ULearn: Personalized Medical Learning on the Web for Patient Empowerment

Biagio Lenzitti, Markus Helfert, Davide Taibi, Marco Alfano, Marco Alfano

Risultato della ricerca: Conference contribution

2 Citazioni (Scopus)

Abstract

Abstract. Health literacy constitutes an important step towards patient empowerment and the Web is presently the biggest repository of medical information and, thus, the biggest medical resource to be used in the learning process. However, at present web medical information is mainly accessed through generic search engines that do not take into account the user specific needs and starting knowledge and so are not able to support learning activities tailored to the specific user requirements. This work presents “ULearn” a meta engine that supports access, understanding and learning on the Web in the medical domain based on specific user requirements and knowledge levels towards what we call “balanced learning”. Balanced learning allows users to perform learning activities based on specific user requirements (understanding, deepinning, widening and exploring) towards his/her empowerment. We have designed and developed ULearn to suggest search keywords correlated to the different user requirements and we have carried out some preliminary experiments to evaluate the effectiveness of the provided information.
Lingua originaleEnglish
Titolo della pubblicazione ospiteAdvances in Web-Based Learning – ICWL 2019. ICWL 2019
Pagine217-228
Numero di pagine12
Stato di pubblicazionePublished - 2019

Serie di pubblicazioni

NomeLECTURE NOTES IN COMPUTER SCIENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
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