开发一个个性化,基于证据和包容性学习 (PEBIL) 混合学习模型:一个跨部门调查
Athanasios Hassoulas1, Andreia de Almeida1, Hannah West1
1School of Medicine, Cardiff University, 905 Neuadd Meirionnydd, Heath Park, Cardiff, CF14 4YS UK.
概括
学生发现混合学习是有效的,大多数学生使用电子学习资源 (ELR) 并更喜欢非同步的选择以实现自主学习. 本研究提出了一个针对数字教育的个性化,基于证据和包容性学习 (PEBIL) 模型.
科学领域:
- 医学教育 医学教育
- 数字学习技术数字学习技术
- 教学教育学 在教学上
背景情况:
- 在COVID-19之前,混合学习模型已经存在,但大流行加速了数字解决方案的采用.
- 在疫情后,人们已经超越了传统的讲座,转向了面对面的互动和数字会议.
- 学术人员正在探索数字工具,以提高教学成果.
研究的目的:
- 评估学生的体验,满意度和对电子学习资源 (ELR) 的参与度.
- 评估学生对疫情期间和之后实施的各种混合学习方法的看法.
- 为了确定有效的数字工具和战略,以满足学生的各种学习需求.
主要方法:
- 卡迪夫大学医学院的一个多学科团队开发了一项调查.
- 该调查探讨了学生对ELR和混合学习体验的看法.
- 共有179名本科生和研究生完成了调查.
主要成果:
- 97%的学生报告说,电子学习资源被整合到他们的教学中.
- 77%的人将电子学习资源的质量评为好到优秀.
- 66%的人更喜欢非同步的资源以实现自主学习.
结论:
- 学生们积极地体验和感知了各种数字平台和方法.
- 建议采用个性化,基于证据和包容性学习 (PEBIL) 模式.
- PEBIL模型促进了数字技术在线和离线学习环境中的整合.
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