Building a candidate-evaluation platform: architecture, item-authoring, anti‑cheat, scoring, ATS integration, privacy & UX — share concrete artifacts
We’re designing a platform to run programming tasks, situational/behavioral exercises, and domain-specific evaluations that produce actionable hiring signals. I’m looking for concrete, reusable community contributions from people who’ve built or run these systems. Please share first‑hand experience, templates, code, and tradeoffs (not high‑level theory). Useful contributions include:
- Architecture patterns & infra: short examples (e.g., sandboxing/execution engines, autoscaling patterns, event buses, observability), recommended OSS components or vendors for code execution/ sandboxes, common failure modes, and cost tradeoffs.
- Item‑authoring process & artifacts: item blueprints, rubric templates, metadata & versioning schema for item banks, pilot/validation workflows, content review checklists, accessibility & bias review steps.
- Anti‑cheat techniques & tradeoffs: proctoring vs. anti‑plagiarism vs. behavioral signals, specific tools (MOSS/JPlag, browser lockdown, keystroke/voice analytics), false‑positive risks, candidate privacy tradeoffs, and recommended mitigations.
- Scoring approaches & psychometrics: example rubrics for coding tasks and SJTs, unit‑test vs. semantic scoring, composite scoring formulas, IRT vs. classical test theory notes, how you validated predictive validity, and sample thresholds or normalization techniques.
- ATS/HRIS integration patterns: sample SCIM/SSO mappings, webhook payloads, API contracts to push scores and artifacts, event models (invite → started → submitted → scored), error handling and idempotency practices.
- Privacy, compliance & security safeguards: consent flows, retention policies, encryption, data minimization, audit logging, vendor diligence (processor agreements), SOC2/GDPR/EEA considerations, and anonymization approaches for analytics.
- Candidate UX choices: instruction templates, estimated time & pacing, save/resume behavior, accommodation workflows, feedback policies, and examples that reduced drop‑off rates.
- Metrics & monitoring: concrete metrics you track (completion rate, time‑to‑submit, item variance/DIF, reliability, predictive validity, plagiarism rate, candidate NPS), dashboards/alerts, and thresholds you found actionable.
- OSS tools & vendors you trust (and why): name, short note on where they fit best, and contract/operational caveats.
- Common pitfalls: short, concrete examples of things that caused production incidents, legal complaints, or biased outcomes and how you fixed them.
What to paste or link: rubric JSONs, item metadata schemas, sample webhook/SCIM payloads, code snippets (Judge0, runner containers, sandbox orchestration), dashboard screenshots, or vendor evaluation checklists. Please note your role (engineer, assessment author, security lead, TA/PM, vendor) and whether the contribution is from internal tooling, a purchased vendor, or open source. I will collect and summarize useful artifacts into a community thread if there’s interest.