Large qualitative sample and thematic analysis to redefine student dropout and retention strategy in open online education Article Swipe
Steven J. Greenland
,
Catherine Moore
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1111/bjet.13173
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1111/bjet.13173
Open online education experiences persistently high dropout rates, and the efficacy of dropout interventions has been questioned. Despite considerable research, dropout reasons are not fully understood, and further in‐depth investigation has been called for. Prior qualitative retention studies have frequently relied on smaller samples that are unable to generate deeper appreciation of dropout reasons. Over 200 in‐depth interviews were therefore conducted with students that had dropped out of open online education. The probability‐based qualitative sample facilitated capture of subthemes down to a 5% incidence level or frequency of occurrence. Thematic analysis revealed 41 subthemes within 10 broad dropout reasons. While the broad dropout themes have been identified previously, the subthemes are new and provide richer understanding. This study also captured students' suggestions for what might have prevented their dropout. Thematic analysis identified 19 subthemes within 5 broad intervention themes that respond to the root dropout causes. Many intervention subthemes address personal and learner context dropout factors that have often been considered uncontrollable and unavoidable. This paper therefore redefines dropout in open online education and offers new insights for improving retention. It also provides a strategic framework for evaluating dropouts and prioritising student‐informed interventions that respond to the main dropout causes. Practitioner notes What is already known about this topic Persistently high open online education dropout suggests existing interventions are ineffective. Prior qualitative retention studies identified the main dropout reasons, but small sample sizes failed to generate deeper insights and more in‐depth investigation has been called for. What this paper adds A probability‐based qualitative sample of 226 participants captured dropout subthemes down to a 5% level of incidence or frequency of occurrence. Thematic analysis identified 41 subthemes, within 10 broad dropout reasons. These subthemes are new and offer richer understanding. Thematic analysis also identified 19 subthemes within 5 broad dropout intervention areas that students suggested could have prevented their dropout. These include new insights for addressing dropout causes that have often previously been considered unavoidable. Implications for practice and/or policy The strategic framework provides a retention management approach that prioritises responding to the main dropout causes with student‐informed interventions. This approach and the deeper understanding afforded by robust qualitative investigation should help reduce persistently high dropout rates.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/bjet.13173
- OA Status
- hybrid
- Cited By
- 29
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3215662188
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3215662188Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/bjet.13173Digital Object Identifier
- Title
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Large qualitative sample and thematic analysis to redefine student dropout and retention strategy in open online educationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
- Publication date
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2021-11-29Full publication date if available
- Authors
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Steven J. Greenland, Catherine MooreList of authors in order
- Landing page
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https://doi.org/10.1111/bjet.13173Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1111/bjet.13173Direct OA link when available
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Dropout (neural networks), Thematic analysis, Psychological intervention, Psychology, Qualitative research, Context (archaeology), Qualitative property, Sociology, Computer science, Geography, Machine learning, Archaeology, Psychiatry, Social scienceTop concepts (fields/topics) attached by OpenAlex
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29Total citation count in OpenAlex
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2025: 4, 2024: 5, 2023: 14, 2022: 6Per-year citation counts (last 5 years)
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61Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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