Decoding Actionability: A Computational Analysis of Teacher Observation Feedback

This study presents a computational analysis to classify actionability in teacher feedback.We fine-tuned a RoBERTa model on 662 manually annotated feedback examples from West African classrooms, achieving strong classification performance (accuracy = 0.94, precision = 0.90, recall = 0.96, f1 = 0.93).This enabled classification of over 12,000 feedback instances.A comparison of linguistic features indicated that actionable feedback was associated with lower word count but higher readability, greater lexical diversity, and more modifier usage.These findings suggest that concise, accessible language with precise descriptive terms may be more actionable for teachers.Our results support focusing on clarity in teacher observation protocols while demonstrating the potential of computational approaches in analyzing educational feedback at scale.