Heting Mao
IkanRiddle
·
AI & ML interests
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Recent Activity
reacted
to
kanaria007's
post
with ❤️
about 18 hours ago
✅ New Article: *Measuring What Matters in Learning* (v0.1)
Title:
📏 Measuring What Matters in Learning: GCS and Metrics for Support Systems
🔗 https://huggingface.co/blog/kanaria007/measuring-what-matters-in-learning
---
Summary:
Most “AI for education” metrics measure *grades, time-on-task, and engagement*.
That’s not enough for *support systems* (tutors, developmental assistants, social-skills coaches), where the real failure mode is: *the score goes up while the learner breaks*.
This guide reframes learning evaluation as *multi-goal contribution*, tracked as a *GCS vector* (mastery, retention, wellbeing/load, self-efficacy, autonomy, fairness, safety) — and shows how to operationalize it without falling into classic metric traps.
> If you can’t measure wellbeing, fairness, and safety,
> you’re not measuring learning — you’re measuring extraction.
---
Why It Matters:
• Moves beyond “grading” into *support metrics* designed for real learners
• Makes *wellbeing, autonomy, fairness, and safety* first-class (not afterthoughts)
• Separates *daily ops metrics* vs *research evaluation* vs *governance/safety*
• Turns “explainability” into *answerable questions* (“why this intervention, now?”)
---
What’s Inside:
• A practical *GCS vector* for learning & developmental support
• How core metrics translate into education contexts (plan consistency, trace coverage, rollback health)
• A tiered metric taxonomy: *Ops / Research / Safety*
• Parent-facing views that avoid shaming, leaderboards, and over-monitoring
• Pitfalls and failure patterns: “optimize test scores”, “maximize engagement”, “ignore fairness”, etc.
---
📖 Structured Intelligence Engineering Series
Formal contracts live in the evaluation/spec documents; this is the *how-to-think / how-to-use* layer.
reacted
to
kanaria007's
post
with 🤗
about 18 hours ago
✅ New Article: *Measuring What Matters in Learning* (v0.1)
Title:
📏 Measuring What Matters in Learning: GCS and Metrics for Support Systems
🔗 https://huggingface.co/blog/kanaria007/measuring-what-matters-in-learning
---
Summary:
Most “AI for education” metrics measure *grades, time-on-task, and engagement*.
That’s not enough for *support systems* (tutors, developmental assistants, social-skills coaches), where the real failure mode is: *the score goes up while the learner breaks*.
This guide reframes learning evaluation as *multi-goal contribution*, tracked as a *GCS vector* (mastery, retention, wellbeing/load, self-efficacy, autonomy, fairness, safety) — and shows how to operationalize it without falling into classic metric traps.
> If you can’t measure wellbeing, fairness, and safety,
> you’re not measuring learning — you’re measuring extraction.
---
Why It Matters:
• Moves beyond “grading” into *support metrics* designed for real learners
• Makes *wellbeing, autonomy, fairness, and safety* first-class (not afterthoughts)
• Separates *daily ops metrics* vs *research evaluation* vs *governance/safety*
• Turns “explainability” into *answerable questions* (“why this intervention, now?”)
---
What’s Inside:
• A practical *GCS vector* for learning & developmental support
• How core metrics translate into education contexts (plan consistency, trace coverage, rollback health)
• A tiered metric taxonomy: *Ops / Research / Safety*
• Parent-facing views that avoid shaming, leaderboards, and over-monitoring
• Pitfalls and failure patterns: “optimize test scores”, “maximize engagement”, “ignore fairness”, etc.
---
📖 Structured Intelligence Engineering Series
Formal contracts live in the evaluation/spec documents; this is the *how-to-think / how-to-use* layer.
reacted
to
kanaria007's
post
with 🤗
about 18 hours ago
✅ New Article: *Designing, Safeguarding, and Evaluating Learning Companions* (v0.1)
Title:
🛡️ Designing, Safeguarding, and Evaluating SI-Core Learning Companions
🔗 https://huggingface.co/blog/kanaria007/designing-safeguarding-and-evaluating
---
Summary:
Most “AI tutoring” talks about prompts, content, and engagement graphs.
But real learning companions—especially for children / ND learners—fail in quieter ways: *the system “works” while stress rises, agency drops, or fairness erodes.*
This article is a practical playbook for building SI-Core–wrapped learning companions that are *goal-aware (GCS surfaces), safety-bounded (ETH guardrails), and honestly evaluated (PoC → real-world studies)*—without collapsing everything into a single score.
> Mastery is important, but not the only axis.
> *Wellbeing, autonomy, and fairness must be first-class.*
---
Why It Matters:
• Replaces “one number” optimization with *goal surfaces* (and explicit anti-goals)
• Treats *child/ND safety* as a runtime policy problem, not a UX afterthought
• Makes oversight concrete: *safe-mode, human-in-the-loop, and “Why did it do X?” explanations*
• Shows how to evaluate impact without fooling yourself: *honest PoCs, heterogeneity, effect sizes, ethics of evaluation*
---
What’s Inside:
• A practical definition of a “learning companion” under SI-Core ([OBS]/[ID]/[ETH]/[MEM]/PLB loop)
• GCS decomposition + *age/context goal templates* (and “bad but attractive” optima)
• Safety playbook: threat model, *ETH policies*, ND/age extensions, safe-mode patterns
• Teacher/parent ops: onboarding, dashboards, contestation/override, downtime playbooks, comms
• Red-teaming & drills: scenario suites by age/context, *measuring safety over time*
• Evaluation design: “honest PoC”, day-to-day vs research metrics, ROI framing, analysis patterns
• Interpreting results: *effect size vs p-value*, “works for whom?”, go/no-go and scale-up stages
---
📖 Structured Intelligence Engineering Series
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