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AwARE: a framework for adaptive recommendation of educational resources

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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Notes

  1. The basic assumption of Latent Factor models is that there exist an unknown low-dimensional representation of users and items where user-item affinity can be modeled accurately.

  2. http://www.teachwithmovies.org.

  3. When filling a Likert item, users specify their level of agreement or disagreement on a symmetric agree-disagree scale.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico-Brasil (CNPq, Edital Universal 2016, grant 400.954/2016-8) and (CNPq, Edital Universal 2018, grant 423.518/2018-6).

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Correspondence to Vinicius Maran.

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Machado, G.M., Maran, V., Lunardi, G.M. et al. AwARE: a framework for adaptive recommendation of educational resources. Computing 103, 675–705 (2021). https://doi.org/10.1007/s00607-021-00903-3

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

Keywords

  • Recommender systems
  • Adaptive systems
  • Matrix factorization
  • User profile

Mathematics Subject Classification

  • 68U35
  • 68M01