Abstract
Field Computer-implemented systems for detecting employee knowledge deficits from declining task-completion efficiency and elevated error rates in domain-specific workflows, without processing natural language communications or administering formal assessments. Background Employees who have knowledge gaps in a particular domain tend to complete tasks in that domain more slowly and with more errors over time. These implicit behavioral signals are available from standard enterprise system metrics but are not commonly used as inputs to knowledge management systems. The approach described here treats efficiency decline and error rate elevation as primary gap detection signals. Technical Description The system ingests task-completion metrics from enterprise workflow systems. For each task completion event, the system records: employee_id, task_type_id, knowledge_domain_id (derived from a task-to-domain mapping table), start_timestamp, completion_timestamp, error_event_count, and rework_flag (boolean indicating whether the task required correction). Two primary signals are computed per employee per knowledge domain over a rolling evaluation window (default: 30 days). Signal 1 (Efficiency Decline): Task-completion efficiency is computed as expected_completion_time divided by actual_completion_time. The expected time is derived from the peer cohort median for the same task type. An exponentially weighted moving average (EWMA) with a decay parameter of 0.3 smooths individual task variations. A sustained decline below 0.75 (meaning the employee is taking more than 33% longer than the peer median) for two or more consecutive evaluation windows triggers this signal. Signal 2 (Error Rate Elevation): Error rate is computed as error_event_count divided by total_task_count per knowledge domain per evaluation window. A peer-relative z-score is computed. When the z-score exceeds 1.5 for two or more consecutive windows, this signal triggers. A secondary signal (Rework Frequency) tracks how often the employee's completed tasks require correction by a supervisor or system validation. Rework rate exceeding 2 times the peer baseline triggers an additional flag. When both the efficiency decline and error rate elevation signals trigger simultaneously for the same knowledge domain, the system generates a gap record containing: employee_id, knowledge_domain_id, efficiency_ewma, error_rate_zscore, rework_rate, peer_baselines, and evaluation_window_timestamps. Distinguishing Characteristics This system operates on task-completion metrics (time, errors, rework), not on natural language communications. No NLP pipeline is involved. No intent classification, entity extraction, or taxonomy mapping from text occurs. The approach also differs from formal assessment-based gap detection because no quiz, simulation, or evaluation instrument is administered. Gaps are inferred purely from deteriorating operational performance in domain-specific tasks.
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Davis, Kenneth, "Implicit Task Performance Signal Analysis for Employee Knowledge Gap Inference", Technical Disclosure Commons, (March 25, 2026)
https://www.tdcommons.org/dpubs_series/9638