Adaptive Navigation Support for Open Corpus Hypermedia Systems

Brusilovsky, Peter

doi:10.1007/978-3-540-70987-9_2

Cited by 27 publications

(22 citation statements)

References 14 publications

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“…To achieve this goal, adaptive hypermedia represents a "crossroad of hypermedia and user modeling" [1]. Hence, Adaptation Model (AM) comprises many methods and approaches for adaptive systems' presentation, navigation support [2][3], collaboration [4] and assessment mode [5] [6].…”

Section: Introductionmentioning

confidence: 99%

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

Al-Azawei

Badii

2014

IJCSIT

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Section: Introductionmentioning

confidence: 99%

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

Al-Azawei

Badii

2014

IJCSIT

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“…At the core of all personalisation research is the need for a User Model (Knutov et al 2009), which uses terms from a Domain Model to indicate a user's relationship to different concepts. The Domain Model describes how concepts are connected to each other defining a semantic relationship between them (De Bra et al 1999, Conlan et al 2002, Brusilovsky 2008. The User Model contains characteristics of individual users such as goals, knowledge, background and preferences modelled in terms of the Domain Model concepts.…”

Section: What Is Personalisation and Adaptation?mentioning

confidence: 99%

Moving Towards Personalising Translation Technology

O’Brien¹,

Conlan²

2018

Moving Boundaries in Translation Studies

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Technology has had an important impact on the work of translators and represents a shift in the boundaries of translation work over time. Improvements in machine translation have brought about further boundary shifts in some translation work and are likely to continue having an impact. Yet translators sometimes feel frustrated with the tools they use. This chapter looks to the field of personalisation in information technology and proposes that personalising translation technology may be a way of improving translator-computer interaction. Personalisation of translation technology is considered from the perspectives of context, user modelling, trust, motivation and well-being.

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“…To solve this problem and to achieve a personalized distribution of prepared learning materials, different approaches have been developed. Based on intelligent and adaptive algorithms Intelligent Tutoring Systems (ITS), Adaptive Hypermedia Systems (AHS) and Recommender Systems (RS) were developed (Brusilovsky, 2008). These systems introduced interactivity and were enhanced by incorporating communication capabilities, evaluation and monitoring of learners' progress (Kamal et al, 2014).…”

Section: The Evolution Of Educational Recommender Systemsmentioning

confidence: 99%

“…S ciljem rješavanja tog problema, a kako bi se postigla individualizirana distribucija pripremljenih materijala za učenje, razvijeni su različiti pristupi. Utemeljeni na inteligentnim i prilagodljivim algoritmima razvijeni su inteligentni tutorski sustavi (ITS), prilagodljivi hipermedijski sustavi (AHS) i sustavi preporučivanja (RS) (Brusilovsky, 2008). Ti su sustavi uveli interaktivnost kao i komunikacijske mogućnosti, evaluaciju i praćenje napretka polaznika tijekom učenja (Kamal i sur., 2014).…”

Section: Evolucija Obrazovnih Sustava Preporučivanjaunclassified

Educational Recommender Systems: An Overview and Guidelines for Further Research and Development / Obrazovni sustavi preporučivanja: pregled stanja sa smjernicama za daljnja istraživanja i razvoj

Đurović

Dlab

Hoić-Božić

2018

Croatian Journal of Education

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Educational Recommender Systems (ERS) are increasingly used as tools to help students and teachers during the implementation of the learning process. The main difference between ERS and their commercial counterparts is in the pedagogical principles appropriate for the learning and teaching process. The differences in the educational methods used in a variety of educational situations, and their dependence on the field of study, set initial guidelines for ERS design. This paper reviews the evolution of ERS up to the currently achieved level of development and presents the basic techniques used in ERS design and the common problems they encounter in their work. Examples of classification of different ERS, according to their specific characteristics and basic approaches in their work, are presented. Based on this analysis, along with the training and upgrading of the existing algorithms, five specific areas in which future research and development can be expected are defined: construction of universal ERS, ERS intended primarily for teachers, ERS that links student achievements across different courses, ERS which take into account physical distance between students and use of ERS to motivate students to work continuously.

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Adaptive Navigation Support for Open Corpus Hypermedia Systems

Cited by 27 publications

References 14 publications

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss)

Moving Towards Personalising Translation Technology

Educational Recommender Systems: An Overview and Guidelines for Further Research and Development / Obrazovni sustavi preporučivanja: pregled stanja sa smjernicama za daljnja istraživanja i razvoj

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