A User-Adaptive Self-Proclamative Multi-Agent Based Recommendation System Design for E-Learning Digital Libraries

Ponnusamy, R.; Gopal, T. Venu

doi:10.1109/iccis.2006.252279

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…For example, the interactivity kiosk collects visitors' responses to a learning test, that is, wrong or right answer (Haverkamp and Gauch, 1998;Zaiane, 2002;Furukawa et al, 2003;Ponnusamy and Gopal, 2006). After connecting each subject covered by the visitors, their learning habits are estimated.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Experiences with RFID-based interactive learning in museums

Huang

Chang

Sandnes

2010

IJAACS

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Tourism plays an important role in the economies of many countries. Tourism can secure employment, foreign exchange earnings, investment and regional development. To attract more tourists and local visitors, many stakeholders such as natural parks, museums, art galleries, hotels and restaurants provide personalised services to meet individual needs. With the increasing number of tourists comes an increased demand for guides at education-oriented leisure centers. Each provided needs unique way to present their services. In this study, these educational leisure centres are coarsely divided into art and science. This paper introduces the architecture of the proposed guide system including a PDA-based recommendation guide for art museums and an Radiofrequency identification-based interactive learning system using collaborative filtering technology for science and engineering education. Evaluations of the two systems reveal that the system inspires and nurtures visitors' interest in science and arts.

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Section: Collaborative Filteringmentioning

confidence: 99%

Experiences with RFID-based interactive learning in museums

Huang

Chang

Sandnes

2010

IJAACS

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“…In [17] proposed architecture has agent for each subject is specific including expression extraction and user interface using ACM CR classification hierarchy. Concept dictionary contains a list of concepts.…”

Section: Multi Agent Architecture For Intelligent E-learningmentioning

confidence: 99%

Intelligent Agent Based Architectures for E-Learning System: Survey

Arif¹,

Hussain²

2015

IJUNESST

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“…For example, the interactivity kiosk collects visitors' responses to a learning test, i.e., wrong or right answer [8,10,13,18]. After connecting each subject covered by the visitors, their learning habits are estimated.…”

Section: Collaborative Filteringmentioning

confidence: 99%

RFID-Based Interactive Learning in Science Museums

Huang

Chang

Sandnes

Ubiquitous Intelligence and Computing

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Science and engineering education play a very important role in the industrial development of a country. Although most people are aware of the importance of science, students often find science boring and uninteresting as traditional textbooks only provide static information. Students who achieve better usually have practical experiences related to the subject or have been exposed to interactive learning tools. In order to promote science education development, a learning system based on RFID is proposed to provide personalized learning service. This paper introduces the architecture of the proposed service and the technologies used including RFID and collaborative filtering. The contributions of the proposed system and how the profiles are used to provide personalization are also discussed. Evaluations of the system reveal that the system inspires and nurtures their interest in science.

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A User-Adaptive Self-Proclamative Multi-Agent Based Recommendation System Design for E-Learning Digital Libraries

Cited by 6 publications

References 11 publications

Experiences with RFID-based interactive learning in museums

Experiences with RFID-based interactive learning in museums

Intelligent Agent Based Architectures for E-Learning System: Survey

RFID-Based Interactive Learning in Science Museums

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