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A Multifactorial Analysis of the Acceptance of Recommender System for Saudi Universities the Literature Revisited

A Multifactorial Analysis of the Acceptance of Recommender System for Saudi Universities the Literature Revisited
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Author(s): Hadeel Alharbi (University of New England, Australia)and Kamaljeet Sandhu (University of New England, Australia)
Copyright: 2017
Pages: 16
Source title: Design Solutions for User-Centric Information Systems
Source Author(s)/Editor(s): Saqib Saeed (Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia), Yasser A. Bamarouf (Imam Abdulrahman Bin Faisal University, Saudi Arabia), T. Ramayah (University Sains Malaysia, Malaysia)and Sardar Zafar Iqbal (Imam Abdulrahman Bin Faisal University, Saudi Arabia)
DOI: 10.4018/978-1-5225-1944-7.ch005

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Abstract

Technology for learning is increasingly about enhancing users' interactions with the technology to improve learning outcomes. Of particular importance however to improving educational outcomes is the need to complement the technological advancements with advances in the educational practices of teachers to broaden the uptake of new technologies for learning. Recommender Systems are personalised services that aim to predict a learner's interest in some services or items such as courses, grades, references, links, etc. available in e-learning applications and to provide appropriate recommendations. Such systems can potentially enhance student learning by providing students with a more hands on, interactive and tailored learning experience.

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