A Model for a Collaborative Recommender System for Multimedia Learning Material

Baloian, Nelson; Galdames, Patricio; Collázos, César A.; Guerrero, Luis A.

doi:10.1007/978-3-540-30112-7_24

Cited by 27 publications

(20 citation statements)

References 9 publications

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“…Another recommendation system, proposing suggestions of learning materials, was presented in Baloian et al (2004). The authors presented a collaborative filtering recommendation system that suggests suitable multimedia material for learners according to their profiles and the technical environment features.…”

Section: Overview Of Tel Recommendation Systems Based On the Proposedmentioning

confidence: 99%

“…When comparing the main features of existing TEL recommendation systems (Zaïane 2002;Tang and McCalla 2003a;Shen and Shen 2004;Baloian et al 2004;Lu 2004;Wang and Shao 2004;Romero et al 2007;Wan et al 2008;Khribi et al 2008Khribi et al , 2013Zhuhadar et al 2010;Manouselis et al 2011), etc., one can notice a shared composition in terms of their most essential phases, namely the data acquisition, modeling, and recommendation phases.…”

Section: Tel Recommendation Systemsmentioning

confidence: 99%

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Recommendation Systems for Personalized Technology-Enhanced Learning

Khribi¹,

Jemni²,

Nasraoui

2014

Ubiquitous Learning Environments and Technologies

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From e-commerce to e-learning, recommendation systems have given birth to an important and thriving research niche and have been deployed in a variety of application areas over the last decade. In particular, in the technologyenhanced learning (TEL) field, recommendation systems have attracted increasing interest, especially with the rise of educational data mining and big data learning analytics. Generally, TEL recommendation systems are used to support learners in locating relevant educational content according to their profiles. These systems may involve several phases, such as data acquisition and preparation, modeling, and recommendation computation, phases, which together, can describe a TEL recommendation system and distinguish it from others. However, such a description needs to be expanded and generalized in order to cover most of the TEL recommendation systems, especially, in the context of anywhere and anytime learning based on various Web-based learning environments, including Learning Object Repository (LOR), Open Courseware (OCW), Open Educational Resources (OER), Learning Management Systems (LMS), Massive Open Online Courses (MOOC), Educational Widgets, Educational Mobile applications, etc. In this chapter, we provide a generic meta-level framework for a common description of TEL recommendation systems. Then, we present an analysis of several existing TEL recommendation systems with respect to our defined framework.

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Section: Overview Of Tel Recommendation Systems Based On the Proposedmentioning

confidence: 99%

Section: Tel Recommendation Systemsmentioning

confidence: 99%

Recommendation Systems for Personalized Technology-Enhanced Learning

Khribi¹,

Jemni²,

Nasraoui

2014

Ubiquitous Learning Environments and Technologies

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“…Learning Management Systems (LMS) are software packages to enable the management of educational content and also integrate tools that support most of groupware needs, such as e-mail, discussion forums, chat, virtual classrooms, and so on (Baloian et al, 2004). Over the last years, a great amount of full-featured Web based LMS systems have appeared in the marketplace offering designers and instructors generic, powerful userfriendly layouts for the easy and rapid creation and organisation of courses and activities, which can then be customised to the tutor's needs, learners' profile and specific pedagogical goals.…”

Section: Learning Management Systemsmentioning

confidence: 99%

Using trustworthy web services for secure e-assessment in collaborative learning grids

Caballé

Miguel

Xhafa

et al. 2017

IJWGS

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Abstract:The paper presents innovative trustworthy services to support secure e-assessment in web-based collaborative learning grids. Although e-Learning has been widely adopted, there exist still drawbacks which limit their potential. Among these limitations, we investigate information security requirements in on-line assessment learning activities, (e-assessment). In previous research, we proposed a trustworthiness model to support secure e-assessment requirements for e-Learning. In this paper, we present effective applications of our approach by integrating flexible and interoperable Web based secure e-learning services based on our trustworthiness model into e-assessment activities in on-line collaborative learning courses. Moreover, we leverage Grid technology to meet further demanding requirements of collaborative learning applications in terms of computation performance and management of large data sets, in order for the trustworthy collaborative learning services to be continuously adapted, adjusted, and personalised to each specific target learning group. Evaluation in a real context is provided while implications of this study are eventually remarked and discussed.Keywords: information security; trustworthiness; assessment; prediction; online collaborative learning; CSCL; service-oriented architecture; SOA; web services; grid technology. 50 S. Caballé et al.Reference to this paper should be made as follows: Caballé, S., Miguel, J., Xhafa, F., Capuano, N. and Conesa, J. (2017) Nicola Capuano is research assistant at the Department of Information Engineering, Electric Engineering and Applied Mathematics of the University of Salerno. His main research interest is artificial intelligence and, among its applications, intelligent tutoring systems, knowledge representation and educational data mining. He works as a project manager and research consultant within several research and development projects. He is author of about 100 scientific papers. He is scientific referee and member of editorial boards for international journals and conferences.Jordi Conesa is an associate professor of information systems at the Open University of Catalonia. He received his PhD in software engineering from the Technical University of Catalonia. His research interest concerns the areas of conceptual modelling, ontologies, semantic web, knowledge-based systems and e-learning. His long-term goal is to develop methodologies and tools to use ontologies effectively in several application domains, such as conceptual modelling, software engineering and e-learning. He has authored more than 50 research papers, participated in several research projects, including EU FP7, CICYT and AVANZA funded projects, and contributed to the organisation of some international conferences.

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“…Typically, this kind of systems falls in the category of recommender systems based on complex recognition pattern methods or user profile analysis [7,1]. Other systems assist users to generate LSQueries.…”

Section: Lom-semantic-based Retrieval Of Learning Objectmentioning

confidence: 99%

“…More advanced exploitation systems called recommender systems make use of the experience and opinion of other people having already used this material (see [5,6] for a sample). Baloian et al [7] use LOM and user/system modeling as a base of a collaborative recommender system for learning material. Duval and Hodgins [1] suggest a collaborative filtering system based on rating and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Taking Advantage of LOM Semantics for Supporting Lesson Authoring

Motelet

Baloian

2005

On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshops

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Abstract. Learning Object Metadata (LOM) is an interoperable standard focused on enabling the reuse of learning material for authoring lessons. Nevertheless, few work was done on taking advantage of LOMsemantics to facilitate retrieval of learning material. This article suggests an original approach which uses the structure of a lesson in order to automatically generate LOM-semantic-based queries whereas the user continues to formulate easy-to-write queries without semantic specifications. This proposal consists of a four-component framework attempting to consider the main issues of semantic-based retrieval of documents.

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A Model for a Collaborative Recommender System for Multimedia Learning Material

Cited by 27 publications

References 9 publications

Recommendation Systems for Personalized Technology-Enhanced Learning

Recommendation Systems for Personalized Technology-Enhanced Learning

Using trustworthy web services for secure e-assessment in collaborative learning grids

Taking Advantage of LOM Semantics for Supporting Lesson Authoring

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