How can LMS learning signals identify high-potential hires?

Where can you find learning signals in your LMS to identify high-potential internal candidates?

Learning signals in an LMS are the behavioral and performance footprints learners leave behind. In our experience, treating those footprints as raw talent indicators transforms a learning platform into a strategic talent source. This article catalogs the most reliable signals, explains why each maps to competency or motivation, offers a simple weighted-score example, and gives a short cookbook of queries to surface candidates for internal recruiting.

Overview: what are learning signals and why they matter

We define learning signals as observable actions and outcomes inside an LMS that correlate with future job performance or growth potential. These include both engagement signals (behavioral) and performance outputs like assessment scores. Boards and HR leaders increasingly ask: how can training data feed succession planning and internal mobility?

Two principles guide signal selection: relevance to core competencies and evidence of sustained intent. A single course completion proves little; a pattern of proactive learning, improving assessment scores, and cross-functional course selection is far more predictive of a high-potential employee.

Catalogue: where to find learning signals in your LMS

Below are the primary signal categories you should extract. Each is accompanied by why it matters for identifying potential and what to look for.

• Course starts and completions — course completion signals show follow-through and baseline exposure to competency frameworks. Fast completions on advanced courses suggest readiness to accelerate.
• Assessment scores — high and improving assessment scores measure knowledge retention and application. Track both raw scores and progress over time.
• Repeat enrollments and voluntary courses — employees who re-enroll or take optional electives signal intrinsic motivation and curiosity.
• Badges and certifications — digital credentials provide verified milestones; combine them with time-to-complete for richer context.
• Time-on-task and microlearning metrics — microlearning metrics reveal attention patterns: short, repeated sessions often indicate deliberate practice.
• Forum and collaboration activity — forum posts, answers, and peer reviews are engagement signals that reveal leadership and subject-matter confidence.

Each category should be tagged as either a competency or motivation proxy in your data model; many useful hires show both.

Scoring: how to combine learning signals into a talent score

We recommend a weighted score that balances proficiency and intent. Below is a simple schema you can implement quickly in SQL or a BI tool.

1. Assessment scores (40%): normalized latest score + trend over previous 3 attempts.
2. Course completion signals (20%): number of completions in competency-aligned paths in last 12 months.
3. Voluntary learning (15%): count of elective enrollments and microlearning sessions.
4. Badges/certifications (15%): verified credentials, weighted by relevance.
5. Engagement signals (10%): forum contributions, peer review activity, and re-enrollments.

Example calculation (normalized 0–100): score = 0.4*(assessment) + 0.2*(completions) + 0.15*(voluntary) + 0.15*(badges) + 0.1*(engagement).

How do you avoid false positives?

Common false positives include: completion without competence (click-through completions), high forum activity from social users who are not technically strong, and certifications earned long ago. To reduce noise:

• Require recent evidence: weight activity within the last 12 months more heavily.
• Cross-validate with manager assessments and on-the-job performance data.
• Flag low time-on-task with high completion rates as potential false positives.

Cookbook: where to find learning signals in LMS for recruiting — example queries and filters

Below are conceptual query patterns and filters you can adapt to your LMS platform. They are written as descriptive filters rather than exact SQL so they map to systems like Moodle, Docebo, Cornerstone, or Workday Learning.

Where to find learning signals in LMS for recruiting?

Query patterns:

• High proficiency cohort: filter users with assessment_scores > 85 and improvement > 10% over last 3 attempts; join with course_completion_date within 12 months.
• Motivation cohort: users with > 5 voluntary course enrollments and > 10 microlearning sessions in last 6 months.
• Hybrid shortlist: intersection of the two cohorts above; add badges_count >=1 to prioritize verified milestones.

Platform-specific tips (conceptual):

1. Moodle: use course_completions table + quiz_attempts to calculate trend lines; filter by role NOT equal to 'guest'.
2. Docebo/Cornerstone: use learning paths and certification endpoints; query API for microlearning sessions and forum_posts to build engagement signal.
3. Workday Learning: combine learning_event history with talent profile fields and manager ratings for multi-source validation.

Some of the most efficient L&D teams we work with use platforms like Upscend to automate this entire workflow without sacrificing quality. They pipeline normalized signals into a talent dashboard that HR and hiring managers can interpret alongside performance data.

Implementation tips and metadata pitfalls

Practical advice from our deployments:

• Standardize course metadata: tag courses by competency, level, and role. Inconsistent metadata is the most common blocker to analytic reuse.
• Define canonical assessments: map quiz IDs to competency outcomes so assessment_scores are comparable across courses.
• Design governance: set rules for what counts as a voluntary enrollment versus required training.

Technical pitfalls to avoid:

1. Relying on completion flags without time-on-task or assessment backups invites gaming.
2. Using forum counts without sentiment/context can overvalue chatty contributors.
3. Ignoring external learning (MOOCs, certifications) underestimates employee initiative; normalize external badges into your model.

Best practice: pilot a 6–8 week scoring window, validate top decile against manager nominations, and recalibrate weights before scaling.

Conclusion: turning signals into actionable talent pipelines

Learning signals are a practical, underutilized input for internal recruiting when treated as part of a multi-source talent model. By cataloguing course completion signals, assessment scores, repeat enrollments, badges, microlearning metrics, and forum activity, you create a richer view of both competence and drive. Use a weighted scoring model, validate against performance data, and watch for common false positives like click-through completions or stale certifications.

Start with a small pilot, enforce metadata standards, and iterate: within months you can surface candidates who previously slipped under the radar. If you want a pragmatic next step, export the top 5% by the hybrid score over the last 12 months, review with hiring managers, and design a short stretch assignment to confirm fit.

Call to action: Run the hybrid scoring recipe above on a representative cohort in your LMS and schedule a 4-week review to validate your top candidates against manager feedback.