HR & People Analytics Insights
Upscend Team
-January 11, 2026
9 min read
This article explains how LMS data and learning analytics can identify internal candidates by mapping enrollments, completions, assessments and microlearning to skills, engagement and readiness. It outlines API/CSV extraction, signal-to-skill mapping, a simple scoring model, common data quality fixes, and a governance checklist for piloting internal recruiting workflows.
LMS data is a rich behavioral record that, when organized properly, reveals real-world signals about skills, engagement and readiness for new roles. In our experience, the best internal recruiting pipelines combine learning analytics with HR records so hiring managers can surface candidates who already demonstrate capability and motivation.
This article breaks down the key types of LMS data, explains extraction and validation methods, maps learning signals to candidate attributes, and offers a simple scoring model you can implement now.
Below are the primary learning signals we track and the candidate attributes they best approximate. Each row is a diagnostic: treat the signal as probabilistic evidence, not proof.
Key LMS sources include enrollment logs, LMS reports, assessment results and microlearning interactions. Mapping them properly lets talent teams infer skills, engagement, and learning agility.
Extraction starts with two pragmatic choices: API-first or export-and-ETL. Most modern LMS platforms provide REST APIs for event and user data; older systems allow CSV exports from LMS reports. In our experience, an API pipeline reduces manual errors and enables near-real-time matching.
Key steps: identify the required tables/feeds, schedule incremental pulls, and normalize identifiers so LMS user IDs join cleanly with HRMS employee IDs.
High-level API calls look like this (conceptual):
For CSV exports, include fields: user_id, course_id, enrollment_date, completion_date, score, time_spent, badge_id. Then run joins against HR attributes.
Sample SQL (high-level) to pull recent completions for matching:
SELECT u.employee_id, c.course_code, e.completion_date, a.score FROM enrollments e JOIN users u ON e.user_id = u.id LEFT JOIN assessments a ON e.id = a.enrollment_id JOIN courses c ON e.course_id = c.id WHERE e.completion_date > CURRENT_DATE - INTERVAL '180 days';
Signal-to-skill mapping is the core transformation: it turns noisy events into interpretable attributes. We recommend a layered approach that converts raw LMS data into standardized competency indicators.
Layer 1: event normalization (timestamps, IDs). Layer 2: rule-based mapping (e.g., course tags → skill tags). Layer 3: scoring and decay (older signals weigh less).
Example mapping rules:
Simple scoring model (example, normalize 0–100):
Use a calibrated threshold (e.g., 70+) to flag strong internal candidates, then verify with manager feedback and work samples.
Operationalizing LMS data for internal recruiting means building a short-listing workflow that feeds talent reviews and hiring panels. In our work with L&D and TA teams, we see two common patterns: embedded analytics in the LMS or an external talent-matching dashboard that merges LMS signals with HR data.
Practical steps: create role competency profiles, map LMS courses to those competencies, score the internal population, and present ranked shortlists to hiring managers with transparency about signal provenance.
Some of the most efficient L&D teams we work with use platforms like Upscend to automate this entire workflow without sacrificing quality. These teams treat the LMS as an operational data source—feeding real-time match scores into talent pools and alerts for managers when high-potential employees surface.
Use LMS reports to generate candidate lists by filtering for:
Export the filtered list to a secure HR dashboard, include links to the underlying LMS report rows, and append manager endorsements to create a human-in-the-loop pipeline.
When using LMS data for recruiting, expect noise. Common problems: inconsistent course tagging, duplicate user accounts, missing assessment data, and sessions logged as idle time. We recommend systematic checks to increase trust.
Data quality checklist:
Mitigation patterns:
Successful pilots treat LMS data integration as cross-functional: L&D maintains taxonomy, TA defines role thresholds, and HR ops manages identity joins. Establish simple SLAs for data freshness and model retraining.
Minimum implementation checklist:
Governance notes:
When treated as a first-class data source, LMS data becomes a practical engine for internal mobility. In our experience, teams that normalize events, map courses to competencies, and apply transparent scoring models find more reliable internal matches while reducing time-to-fill.
Start small: pilot one role, standardize the course→skill mapping, and compare shortlisted candidates against traditional sourcing over a 6-month window. Use the checks and scoring approach above to guard against noisy signals and missing assessments.
Ready to try it? Build a simple export or API pull this week, run the sample SQL to produce a scored shortlist, and schedule a validation session with the hiring manager. If you need a pragmatic roadmap, begin with the checklist in this article and measure outcomes after the first two hire cycles.
