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EdWorkingPapers
While recent studies have demonstrated the potential of automated feedback to enhance teacher instruction in virtual settings, its efficacy in traditional classrooms remains unexplored. In collaboration with TeachFX, we conducted a pre-registered randomized controlled trial involving 523 Utah mathematics and science teachers to assess the impact of automated feedback in K-12 classrooms. This feedback targeted focusing questions questions that probe students thinking by pressing for explanations and reflection. Our findings indicate that automated feedback increased teachers use of focusing questions by 20%. However, there was no discernible effect on other teaching practices. Qualitative interviews revealed mixed engagement with the automated feedback: some teachers noticed and appreciated the reflective insights from the feedback, while others had no knowledge of it. Teachers also expressed skepticism about the accuracy of feedback, concerns about data security, and/or noted that time constraints prevented their engagement with the feedback. Our findings highlight avenues for future work, including integrating this feedback into existing professional development activities to maximize its effect.
Study at a glance
- Question: Does automated feedback improve the questions teachers ask in traditional K-12 classrooms?
- Setting and data: 523 K-12 mathematics and science teachers in Utah
- Method: Randomized controlled trial and qualitative interviews
Why it matters
Automated feedback has the potential to enhance teacher instruction in virtual settings, but its efficacy in traditional classrooms remains unexplored.
Key findings
- Among 523 Utah K-12 math and science teachers, automated feedback increased teachers' use of questions probing student thinking by 20%.
- The automated feedback had no noticeable effect on other classroom teaching practices.
- Teachers reported mixed engagement, noting helpful reflective insights alongside skepticism about accuracy, data security concerns, and time constraints.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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