证明能引发教师认为有用的反馈，同时不会给学生带来过多负担。然而，一个重要问题仍未得到解答：使用这一简单方法是否与学生实际课程体验的可测量改善相关？我们研究了2021年秋季至2025年秋季四年间103门课程中HRCF的使用情况，涵盖一所R1类大学的42门独特计算机科学课程，共涉及24,216名学生选课。通过对这些课程四类期末学生评估项目的回归分析，我们发现首次使用HRCF与平均学生评分的可测量变化无关。然而，在中小规模（少于250名学生）的课程中，持续使用HRCF与学习相关项目的平均评分每额外学期增加0.045至0.048分相关。对于大规模（250名及以上学生）课程，以及衡量教学质量和课程组织的项目，我们未观察到统计上显著的关联。综合这些发现表明，持续使用HRCF可能有助于改善学生的学习体验，但可能需要进一步的设计优化才能在教学质量和课程组织方面产生可测量的改进。