Cognitive Style and Mobile Technology in E-learning in Undergraduate Medical Education- A Randomized Controlled Trial of Otolaryngology-head and Neck Surgery

Detailed Summary

New designs of 6-year undergraduate medical education (UME) in Taiwan mainly include (1) integral curricula of body organ system, (2) multiple methods of clinical teaching and assessment, and (3) generalism in UME. Accompany with decreasing educational hours in the classrooms and hospital, essential but minor components of primary healthcare such as ophthalmology and otolaryngology-head and neck surgery (ORL-HNS) is disproportionately under-represented in UME. Novel medical education stresses on enabling self-directory learning and increasing learning hours outside the classrooms. Accordingly, we hypothesize that innovations in educational technology can enhance the learning outcomes of ORL-HNS. This study is aimed to determine whether mobile technology in e-learning (M-TEL) is an effective tool for the instruction of ORL-HNS and to compare effects of different cognitive styles on learning outcomes of M-TEL with various modules of medical education. This is a randomized controlled trial. We will recruit 60 UME students without previous training in ORL-HNS to undergo the Group Embedded Figures Test to determine their cognitive styles such as field dependence or field-independence. After blinded randomization, students are instructed on two modules of emergent ORL-HNS disorders, using either a standard e-learning of text-figure Power Point show or an interactive multimedia module. Subjects are evaluated on emergent ORL-HNS disorders using text-based assessment and multimedia assessment take place prior to and following instruction. After 7 days later, they will be assessed using global satisfaction score and AttrakDiff2 questionnaire. We anticipate that this study can confirm M-TEL can enhance the efficiency of the instruction of ORL-HNS and understand differences in learning outcomes of M-TEL with various modules of medical education between field dependence and filed independence using this platform.

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