Data collection approaches to enable evaluation of a Massive Open Online Course (MOOC) about data science for continuing education in healthcare: case study
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Published version
Author(s)
Type
Journal Article
Abstract
Background: This paper presents learner perceptions of a pilot Massive Open Online Course (MOOC).
Objective: The aim of this study was to explore data collection approaches to help inform future MOOC evaluations on the use of semi-structured interviews and the Kirkpatrick evaluation model.
Methods: 191 learners joined two course runs of a limited trial of the MOOC. Seven learners volunteered to be interviewed for the study. The study design drew on semi-structured interviews of 2 learners transcribed and analysed using Braun and Clark's method for thematic coding. This limited participant set was used to identify how the Kirkpatrick evaluation model could be used to evaluate further implementations of the course at scale.
Results: The study identified several themes that could be used for further analysis. The themes and sub-themes include: Learner background (educational, professional, topic significance), MOOC learning (learning achievement, MOOC application) and MOOC features (MOOC positives, MOOC negatives, networking). There was not sufficient data points to perform a Kirkpatrick evaluation.
Conclusions: Semi-structured interviews for MOOC evaluation can provide a valuable in-depth analysis of learners’ experience of the course. However, there must be sufficient data sources to complete a Kirkpatrick evaluation to provide for data triangulation. For example, data from pre-course and post-course surveys, quizzes and/or test results could be used to improve the evaluation methodology.
Objective: The aim of this study was to explore data collection approaches to help inform future MOOC evaluations on the use of semi-structured interviews and the Kirkpatrick evaluation model.
Methods: 191 learners joined two course runs of a limited trial of the MOOC. Seven learners volunteered to be interviewed for the study. The study design drew on semi-structured interviews of 2 learners transcribed and analysed using Braun and Clark's method for thematic coding. This limited participant set was used to identify how the Kirkpatrick evaluation model could be used to evaluate further implementations of the course at scale.
Results: The study identified several themes that could be used for further analysis. The themes and sub-themes include: Learner background (educational, professional, topic significance), MOOC learning (learning achievement, MOOC application) and MOOC features (MOOC positives, MOOC negatives, networking). There was not sufficient data points to perform a Kirkpatrick evaluation.
Conclusions: Semi-structured interviews for MOOC evaluation can provide a valuable in-depth analysis of learners’ experience of the course. However, there must be sufficient data sources to complete a Kirkpatrick evaluation to provide for data triangulation. For example, data from pre-course and post-course surveys, quizzes and/or test results could be used to improve the evaluation methodology.
Date Issued
2019-04-02
Date Acceptance
2019-01-26
Citation
JMIR Medical Education, 2019, 5 (1)
ISSN
2369-3762
Publisher
JMIR Publications
Journal / Book Title
JMIR Medical Education
Volume
5
Issue
1
Copyright Statement
©Abrar Alturkistani, Azeem Majeed, Josip Car, David Brindley, Glenn Wells, Edward Meinert. Originally published in JMIRMedical Education (http://mededu.jmir.org), 02.04.2019. This is an open-access article distributed under the terms of the CreativeCommons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. Thecomplete bibliographic information, a link to the original publication on http://mededu.jmir.org/, as well as this copyright andlicense information must be included.
Sponsor
European Institute of Innovation and Technology
Subjects
MOOC
education
education, distance
massive open online course
online education
online learning
teaching
Publication Status
Published
Article Number
ARTN e10982