research-article

AwARE: a framework for adaptive recommendation of educational resources

Authors:

Guilherme Medeiros Machado,

Vinicius Maran,

Gabriel Machado Lunardi,

Leandro Krug Wives,

José Palazzo Moreira de OliveiraAuthors Info & Claims

Computing, Volume 103, Issue 4

Pages 675 - 705

https://doi.org/10.1007/s00607-021-00903-3

Published: 01 April 2021 Publication History

Abstract

Recommender systems appeared in the early 90s to help users deal with cognitive overload brought by the internet. From there to now, such systems have assumed many other roles like help users to explore, improve decision making, or even entertain. The system needs to look to user characteristics to accomplish such new goals. These characteristics help understand what the user task is and how to adapt the recommendation to support such task. Related research has proposed recommender systems in education. These recommender systems help learners to find the educational resources most fit for their needs. In this paper, we present an integration model between recommender and adaptive hypermedia systems. It results in a new process for educational resource recommendation, using a new algorithm of adaptive recommendation. Through a prototype and an online experiment on the educational scenario, we proved that AwARE could improve the recommendation accuracy, interaction with the system, and user satisfaction. Besides the prototype description, the paper presents a protocol to evaluate the proposed approach by both the providers’ and consumers’ point of view.

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Cited By

Yang J(2023)Evaluation method of English online course teaching effect based on ResNet algorithmJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23004845:3(4907-4916)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-230048
Xiao XJin BZhang C(2023)Personalized paper recommendation for postgraduates using multi-semantic path fusionApplied Intelligence10.1007/s10489-022-04017-x53:8(9634-9649)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s10489-022-04017-x
Ding F(2022)Supporting Adaptive English Learning With Fuzzy Logic-Based Personalized LearningInternational Journal of Gaming and Computer-Mediated Simulations10.4018/IJGCMS.31458814:2(1-19)Online publication date: 23-Nov-2022
https://dl.acm.org/doi/10.4018/IJGCMS.314588
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Published In

cover image Computing

Computing Volume 103, Issue 4

Apr 2021

212 pages

ISSN:0010-485X

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© The Author(s), under exclusive licence to Springer-Verlag GmbH, AT part of Springer Nature 2021.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 April 2021

Accepted: 05 January 2021

Received: 19 December 2019

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Cited By

Yang J(2023)Evaluation method of English online course teaching effect based on ResNet algorithmJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23004845:3(4907-4916)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-230048
Xiao XJin BZhang C(2023)Personalized paper recommendation for postgraduates using multi-semantic path fusionApplied Intelligence10.1007/s10489-022-04017-x53:8(9634-9649)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s10489-022-04017-x
Ding F(2022)Supporting Adaptive English Learning With Fuzzy Logic-Based Personalized LearningInternational Journal of Gaming and Computer-Mediated Simulations10.4018/IJGCMS.31458814:2(1-19)Online publication date: 23-Nov-2022
https://dl.acm.org/doi/10.4018/IJGCMS.314588
Wei F(2021)A Method of Recommending Physical Education Network Course Resources Based on Machine Learning AlgorithmsSecurity and Communication Networks10.1155/2021/49256052021Online publication date: 31-Oct-2021
https://dl.acm.org/doi/10.1155/2021/4925605

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