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关于大规模在线开放课程中真实世界证据的混合方法评估方案。

Protocol for a mixed-methods evaluation of a massive open online course on real world evidence.

作者信息

Meinert Edward, Alturkistani Abrar, Brindley David, Carter Alison, Wells Glenn, Car Josip

机构信息

Department of Paediatrics, University of Oxford, Oxford, UK.

Department of Public Health and Primary Care, School of Public Health, Imperial College London, London, UK.

出版信息

BMJ Open. 2018 Aug 13;8(8):e025188. doi: 10.1136/bmjopen-2018-025188.

Abstract

INTRODUCTION

Increasing number of Massive Open Online Courses (MOOCs) are being used to train learners at scale in various healthcare-related skills. However, many challenges in course delivery require further understanding, for example, factors exploring the reasons for high MOOC dropout rates, recorded low social interaction between learners and the lack of understanding of the impact of a course facilitators' presence in course engagement. There is a need to generate further evidence to explore these detriments to MOOC course delivery to enable enhanced course learning design. The proposed mixed-methods evaluation of the MOOC was determined based on the MOOC's aims and objectives and the methodological approaches used to evaluate this type of a course. The MOOC evaluation will help appraise the effectiveness of the MOOC in delivering its intended objectives. This protocol aims to describe the design of a study evaluating learners knowledge, skills and attitudes in a MOOCs about data science for healthcare.

METHODS AND ANALYSIS

Study participants will be recruited from learners who have registered for the MOOC. On registration, learners will be given an opportunity to opt into the study and complete informed consent. Following completion of the course, study participants will be contacted to complete semistructured interviews. Interviews will be transcribed and coded using thematic analysis, with data analysed using two evaluation models: (1) the reach, effectiveness, adoption, implementation, maintenance framework and the (2) Kirkpatrick model drawing data from pre and post-course surveys and post-MOOC semi-structured interviews. The primary goal of the evaluation is to appraise participants' knowledge, skills and attitude after taking the MOOC.

ETHICS AND DISSEMINATION

Ethics approval for this study was obtained from Imperial College London through the Education Ethics Review Process (EERP) (EERP1617-030). A summary of the research findings will be reported through a peer-reviewed journal and will be presented at an international conference.

摘要

引言

越来越多的大规模开放在线课程(MOOC)被用于大规模培训学习者的各种医疗相关技能。然而,课程交付中存在许多挑战需要进一步了解,例如,探究MOOC高辍学率原因的因素、学习者之间社交互动记录较低以及对课程促进者的存在对课程参与度影响的理解不足。需要产生更多证据来探索这些对MOOC课程交付的不利因素,以实现增强的课程学习设计。基于MOOC的目标以及用于评估此类课程的方法,确定了对该MOOC进行的拟议混合方法评估。MOOC评估将有助于评估MOOC在实现其预期目标方面的有效性。本方案旨在描述一项研究的设计,该研究评估学习者在关于医疗保健数据科学的MOOC中的知识、技能和态度。

方法与分析

研究参与者将从注册该MOOC的学习者中招募。注册时,学习者将有机会选择参与研究并完成知情同意。课程结束后,将联系研究参与者完成半结构化访谈。访谈将进行转录并使用主题分析进行编码,数据将使用两种评估模型进行分析:(1)覆盖范围、有效性、采用、实施、维持框架,以及(2)柯克帕特里克模型,数据来自课程前后的调查以及MOOC后的半结构化访谈。评估的主要目标是评估参与者在参加MOOC后的知识、技能和态度。

伦理与传播

本研究已通过教育伦理审查程序(EERP)(EERP1617 - 030)获得伦敦帝国理工学院的伦理批准。研究结果的摘要将通过同行评审期刊发表,并将在国际会议上展示。

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