Dashboard-monitoring is a newly observed orchestration type in which the teacher uses real-time AI dashboards to grasp student status and adjust lesson direction and pace (14 of 46 episodes)¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q1
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q1 i? In dashboard-monitoring orchestration, the teacher monitors students' emotional states, connection status, and answer rates in real time via dashboards and reflects this in lesson operation; it was observed 14 times, the second most frequent type. → Yun Hwa Noh & Hee-jeong Kim 2026

Evidence¶

Yun Hwa Noh & Hee-jeong Kim 2026¶

Yun Hwa Noh & Hee-jeong Kim. (2026). Exploring teachers' instrumental orchestration and roles in high school mathematics classes using AI-based digital tools. The Mathematical Education 65(2), 309-335. https://doi.org/10.63311/mathedu.26.6525

q1 · i? · qualitative · r3

Qualitative case analysis of lesson recordings and teacher tablet screen captures. The dashboard-monitoring type was defined from observed episodes in which the teacher used AI digital educational materials and Snorkl dashboards; the article reports it was observed 14 times among 46 episodes, second most frequent after explain-the-screen.

"'대시보드-모니터링'은 교사가 대시보드를 통해 학생들의 감정 상태, 접속 현황, 문제 정답률 등을 실시간으로 파악하고 이를 수업 운영에 반영하는 오케스트레이션 유형이다."

Discussion¶

Related Claims¶

• AI-based high school mathematics lessons exhibit four existing and three newly emerging instrumental orchestration types, with the new types appearing in 36 of 46 episodes — a broader claim this one bears on
• AI-mediating is a newly observed orchestration type in which the teacher concretizes or supplements correct AI feedback for students in curriculum context (12 of 46 episodes) — related
• Explain-the-screen was the most frequent orchestration type (24 of 46 episodes), and in AI-based lessons it was dynamically reconfigured in real time by AI-generated learning data — related
• AI-correcting is a newly observed orchestration type in which the teacher identifies erroneous AI feedback, points it out to students, and re-guides or edits AI prompts (10 of 46 episodes) — related
• Learning Analytics Dashboards are the most frequently reported AI system type supporting lecturer decisions, primarily for monitoring, feedback, and assessment — related
• Teacher roles vary by orchestration type, with content-related and ethical roles prominent when the teacher intervenes in AI-generated feedback, and a knowledge-transmitter role emerging in explain-the-screen — related
• Instructor roles shift from exclusive information source toward orchestrating and regulating AI-mediated learning activities — related
• Teacher interviews identified five types of monitored information and, via the Kano method, four highest-priority dashboard indicators for CSCL monitoring — related
• Dashboard support type did not influence detection of CSCL events but did influence teachers' interpretation of the situation — related