Automatic Learning Object Extraction and Classification in Heterogeneous Environments

Gil, Ana Belén; Prieta, Fernando De la; Rodrı́guez, Sara

doi:10.1007/978-3-642-19917-2_14

Cited by 18 publications

(11 citation statements)

References 10 publications

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“…To estimate these issues, a set of queries were analyzed, in two repositories: LORNET 1 and Merlot. 2 For this reason the search patterns or topics were chosen at random among topics in English for Science and Technology of UNESCO codes developed by experts. These topics are mainly composed of sets of words (longer queries) due to the specific nature of this terminology, but single words (short queries) were also used to assess different behaviors on the search engines repositories.…”

Section: Experiments and Conclusionmentioning

confidence: 99%

“…Thus, the objective of this study is to present AIREH tool (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments) [2] which makes it possible to search and recover educational resources encapsulated in the form of a LO. Similarly, a system can use a Case-Based Reasoning (CBR) system to recommend which educational resources might be of particular interest to the user, based on information from previous searches.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Approach to Retrieve Learning Resources Over the Cloud

Prieta

Gil

Rodrı́guez

et al. 2013

The 2nd International Workshop on Learning Technology for Education in Cloud

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This study proposes AIREH (Architecture for Intelligent Retrieval of Educational content in Heterogeneous Environments) that is a model for the development of digital content retrieval based on the paradigm of virtual organizations of intelligent agents Learning objects have made it possible to create digital resources that can be reused in various didactic units. These resources are stored in repositories, and thus require a search process that allows them to be located and retrieved.

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Section: Experiments and Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enhanced Approach to Retrieve Learning Resources Over the Cloud

Prieta

Gil

Rodrı́guez

et al. 2013

The 2nd International Workshop on Learning Technology for Education in Cloud

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“…This work presents the evolution of platform CLOR [3], by means of the integration of the federated searcher AIREH [9] in order to produce a clear advantage in the context of LO paradigm. On the one hand, CLOR (Cloud-based Learning Object Repository) is the present of a new generation of LOR because it is deployed into a cloud platform and makes use of the advantages of this computational paradigm (non-SQL databases, unlimited storage, etc.).…”

Section: Introductionmentioning

confidence: 99%

Learning Object Repositories with Federated Searcher over the Cloud

Prieta

Gil

Martín

et al. 2014

Advances in Intelligent Systems and Computing

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“…Once the service agents return their results, the master agent combines the results and displays them to users. In AIREH (Architecture for Intelligent Recovery of educational content in Heterogeneous Environments) [9], an intermediary communication point is proposed for retrieving learning objects from heterogeneous environments. The intermediary communication point is responsible for sending user requests to different repositories and then merging the returned ranked results.…”

Section: Related Workmentioning

confidence: 99%

“…Moodle, Mahara) maps its data into the 3A model and offers REST (Representational State Transfer) 9 APIs that are called by the harvester to get data in XML or JSON (JavaScript Object Notation) 10 formats.…”

Section: Fig 1 Common Data Modelmentioning

confidence: 99%

A Federated Recommender System for Online Learning Environments

Zhou

Helou

Moccozet

et al. 2012

Advances in Web-Based Learning - ICWL 2012

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Abstract. From e-commerce to social networking sites, recommender systems are gaining more and more interest. They provide connections, news, resources, or products of interest. This paper presents a federated recommender system, which exploits data from different online learning platforms and delivers personalized recommendation. The underlying educational objective is to enable academic institutions to provide a Web 2.0 dashboard bringing together open resources from the Cloud and proprietary content from in-house learning management systems. The paper describes the main aspects of the federated recommender system, including its adopted architecture, the common data model used to harvest the different learning platforms, the recommendation algorithm, as well as the recommendation display widget.

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Automatic Learning Object Extraction and Classification in Heterogeneous Environments

Cited by 18 publications

References 10 publications

An Enhanced Approach to Retrieve Learning Resources Over the Cloud

An Enhanced Approach to Retrieve Learning Resources Over the Cloud

Learning Object Repositories with Federated Searcher over the Cloud

A Federated Recommender System for Online Learning Environments

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