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Presently, searching the internet for learning material relevant to ones own interest continues to be a time‐consuming task. Systems that can suggest learning material (learning objects) to a learner would reduce time spent searching for material, and enable the learner to spend more time for actual learning. The purpose of this paper is to present a system of “hybrid search and delivery of learning objects to learners”.

This paper presents a system of “hybrid search and delivery of learning objects to learners” that combines the use of WordNet for semantic query expansion and an approach to personalized learning object delivery by suggesting relevant learning objects based on attributes specified in the learner's profile. The learning objects are related to the learner's attributes using the IEEE LOM and IMS LIP standards. The system includes a web crawler to collect learning objects from existing learning object repositories, such as NEEDS or SMETE.

The presented HSDLO system has the ability to accurately search and deliver learning objects of interest to a learner as well as adjust the learner's profile over time by evaluating the learner's preferences implicitly through the learning object selections.

Since real LOM's from SMETE are not much populated, the system is tested with a limited set of attributes. The system is evaluated using a test bench rather than real learners.

The paper proposes a combination of three search techniques in one system as well an architectural solution which can be used for other types of online search engines.

The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions.

As proof of concept, a rule set is implemented using the rule markup language (RuleML), a member of XML‐based languages. This representation in RuleML allows the rule base to be platform‐independent, flexibly extensible and executable.

The RuleML source representation is easily converted to other XML‐based languages (such as RDF, OWL and XMI) as well as incorporated into, and extracted from, existing XML‐based repositories (such as IEEE LOM and CanLOM) using XSL Transformations (XSLT).

The RuleML facts and rules represented in the positional slotted language are used by the OO jDREW reasoning engine to detect and map between semantically equivalent components of course outlines as the key step in their interoperation. In particular, this will enable the precise delivery of learning objects (e.g. course outlines) from repositories to a specific learner's context.

Although the particular scenario is discussed in the present paper, the proposed approach can be applied to other tasks related to enabling semantic interoperability.