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Evaluating Chemistry Teacher’s Questioning Skills in Microteaching Based on Artificial Intelligence (AI) Using an Assessment Rubric
Abstract
This study evaluates the questioning skills of chemistry teachers during microteaching using an AI-assisted assessment rubric. A total of 200 publicly available YouTube videos (2019–2024) were selected using defined criteria: chemistry instruction, teacher–student questioning, Indonesian language, minimum audio clarity of 45 dB, and at least 5 minutes in duration. All videos featured pre-service teachers. Transcripts were generated using Otter.ai and segmented into discrete questioning episodes. Evaluation was performed using Gemini Flash 2.0 (build: 2025.03, temperature: 0.0), a large language model configured via prompt design and anchored exemplars to assess six pedagogical indicators: question type, content relevance, question complexity, wait time, teacher’s response, and student interaction. Each indicator was rated on a 4-point scale. Reliability checks against human-coded samples (n = 40) yielded strong agreement (Cohen’s κ = 0.78). Results showed that 25% of sessions were classified as high-performing, with open-ended and cognitively demanding questions, extended wait time, and rich student engagement. In contrast, 42% were low-performing, marked by factual recall, short pauses, and minimal interaction. Clustering analysis (Gower k-medoids) identified three distinct performance profiles (average silhouette = 0.41). This AI-based framework enables reliable, scalable, and interpretable evaluation of questioning practices. A prototype feedback tool was developed, providing per-indicator scores, question examples, and suggested improvements supporting formative teacher development. Ethical compliance was ensured through the exclusive use of public, anonymized content.
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DOI: https://doi.org/10.20961/ijpte.v9i2.106168
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