Understanding the effectiveness of automated feedback: Using process data to uncover the role of behavioral engagement
Open accessAbstract
In the last couple of years, feedback research has shifted towards a feedback-as-process approach, taking a learner-centered perspective and focusing on the proactive role of the learner in feedback effectiveness. Process measures can provide new insights into the role of the learner by making learners’ actual behavioral engagement visible. We conducted an experimental study, comparing two groups (feedback vs. no feedback) of English-as-a-foreign-language learners in lower secondary schools ( N = 189). The learners completed a writing task and revised it with or without feedback. A second writing task served as a transfer task. Performance was automatically assessed using a scoring algorithm. To determine the level of learners’ behavioral engagement during the text revision, we used the revision time and the edit distance (i.e., a similarity measure) as behavioral measures. Our analyses showed a positive effect of feedback on text revision. We found a full mediation of the effect of feedback on text revision through revision time with an estimated portion of mediation (POM) of .63∗∗∗ and a partial mediation of the feedback effect on text revision through the edit distance with a POM of .30∗∗. We did not find significant mediation effects of either engagement variable regarding performance in a transfer task. Our findings contribute to the understanding of feedback effectiveness, highlighting the central role of learner engagement in the feedback process.
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The impact of feedback on learning outcomes is mediated through complex internal mechanisms involving cognitive processing, behavioral patterns, and affective states.
“The impact of feedback on learning outcomes is mediated through complex internal mechanisms. Three commonly-discussed mechanisms involve different dimensions of learners' engagement (Fredricks et al., 2004): cognitive processing, behavioral patterns, and affective states(Schiller et al., 2024).”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Schiller et al., 2024
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AI가 생성한 패션 디자인에 대한 창의성 지각이 행동적 인게이지먼트에 미치는 영향
Lee, H. K., Meiqi, S., & 윤소정 (2026) · Journal of the Korean Society of Clothing and Textiles · doi:10.5850/jksct.2026.50.4.809
- Authors
- Schiller, R., Fleckenstein, J., Mertens, U., Horbach, A., & Meyer, J.
- Affiliations
- Leibniz Institute for Science and Mathematics Education (DE)
- University of Hildesheim (DE)
- Corresponding author
- Schiller, Ronja
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 223
- Pages
- 105163
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W4402490467
- Cited by
- 36
- References
- 79
- Citations by year
- 2026: 19 · 2025: 16 · 2024: 1
- FWCI
- 45.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers & Education (publishedVersion, cc-by)
- Funders
- Federal Ministry of Education and Research Bonn Office (01JG2104)
- Jacobs Foundation (2024-2026)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- automated feedback, feedback effectiveness, student engagement, behavioral engagement, textual revision, English as a foreign language, writing instruction, edit distance, automatic scoring, transfer tasks, mediation analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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