## Overview

This article explains how Coassemble defines and tracks **anonymous users**, with a focus on environments where multiple learners may share devices (for example, hospital settings). It also outlines the limitations and risks for statistical reporting when using anonymous access.

## Definition of anonymous users in Coassemble

An **anonymous user** in Coassemble is a learner who accesses course content without providing personally identifiable information (such as an email address) and without a formal user account.

When using anonymous links:

* Learners are not required to log in.
* Coassemble does not collect identifiers like name or email (unless separately requested in the course itself).
* Tracking is based on technical session/device information rather than user identity.

## How anonymous users are tracked

When someone accesses a course via an anonymous link, Coassemble uses available technical information to track that session. Typical signals may include:

* Device/browser profile
* Session or tracking cookies
* IP address and related network information

These signals allow Coassemble to track activity for a **session** or **device profile**, but they do **not** reliably identify a specific individual.

### Unique anonymous sessions vs. unique individuals

Coassemble can:

* Treat a single device/session using an anonymous link as a **unique anonymous session**.

However, Coassemble **cannot guarantee** that:

* Each unique anonymous session corresponds to a unique person.
* Multiple individuals using the same device or network are counted separately.

In other words, uniqueness is at the level of **device/session**, not confirmed human identity.

## Shared devices and IP addresses

In environments like hospitals, it is common for multiple people to share the same computer and/or network. In these cases:

* **Coassemble cannot distinguish between different individuals using the same shared computer via the same anonymous link.**
* Activities from different people on that device may appear as coming from a single anonymous user/session, depending on how the browser and device handle sessions and cookies.

### Risks and effects on analytics

Using anonymous links on shared devices introduces the following risks for analytics and reporting:

* **Merged activity:** Activity from multiple individuals may be merged into one anonymous session if the same browser session or device profile is reused.
* **Double-counting or miscounting:** Metrics for “unique anonymous learners” may:
  * Over-count when one individual uses multiple devices/browsers.
  * Under-count or merge when multiple individuals use the same device/browser.
* **Reduced statistical integrity:** Because of the above, reported numbers may not strictly match the actual count of distinct human learners.

These limitations are inherent to anonymous, device-based tracking and are not fully solvable without collecting identifying information.

## Ensuring more reliable tracking

There is no complete “silver bullet” for guaranteeing both full anonymity and perfectly unique learner counts, especially with shared devices. However, the following options can improve reliability:

### 1. Collect email addresses or identifiers

* Ask learners to provide an **email address** or another unique identifier.
* This allows Coassemble to associate activity with a specific person rather than just a device.

**Limitations:**

* If the same device remains logged in under one learner’s account and another learner uses it without logging out, their activity may still be recorded under the same account.
* Collecting emails or identifiers reduces anonymity.

### 2. Use unique devices/device profiles (where possible)

* Encourage use of **personal devices** (e.g., personal smartphones) instead of shared workstations.
* Ensure that browsers are restarted or sessions cleared between learners on shared devices to reduce merging, though this is not a guarantee.

**Limitations:**

* Even with device separation, one person using multiple devices will inflate the apparent number of unique anonymous learners.

## Implications for research

For projects that require **high statistical integrity** (such as research studies or funded projects):

* Treat Coassemble’s anonymous tracking as a **good-faith approximation** of participation, not an exact headcount of distinct individuals.
* Clearly state in your methodology that:
  * Anonymous access relies on device/session tracking.
  * Shared devices can cause under-counting of unique learners.
  * Multiple devices per person can cause over-counting.
* When strict uniqueness is required, consider:
  * Requiring logins or unique identifiers.
  * Supplementing platform analytics with external data collection (e.g., sign-in sheets, survey tools with unique access codes).

## Summary

* Coassemble defines “unique” anonymous users primarily at the **device/session** level, not at the individual person level.
* On shared computers or IP addresses, Coassemble **cannot reliably distinguish** between different individuals.
* This can lead to **merged activities** and **inaccurate counts** of unique anonymous learners.
* Collecting identifiers (like email) and/or using unique devices improves tracking but may affect anonymity and is not foolproof.

Understanding these technical limitations is essential when designing studies, courses, or reports that depend on accurate counts of unique participants under anonymous access.

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