Applying the Student Response System in the Online Dermatologic Video Curriculum on Medical Students' Interaction and Learning Outcomes during the COVID-19 Pandemic

Author:

Hung Chih-Tsung,Fang Shao-An,Liu Feng-Cheng,Hsu Chih-Hsiung,Yu Ting-Yu,Wang Wei-Ming

Abstract

Background: The coronavirus disease 2019 (COVID-19) pandemic impacted medical education worldwide. Online lecture is increasingly prevalent in higher education, but students' completion rate is quite low. Aims: This study aimed to determine the effectiveness of the student response system (SRS) in the online dermatologic video curriculum on medical students. Methods: A prospective study was conducted on 176 undergraduate fourth-year medical students. The online video lecture was integrated with SRS. Results: A total of 173 students completed the pre-test, and the attendance rate (pre-test/total) was 98.3%. A total of 142 students completed the post-test, and the completion rate (post-test/pre-test) was 82.8%. The post-test score (83.69 ± 4.34) was found to be significantly higher than that of the pre-test (62.69 ± 6.08, P =0.0002). A total of 138 students completed the questionnaire, and 92% of students opined that SRS was easy to operate. 86% of students agreed with the fact that the use of SRS could increase their learning performance by interacting with teachers. In the open-ended question, students stated that SRS offered opportunities for student–faculty interaction, allowed them to get immediate feedback, and promote active participation. Conclusions: These results highlight that the integration of SRS in the online video curriculum increases students' completion rates and learning outcomes. Moreover, the SRS is easy to operate for the students and enhances student–faculty interaction. The SRS may be adopted in online learning during this challenging time.

Publisher

Medknow

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Innovative Academic Question Analyzer for Students Using Support Vector Classifier Algorithm;2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM);2024-04-04

2. The evaluation of synchronous and asynchronous online learning: student experience, learning outcomes, and cognitive load;BMC Medical Education;2024-03-22

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