SDAS results analysis, the PIE cycle, eAdmin paperwork, and what numbers can honestly support
A mark sheet tells you who scored what; a Student Data Analysis System answers why, what the trend is and what to do next. Eight questions show how standard scores, percentiles, progress rates, multi-year tracking and teacher analysis are used — and how AI natural-language queries let every teacher ask.
Keep readingHong Kong teachers spend about 8–10 hours a week on paperwork. Six eAdmin scenarios show how AI drafts eNotice, eCircular, parent letters, meeting minutes, event proposals and iPortfolio comments — and why grounding in the school knowledge base and matching the school's voice decide whether a first draft is usable.
Keep readingEvery school runs Plan–Implement–Evaluate, but evaluation often relies on impressions. See how PIE KPIs link directly to platform data — AI usage, objective mastery, teacher training records, SDAS results — so planning has a baseline, implementation has tracking, evaluation has evidence, and grant interim and final reports export from the same cycle.
Keep readingSpecs, pricing, release order and support status in this system for 15 model providers
Keep reading28 lessons of teacher AI literacy: Transformers and the three training stages, then RAG, quantisation and running a model on the school network
Keep reading18 lessons on prompting for real classroom work: planning, question setting, marking, tiering, and what students themselves need to understand
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