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<sec> <title>BACKGROUND</title> <p>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.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This protocol aims to describe the design of a study evaluating learners knowledge, skills and attitudes in a Massive Open Online Course (MOOC) about data science for healthcare.</p> </sec> <sec> <title>METHODS</title> <p>This study will use two evaluation models: 1) The RE-AIM 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.</p> </sec> <sec> <title>RESULTS</title> <p>A summary of the research findings will be reported through a peer-reviewed journal and will be presented at an international conference.</p> </sec> <sec> <title>CONCLUSIONS</title> <p>The proposed multi-method 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.</p> </sec> <sec> <title>CLINICALTRIAL</title> <p>Ethics approval for this study was obtained from Imperial College London through the Education Ethics Review Process (EERP) (EERP1617-030).</p> </sec>

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Journal article


JMIR Publications Inc.

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