Business-Strategy-&-Lms-Tech
Upscend Team
-December 31, 2025
9 min read
Accessibility analytics quantify who is affected, where, and how often, turning qualitative tests into operational priorities. Instrument assistive-tech flags, keyboard-only sessions, UI errors, support tickets, and automated-test runs into event-level data, build operational and stakeholder dashboards, and set alert tiers. Use aggregation and pseudonymization to protect privacy.
Accessibility analytics are the quantitative backbone of any modern EdTech accessibility program. In our experience, teams that pair qualitative testing with continuous measurement close accessibility gaps faster and show clear user impact. This article explains which analytics reveal accessibility issues in learning platforms, practical instrumentation to collect those signals, dashboard templates, sample SQL segments and alerting rules, and how to report results to executives and product teams.
Accessibility efforts are often judged by checklists and test runs, but those snapshots miss real-world user impact. Accessibility analytics turns qualitative findings into operational priorities by quantifying who is affected, where, and how often.
We've found that stakeholders respond faster when dashboards show lost time, increased support load, or higher error rates for users relying on assistive technologies. Measuring impact shifts accessibility from a compliance exercise to a product improvement lever.
Product managers, support teams, and instructional designers all need different views of accessibility data. Product teams use technical regressions and error rates, support uses ticket trends, and learning designers watch completion and time-on-task. A coordinated reporting practice ensures each role gets actionable signals.
To answer which analytics reveal accessibility issues in learning platforms, start with metrics that align to user pain and technical failure modes. Below are the high-impact metric categories that consistently reveal issues:
Combine these signals to avoid false positives. A spike in JS exceptions without matching user complaints may be backend; paired with screen-reader sessions that end early signals a high-priority accessibility failure.
Practical thresholds vary by product, but as baseline rules use:
Instrumentation is the most common gap. We've found success when teams intentionally track assistive tech usage, keyboard-only sessions, error rates, support tickets, and accessibility test regressions together.
Start with event-level data that includes a session id, user cohort, page/component id, and accessibility attributes. Below are practical steps to instrument:
Below are example SQL-style segments you can adapt. They assume a session_events table with columns: session_id, user_id, event_type, input_type, error_flag, assistive_flag, component_id, timestamp.
SELECT COUNT(DISTINCT session_id) AS assistive_sessions FROM session_events WHERE assistive_flag = TRUE AND event_date BETWEEN '2025-01-01' AND '2025-01-31';
SELECT SUM(error_flag)::float / NULLIF(COUNT(DISTINCT session_id),0) AS keyboard_error_rate FROM session_events WHERE input_type = 'keyboard' AND event_date BETWEEN '2025-01-01' AND '2025-01-31';
SELECT t.ticket_id, t.created_at, s.session_id FROM tickets t JOIN session_mapping s ON t.session_ref = s.session_ref WHERE t.tags LIKE '%accessibility%';
Dashboards turn raw events into storylines. Build two complementary dashboards: an Operational Health dashboard for engineers and a Stakeholder Impact dashboard for product, support, and leadership.
Operational dashboards focus on trends and incidents; stakeholder dashboards show outcomes and ROI of fixes.
Example dashboard template (columns): Component | Assistive Sessions | Keyboard Errors | JS Exceptions | Support Tickets | Completion Delta
| Component | Assistive Sessions | Keyboard Errors | Tickets | Completion Delta |
|---|---|---|---|---|
| Video Player | 120 | 18 | 9 | -22% |
| Quiz Widget | 85 | 27 | 12 | -30% |
Effective alerts are action-oriented and low-noise. Use rule tiers:
Include runbooks with each alert that list triage steps, affected components, a sample query to pull sessions, and rollback instructions if a release caused regressions.
Reporting practices for accessibility impact measurement must bridge engineers and executives. An effective cadence combines weekly operational notes, monthly product-impact reports, and quarterly executive summaries.
We've used a concise executive one-pager that highlights the most material metrics, recent fixes, and estimated learning outcome improvements. Below is an example structure for a monthly accessibility report.
Example executive report frequency and format:
A pattern we've noticed: the turning point for most teams isn’t just creating more content — it’s removing friction. Tools like Upscend help by making analytics and personalization part of the core process, turning signals into prioritized workstreams that product teams can act on.
When monitoring accessibility issues, teams frequently misinterpret signals or over-collect sensitive data. Two main pain points are user privacy and noisy signals that don't equate to broken accessibility.
Data privacy: never log PII or raw assistive input (e.g., speech content or screen-reader text). Use flags and aggregates: count of assistive sessions, not transcripts. Pseudonymize user_ids and apply retention limits.
Signals like higher error rates can come from performance issues, user training gaps, or accessibility barriers. Use triangulation: pair error spikes with support tickets, session replays (obfuscated), and automated test failures to confirm root cause.
Common mistakes to avoid:
Accessibility analytics is the operational discipline that converts accessibility goals into measurable product outcomes. By instrumenting assistive tech usage, tracking keyboard-only sessions, logging error rates, tagging support tickets, and feeding automated test runs into your analytics warehouse you can detect, prioritize, and measure fixes with confidence.
Start by implementing the event schema and the SQL segments above, build two dashboards (operational and stakeholder), and enforce a triage-runbook for alert rules. Address privacy by aggregating and pseudonymizing data and interpret signals through cross-checks rather than single metrics.
Next step: Run a 30-day audit: capture baseline metrics for assistive sessions, keyboard-only error rates, and accessibility-tagged support tickets. Use that baseline to set SLAs and to prioritize the first three components for remediation.
If you'd like a one-page template or a sample SQL tailored to your data model, request it and we'll provide a customized segment and dashboard layout.