Skip to content

ggauthier/lom2mlr

 
 

Repository files navigation

Project Lom2MLR

This project aims to convert Learning Object Metadata, IEEE 1484.12.1-2002 into Metadata for Learning Resources, ISO/IEC 19788-1:2011. Note that part 1 of the standard is publically available. This tool is developed by Groupe de travail québécois sur les normes et standards TI pour l’apprentissage, l’éducation et la formation, and as such we want to cover LOM files that follow the Normetic 1.2 profile.

DOCUMENT TO BE UPDATED (Doesn't conform to MLR as it stand)

This project aims to convert Learning Object Metadata, IEEE 1484.12.1-2002 into Metadata for Learning Resources, ISO/IEC 19788-1:2011 Note that part 1 of the standard is publically available. This tool is developed by Groupe de travail québécois sur les normes et standards TI pour l’apprentissage, l’éducation et la formation, and as such we want to cover LOM files that follow the Normetic 1.2 profile

More documentation is available here.

Usage

The documentation contains installation and usage instructions for the command-line tool. There is also a web application version available here. This web application is available as a web form, or as a RESTful service (at the same address). In that second case, the LOM data can be POSTED, and optional arguments can be passed as a query string.

The command-line tool requires many components, as detailed in the instructions, but packaged binary applications are available for Windows and for Mac OS X. Put it in your executable PATH, and type lom2mlr --help for instructions.

Rationale

Conversion of LOM to MLR is not a linear process, and contains many heuristics. These heuristics are described in the rationale document. The rationale.html file is generated from the rationale.md markdown file, using the lom2mlr_markdown script included in this package.

Besides describing the heuristics, the rationale.md file contains testable fragments of LOM to MLR conversion. Use the nosetest command to execute those tests.

About

Convert LOM metadata to MLR format

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • XSLT 52.4%
  • Python 41.0%
  • Makefile 4.1%
  • CSS 2.5%