interactionscan

InteractionScan is a framework and methodology for collecting, analyzing, and visualizing interaction data within software to model how users engage with digital interfaces. It focuses on end-to-end capture of interaction events and turning that data into actionable usability insights.

Data types include mouse movements, clicks, scrolls, keystrokes, touch gestures, voice commands, API calls between components, and timestamps. Some implementations add gaze data or contextual signals. Data is typically collected through embedded instrumentation, SDKs, or server-side telemetry, with emphasis on privacy and data minimization.

Analytical approaches encompass sequence mining, time-series analysis, graph-based models of state transitions, and machine learning for pattern recognition and anomaly detection. Methods such as n-grams, Markov models, and path decomposition help identify common motifs and friction points.

Outputs often include heatmaps, interaction graphs, transition matrices, and metrics like dwell time, completion rate, error rate, and path efficiency. Dashboards support cohort comparisons and longitudinal tracking.

Applications span UX research, product design iteration, accessibility evaluation, game analytics, and educational software assessment.

Privacy and ethics considerations include informed consent, data anonymization, minimization, local processing when possible, and compliance with GDPR, CCPA, and other regulations. Researchers should account for instrumentation bias and sampling limitations.

Relation to related terms includes user interaction analytics, clickstream analysis, telemetry data analysis, and event-stream processing. InteractionScan is not a single standardized protocol but a generic term used across tools and studies to describe interaction-centric analysis.