医学生学习技术使用:影响因素与自我调节

B. Zheng
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引用次数: 0

摘要

背景:随着医学教育向以学生为中心的模式转变,医学生被期望成为自我调节的学习者。技术的进步被认为是支持学生的自我调节学习(SRL)。迫切需要了解哪些因素促成了学生自发的技术使用,以及学生的技术使用如何影响他们的SRL。方法:本研究在中西部一所大学医学院进行,该学校实行翻转课堂模式,要求学生在课前独立学习所有课程材料。对26名一、二年级医学生进行半结构化访谈,了解他们的自主技术使用情况、影响技术使用的因素以及他们在学习中采用的自主学习策略。使用专题分析的定性描述方法从访谈数据中确定关键主题。结果:医学生报告了四种学习技术的使用情况:视频资源(支持排练、阐述和计划)、复习资源(支持排练、努力控制和自我评估)、管理工具(支持组织、阐述、目标设定和时间管理)和社交媒体(支持求助和努力调节)。确定了学生自主技术使用的三个关键决定因素:感知有用性、主观规范和教育兼容性。结论:本研究通过对医学生自主技术使用、自主学习策略和技术使用决定因素的探讨,发现在自主学习环境中,医学生利用多种第三方资源促进学习,并制定必要的学习策略。本研究也为医学教育者和教学设计者提供了重要的启示,以更好地支持学生有效地使用技术进行学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Medical students’ technology use for learning: Contributing factors and self-regulation
Background: With medical education shifting towards student-centered models, medical students are expected to be self-regulated learners. Advancement in technologies has been suggested to be supportive of students’ self-regulated learning (SRL). There is an urgent need to understand what factors contributed to students’ self-initiated technology use, and how does students’ technology use influence their SRL. Method: This study took place in a midwestern university medical school, which implements a flipped classroom model where students are required to learn independently all the course materials before class. Twenty-six first- and second-year medical students participated in a semi-structured interview about their self-initiated technology use, contributing factors towards technology use, and SRL strategies they adopt in learning. A qualitative description methodology using thematic analysis was used to identify key themes from the interview data. Results: Medical students reported the use of four types of technologies for learning: video resources (supporting rehearsal, elaboration and planning), review resources (supporting rehearsal, effort control, and self-assessment), management tools (supporting organization, elaboration, goal-setting, and time management), and social media (supporting help-seeking and effort regulation). Three key determinants of students’ self-initiated technology use were identified, which were: perceived usefulness, subjective norms, and educational compatibility. Conclusions: By probing medical students’ self-initiated technology use, SRL strategies, and determinants of technology use, this study suggested that in self-directed learning environment, medical students used a variety of third-party resources to facilitate learning and develop necessary learning strategies. This study also provided important implications for medical educators and instructional designers to better support students’ effective use of technologies for learning.
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