Abstract / Description of output

Despite the proliferation of educational programmes in Health Informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data. The analysis of students' responses indicates that machine learning emerges as one of the most interesting topics, while certain topics such as data wrangling of genomics data were more challenging for students. Students expressed a preference for sequential learning. They exhibited multimodal tendencies regarding the type of learning resources, with tendency to prefer learning resources that have more visual contents. In all three courses, learners reported using deep learning strategy rather than surface learning, yet they appear to struggle with employing organisation, elaboration, and peer learning tactics. This study provides valuable insights into HI education, offering recommendations for educators, learners, and researchers to enhance HI education.

Original languageEnglish
Title of host publicationDigital Health and Informatics Innovations for Sustainable Health Care Systems
Subtitle of host publicationProceedings of MIE 2024
EditorsJohn Mantas, Arie Hasman, George Demiris, Kaija Saranto, Michael Marschollek, Theodoros N. Arvanitis, Ivana Ognjanović, Arriel Benis, Parisis Gallos, Emmanouil Zoulias, Elisavet Andrikopoulou
PublisherIOS Press
Pages1540-1544
Number of pages5
Volume316
ISBN (Electronic)9781643685335
DOIs
Publication statusPublished - 22 Aug 2024

Publication series

NameStudies in Health Technology and Informatics
PublisherIOS Press
Volume316
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Keywords / Materials (for Non-textual outputs)

  • medical informatics/education
  • humans
  • surveys and questionnaires
  • self report
  • learning
  • curriculum
  • machine Learning
  • male

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