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Title: Crowdsourced recommender system
Authors: Mallia Milanes, Mario
Montebello, Matthew
Keywords: Artificial intelligence
Distance education
Education -- Effect of technological innovations on
Virtual reality in education
Issue Date: 2018
Publisher: HEAd
Citation: Mallia Milanes, M., & Montebello, M. (2018). Crowdsourced recommender system. 4th International Conference on Higher Education Advances (HEAd’18). Seville, Spain.
Abstract: The use of artificially intelligent techniques to overcome specific shortcomings within e-learning systems is a well-researched area that keeps on evolving in an attempt to optimise such resourceful practices. The lack of personalization and the sentiment of isolation coupled with a feeling of being treated like all others, tends to discourage and push learners away from courses that are very well prepared academically and excellently projected intellectually. The use of recommender systems to deliver relevant information in a timely manner that is specifically differentiated to a unique learner is once more being investigated to alievate the e-learning issue of being impersonal. The application of such a technique also assists the learner by reducing information overload and providing learning material that can be shared, criticized and reviewed at one’s own pace. In this paper we propose the use of a fully automated recommender system based on recent AI developments together with Web 2.0 applications and socially networked technologies. We argue that such technologies have provided the extra capabilities that were required to deliver a realistic and practical interfacing medium to assist online learners and take recommender systems to the next level.
Appears in Collections:Scholarly Works - FacICTAI

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