Classroom Chronological Text Analysis

Problem

Classroom evaluations often rely on vague phrases such as an active atmosphere or a good effect. This skill turns chronological classroom transcripts, teacher-student dialogue, and observation notes into teaching-research writing material. It first identifies the subject, learner level, lesson type, and core task, then extracts teacher behavior, student behavior, learning tasks, question chains, feedback, and learning evidence. Finally, it produces problem diagnosis, improvement suggestions, and article topics. Important judgments should cite specific timestamps, utterances, or classroom episodes rather than evaluate the teacher as a person.

How It Works

The skill supports full, brief, and targeted modes, and can focus on user-specified dimensions. Preprocessing scripts can extract basic statistics: transcript_preprocessor.py counts turns, teacher-student ratios, and questions; classroom_analyzer.py helps identify question types, cognitive levels, and IRE/IRF interaction patterns; generate_report.py creates a report skeleton. Reference files cover Bloom cognitive levels, lesson-type dimensions, article templates, and a quality checklist. The analysis order is fixed: basic identification, segmentation, behavior annotation, task analysis, question chains, interaction and feedback, cognitive level, learning evidence, pacing, alignment, problem diagnosis, improvement suggestions, and article topics. Automated classification is heuristic and requires human verification.

Boundaries

It suits K12 and higher-education classrooms, public lessons, demonstration lessons, and partial fragments. If timestamps, student work, exercise results, or grouping details are missing, the output should state the inference limits. It is intended for teaching-research writing and does not replace close reading of classroom materials.