Learning Objects

Learning Object metadata models

Introduction

The main advantage of using learning objects to build learning pathways is modularity. Like Lego bricks, teachers can more easily build flexible learning pathways, adapted to the specific needs or goals of their students. But how can we identify which learning objects can be interchanged with other? In order to do this we need to have well characterised learning objects, i.e. rich metadata. Metadata are all the data describing a learning objects such as its title, description, thumbnail picture, length, learning goal, etc.

Two approaches

Over the last 20 years several data models for Learning Object Metadata were developed. It is very interesting that there are two types of data models for learning objects:

Education-based: these data model are closely linked to the IT system of educational organisations. For exemple the Dublin Core is a set of 15 elements (properties) for describing resources, mainly inherited from how library classified books.
Search engine-based: these data model are closely linked to the development of internet browsing and webpage indexing by search engine. For example, schema.org is a joint initiative by Google and Baidu (and others) to find a common way to describe web resources.

Learning Object Metadata Model

The learning object metadata (LOM) model has its origins in the early 2000s, with the development of the IEEE 1484.12.1-2002 standard by the IEEE Learning Technology Standards Committee (LTSC).[1-3] This standard defined a data model for describing learning objects, which were broadly defined as any digital or non-digital entity that could be used for learning, education, or training.

The LOM data model specified which aspects of a learning object should be described and what vocabularies could be used for these descriptions. It was intended to support the reusability, discoverability, and interoperability of learning objects, particularly in the context of online learning management systems (LMS).

While the LOM standard was widely adopted, including in SCORM specifications for online learning content, some difficulties were encountered by users. To address these challenges and better account for decentralized organizations and open technologies, a new standard called ISO/IEC 19788 “Metadata for Learning Resources” (MLR) was initiated in 2010 within the ISO/IEC JTC1 SC36 committee. This new standard built upon the LOM and provided tools and guidance for transitioning from LOM to MLR.

Schema.org Metadata Model

Schema.org is an initiative launched on June 2, 2011, by major search engines Bing, Google, and Yahoo! (later joined by Yandex in November 2011) to create and support a common set of schemas for structured data markup on web pages.[4-6]

The main objectives were:

To standardize HTML tags for webmasters to create rich results displayed as visual data or infographics on search engine results pages.
To facilitate the semantic web project, making document markup more readable and meaningful to both humans and machines.

The schema.org vocabulary was inspired by earlier formats like microformats, FOAF, and OpenCyc. In 2012, the GoodRelations ontology was integrated into Schema.org.

The data model used by Schema.org is derived from RDF Schema, which in turn was derived from CycL (an ontology language used in the Cyc knowledge base). It has a set of types arranged in a multiple inheritance hierarchy, and a set of properties with defined domains and ranges.

While Schema.org was initially focused on types of entities relevant to search engines, its scope has gradually expanded through community collaboration and extension mechanisms, though it is not intended as a universal ontology.

Conclusion

Modularity through well-characterized learning objects with rich metadata offers adaptable learning pathways. Data models for learning objects have evolved from education-focused metadata like Dublin Core to more comprehensive models like IEEE 1484 and ISO/IEC 19788. Simultaneously, search-engine-based models like Schema.org emerged, enhancing web resource description. Both approaches aim to improve reusability, discoverability, and interoperability of learning objects, catering to diverse educational needs in the digital age.

References

[1] https://en.wikipedia.org/wiki/Learning_object_metadata

[2] https://adlnet.gov/working-groups/standard-for-learning-metadata

[3] https://www.1edtech.org/standards/learning-resource-metadata

[4] https://en.wikipedia.org/wiki/Schema.org

[5] https://schema.org

[6] https://seopressor.com/blog/dublin-core-vs-schemaorg-metadata-comparison

Keywords

educational resource, oer

Learning Object metadata models

Learning Object metadata models

Table of Contents

Introduction

Two approaches

Learning Object Metadata Model

Schema.org Metadata Model

Conclusion

References

Keywords

Version

Licence