ULearn: Personalized Medical Learning on the Web for Patient Empowerment

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationAdvances in Web-Based Learning – ICWL 2019. ICWL 2019
Number of pages12
Publication statusPublished - 2019

Publication series


All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Fingerprint Dive into the research topics of 'ULearn: Personalized Medical Learning on the Web for Patient Empowerment'. Together they form a unique fingerprint.

Cite this