Elizabeth Romero

Multi-model Ontology-Based Hybrid Recommender System in E-learning Domain

This paper introduces a multi-model ontology-based framework for semantic search of educational content in E-learning repository of courses, lectures, multimedia resources, etc. This hybrid recommender system is driven by two types of recommendations: content-based (domain ontology model) and rule-based (learner’s interest-based and cluster-based). The domain ontology is used to represent the learning materials. In this context, the ontology is composed by a hierarchy of concepts and sub-concepts. Whereas, the learner’s ontology model represents a subset of the domain ontology, and the cluster-based recommendations are added as additional semantic recommendations to the model. Combining the content-based with the rule-based provides the user with hybrid recommendations. All of them influenced the re-ranking of the retrieved documents with different weights. Our proposed approach has been implemented on the HyperManyMedia1 platform.

Multi-language Ontology-Based Search Engine

One of the first Multi-Language Information Retrieval (MLIR) systems was implemented in 1969 by Gerard Salton who enhanced his SMART system to retrieve multilingual documents in two languages, English and German. However, the research field of MLIR is still struggling since the majority of information retrieval systems are monolingual and more precisely English-based, even though only 6% of the world’s population native language have as English [14]. This paper presents a Multi-Language Information Retrieval (MLIR) approach that falls into the area of Domain Specific Information Retrieval (E-learning being the domain). The approach we followed is a synergistic approach between (1) Thesaurus-based Approach and (2) Corpus-based Approach. This research has been implemented on a real platform called HyperManyMedia1 at Western Kentucky University.

The Effectiveness of Personalization in Delivering E-learning Classes

In this paper we present an evaluation methodology of personalized delivery of multimedia resources in an e-learning platform that takes advantage of web 2.0 technologies and its standards, such as (i) streaming using RSS and Atom feeds, (ii) searching using meta-data search engine, (iii) delivering using ubiquitous computing and (iv) personalized learning. Our framework consists of the following phases: (1) tracking the preferred delivery mechanism for each student model, (2) measuring the effect of the semantic profile time window parameter, (3) clustering students models based on similarity metrics on the delivery preferences and (4) comparing the results between personalized and non-personalized delivery. One important aspect of our approach is the combination of personalization and the data driven extraction (via clustering) of the students models based on similarity metrics. Our experimental results show that personalized delivery increases the usage of e-learning materials and the percentage of reviewing multimedia resources if these materials are delivered in ways that fit students preferences.