A data-driven, ten-type taxonomy of teacher feedback on open-ended mathematics responses¶

Theory · All theories
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

This taxonomy organizes teacher-authored feedback into 10 empirically derived types: Direct Correction, Targeted Affirmation, Hinting, Non-verbal Affirmation, Rhetorical Question, Show Your Work, Generic Comment, Direct Critique, Worked Example, and Non-specific Positivity. It was built through a pipeline of qualitative coding, factor analysis, and K-means clustering on authentic ASSISTments feedback, and the article states it "illustrates how core dimensions of teacher feedback can combine to reveal distinct patterns of instructional practice." Unlike broader frameworks, it delineates subtypes of affirmation and correctness feedback by specificity and modality.

Design Implications¶

Context¶

Requirements¶

• Requires feedback messages coded across the 19 feedback characteristics by multiple coders so that factor and cluster analyses can be computed
• Requires coder perspectives to be retained as distinct variables rather than collapsed into one interpretive lens

Constraints¶

• The article states the scope "was confined to teacher-written feedback in middle school mathematics", with generalizability to other subjects, grade levels, and modalities untested
• Small clusters (one and two observations) warrant caution in interpretation

Target Learners¶

• middle school mathematics students

Target Learning Objectives¶

• providing pedagogically meaningful feedback on students' open-ended mathematical reasoning and problem-solving explanations

Claims¶

• Some teacher feedback codes commonly co-occur, such as Confirmation with Positive Affect and Mathematical Suggestions with Hints [+W]

Related Theories¶

• Lyster and Ranta's six-category taxonomy of corrective feedback

Examples¶

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Key Sources¶

• Seiyon M. Lee, Sami Baral, Hongming Chip Li, Li Cheng, Shan Zhang, Carly S. Thorp, Jennifer St. John, Tamisha Thompson, Neil Heffernan, Anthony F. Botelho. (2025). Developing Feedback Taxonomy for Math: A Synergy of Perspectives through Data Mining Methods. Journal of Educational Data Mining, Volume 17, No 2. https://osf.io/puhyr/?view_only=514d8534e9be41d9a57ad82e9efb3249