The ClassTrack Suite transforms classroom recordings into structured insights. It uses AI to analyze sessions, applies rubrics, and generates feedback reports for teachers, principals, parents, and admins.
Step-by-Step Flow
1
Input (Upstream)
- Classroom audio session uploaded or streamed.
- Teacher/student metadata attached (class, subject, grade).
- Custom or standard rubrics configured.
2
AI Processing
- Speech-to-text and speaker identification.
- Analyze talk-time balance, questioning styles, pacing, sentiment, engagement.
- Apply rubric dimensions (pedagogy, management, engagement).
- Generate observation scores and highlight strengths/gaps.
3
Outputs (Downstream)
- Teacher Growth Report → feedback + micro-training suggestions.
- Principal Dashboard → aggregated classroom insights.
- Parent Engagement Report → simple summaries of student participation.
- Leader/Admin Analytics → school-wide trends, compliance, and governance.
Output Types
- ✅ Teacher Feedback Pack (growth strategies + reflection prompts)
- ✅ Principal Dashboard (class-by-class and teacher performance view)
- ✅ Parent Reports (weekly engagement summaries)
- ✅ Admin/Leader Dashboards (aggregate trends + compliance exports)
Ecosystem Integration
- Upstream: Lesson Plans + Worksheets set the instructional baseline.
- Core: ClassTrack analyses how teaching actually happens in classrooms.
- Downstream:
- Teacher Action Plans for reteaching strategies.
- AI Studio adjustments to lesson plans & worksheets.
- Evaluation Layer calibration to improve assessments.
Next Step
Next → Explore Use Cases to see how schools, tutoring platforms, LMS/ERPs, and e-learning providers benefit from ClassTrack.FAQ
How does the classroom observation workflow turn audio/video into engagement analytics like talk-time, questioning, and pacing?
How does the classroom observation workflow turn audio/video into engagement analytics like talk-time, questioning, and pacing?
It ingests classroom audio and video, applies speech-to-text and speaker ID to separate teacher and student voices, and then computes engagement analytics such as talk-time balance, questioning frequency, and pacing. Results can be combined with rubric-based scoring and are reviewed via a human-in-the-loop step to target 95% accuracy before publishing.
What inputs and configuration are required to run a classroom observation in CrazyGoldFish?
What inputs and configuration are required to run a classroom observation in CrazyGoldFish?
Provide classroom audio or video and, optionally, an observation rubric to anchor scoring. The workflow processes multimodal inputs with speech-to-text, speaker identification, and engagement analytics; human-in-the-loop review and audit logs are built in for oversight and trust.
What does the observation output include and how is it used downstream?
What does the observation output include and how is it used downstream?
Outputs include rubric-aligned scores (when configured) plus engagement analytics—talk-time, questioning, pacing—aligned to CBSE/ICSE/GDPR standards. These insights surface in compliance-ready dashboards for leaders and feed downstream personalization and content generation to refine reteach strategies and materials.
How do human-in-the-loop reviews work for classroom observations?
How do human-in-the-loop reviews work for classroom observations?
After AI generates suggested metrics and rubric scores, educators review, edit, and approve results with full audit logs. Rechecks can be triggered where needed to maintain transparency and keep outcomes near the 95% accuracy target.
Can we customize observation rubrics and calibrate them over time?
Can we customize observation rubrics and calibrate them over time?
Yes. Institution-specific rubrics can be mapped to the observation workflow, and human-in-the-loop review plus calibration tools help align outcomes to instructional norms while preserving traceable audit trails.
How are teacher–student talk-time and questioning patterns detected reliably?
How are teacher–student talk-time and questioning patterns detected reliably?
The workflow uses speech-to-text and speaker ID to attribute speech segments, then computes talk-time balance and questioning frequency as part of engagement analytics. Educator review remains in the loop to correct edge cases and sustain results near 95% accuracy.
How does ClassTrack fit into the classroom observation workflow?
How does ClassTrack fit into the classroom observation workflow?
ClassTrack captures classroom signals—talk-time balance, questioning, pacing—during observations and feeds them back to refine rubrics and instructional materials. This creates a closed loop where Observe insights continuously improve coaching and generated content under human-in-the-loop oversight.
What integration options exist to embed classroom observation in our LMS/ERP?
What integration options exist to embed classroom observation in our LMS/ERP?
You can integrate via a white-label embeddable UI for seamless embedding, or use REST APIs and webhooks to orchestrate ingestion, evaluation, and reporting. Both approaches are CBSE/ICSE/GDPR aligned and designed so there’s no AI team required to launch and operate.
How does the observation workflow support governance and compliance at scale?
How does the observation workflow support governance and compliance at scale?
Workflows are CBSE/ICSE/GDPR aligned with compliance-ready dashboards, role-based oversight, and audit logs that preserve decision history. Leaders gain visibility into observation volume and trends while educators remain in control via human-in-the-loop approvals.
How do observation insights influence personalization and lesson planning?
How do observation insights influence personalization and lesson planning?
Observation metrics and rubric scores inform downstream Personalization and AI Studio, producing reteach strategies and updated lesson materials grounded in actual classroom dynamics. This keeps the Evaluate–Observe–Generate loop aligned to curriculum goals and improves over time.