Integrating Social Media into Problem-Based Learning to Improve Students’ Learning Performance

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

Abstract

Research into the impact of social presence on students’ learning performance in the online-based problem-based learning (PBL) context is rarely extended in clinical training courses. The constructs of social identification and trust in community members, especially in the current intern group, cannot be overlooked because students encounter a variety of the problem-situations can change constantly. In such circumstances, a collaborative learning process may quickly help students make decisions based on their skills/abilities, experiences, and information in order to achieve their learning goals. According to in-depth interviews with the instructors, this study proposed a model to investigate students’ learning performance that has been used in medical or nursing education but is not yet generally available in the fields of aesthetic medicine or cosmetic/plastic surgery education. Therefore, we expect the results of this study to provide a valuable reference for the PBL design that will enhance students’ professional abilities and skills via their identification and trust in their community and will improve their reasoning skills during clinical training courses. Likewise, the findings may contribute to the ability of instructors to manage their courses and provide the information needed for their students promptly and effectively via virtual communities.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings
EditorsLisbet Rønningsbakk, Ting-Ting Wu, Frode Eika Sandnes, Yueh-Min Huang
PublisherSpringer
Pages729-737
Number of pages9
ISBN (Print)9783030353421
DOIs
Publication statusPublished - 2019
Event2nd International Conference on Innovative Technologies and Learning, ICITL 2019 - Tromsø, Norway
Duration: 2019 Dec 22019 Dec 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11937 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Innovative Technologies and Learning, ICITL 2019
Country/TerritoryNorway
CityTromsø
Period19-12-0219-12-05

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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