Automation

SCORM Localization: The E-Learning Translation Gap

E-learning localization looks simple on the surface: extract the text, translate it, put it back. In practice, SCORM packages are complex containers with dozens of content types, each requiring different handling.

Organizations discover this when their translated courses don’t work—quizzes broken, interactions failing, audio unchanged, images still showing source language text.

What’s actually in a SCORM package

A SCORM package (SCORM 1.2 or SCORM 2004) typically contains:

HTML content pages. The main learning content, often with embedded JavaScript for interactions.

Connect Your TMS Without Switching: The Connector Approach

Your organization uses Phrase. Or Lokalise. Or MemoQ. Or one of dozens of other translation management systems. The workflows are established, teams are trained, data lives there.

Now you need a capability your TMS doesn’t have—advanced AI translation, cross-lingual QA, video dubbing. Do you switch platforms?

Switching is painful. Connectors offer an alternative.

The platform switch problem

Changing TMS platforms involves:

Data migration. Translation memories, termbases, project history—all needs extraction and import. TM quality often degrades in translation between formats.

WordPress Multilingual Without Plugins

You want your WordPress site in multiple languages. The standard solution: install a multilingual plugin like WPML or Polylang, then translate content within WordPress.

This works. It also adds complexity to your WordPress installation, potentially slows your site, and ties your translation workflow to WordPress-specific tooling.

There’s another approach: translate WordPress content through a dedicated translation platform, connected via API.

The plugin overhead problem

Multilingual WordPress plugins add:

Database complexity. Additional tables for translations, language relationships, and translation settings.

GitHub-to-Translation: Developer-First Localization

Developers live in Git. Pull requests, branches, commits, code review—the entire workflow centers on version control. Then localization happens, and suddenly there’s a disconnect: export files, upload to translation platform, wait, download, commit back to repo.

This handoff process is where localization breaks down for developer-driven products.

The developer localization friction

Typical developer-localization workflow:

1. Developer changes string in code
2. Someone remembers to export strings to translation platform
3. Translation platform processes the change
4. Someone remembers to download translations
5. Developer commits translations back to repo
6. Repeat for every change, in every branch

The “someone remembers” steps are where things break. Strings get changed but not translated. Translations complete but don’t make it back to the repo. Branches diverge with different translation states.

Google: auto-translated content not indexed

In a recent Office Hours stream, Google’s John Mueller set out how translating content automatically, especially at scale, can lead to manual actions on behalf of Google’s Web Spam Team, such as de-indexing machine-translated (MT) content.

He confirms translated content is not counted as duplicate content, but then goes into further detail on best practices for translation. Namely, avoiding blind use of their own Google Translate tool.

This is a good sign for search in general, showing willing to clean up the SERPs. Just as with their frequent algorithm updates which have managed to de-index swathes of content mills doing all kinds of programmatic “creation”.

How to do Translation SEO the right way

What is Translation SEO?

Translation SEO is quite simply the optimisation and re purposing of existing content for new markets. It is an easy win for marketing teams looking to gain a foot hold in adjacent foreign markets.

Companies and organisations with strong content marketing strategies can make their content work twice as hard by translating it for new audiences.

By selecting their best performing pieces and thoroughly researching the correct use of the equivalent keywords for the new market, companies can rely on the ideas and traction that the the top pieces have proven over time in their data analytics.